CHARACTERIZATION OF WET STORAGE IMPACTS ON ...

The Pennsylvania State University

The Graduate School

Intercollege Graduate Degree Program in Agricultural and Biological Engineering

CHARACTERIZATION OF WET STORAGE IMPACTS ON

BIOPROCESSING OF CORN STOVER TO BIOFUELS

A Dissertation in

Agricultural and Biological Engineering

by

Irene Dzidzor Darku

© 2013 Irene Dzidzor Darku

Submitted in Partial Fulfillment

of the Requirements

for the Degree of

Doctor of Philosophy

December 2013

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The dissertation of Irene D. Darku was reviewed and approved* by the following:

Thomas L. Richard

Professor of Agricultural and Biological Engineering

Dissertation Advisor

Chair of Committee

Evelyn Thomchick

Associate Professor of Supply Chain Management

Jude Liu

Associate Professor of Agricultural and Biological Engineering

Paul Heinemann

Professor of Agricultural and Biological Engineering

Head of the Department of Agricultural and Biological Engineering

*Signatures are on file in the Graduate School

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Abstract

Narrow harvest windows and contamination concerns with field drying suggest that wet storage

will likely be the preferred storage method for biofuel feedstocks in humid regions of the U.S.

There are, however, at least two major setbacks to the adoption of this storage method. The first

relates to the impact of wet storage on biomass quality, biofuel yield, and biorefinery system

performance. The second relates to the impact of wet storage system on supply chain

management and logistics. These impacts have not been well addressed in prior wet storage

studies, and several logistic models have overlooked the wet storage option entirely. Feedstock

suppliers and feedstock buyers, therefore, have no prior expectation of how wet storage

outcomes compares with conventional dry storage, hence making the adoption of this storage

option a high risk venture.

One of the ways in which wet storage is anticipated to affect downstream processes, especially

biological processes like fermentation, is through the organic acids produced during storage.

These organic acids have the potential to alter the feedstock structure and provide partial

pretreatment, but can also inhibit subsequent biofuel fermentation. Pretreatment is necessary for

lignocellulosic feedstocks since it allows plant cell wall degrading enzymes to have access to

structural sugars (cellulose and hemicelluloses) and convert them to glucose and other simple

sugars that can be fermented to ethanol. Inhibition results in reduced specific biofuel productivity

(the amount of biofuel produced from the feedstock within a given time), when using biological

route of conversion. The contradictory functionality of organic acids generated during wet

storage made the impact of organic acids a major focus of this study.

To address these concerns, a series of experiments were performed to characterize the impact of

a wet storage system on biofuel production and elucidate how this storage option compares with

conversional dry storage systems. The most familiar wet storage process applicable to biofuel

production is ensilage, which has been practiced for centuries in the animal feed industry. The

feedstock is stored under anaerobic conditions, typically at moisture contents of 55 - 75% wet

basis (w.b.), and these conditions result in the production of organic acids by natural acidogenic

microorganisms. In this study, moisture levels outside these conventional ensilage levels were

investigated in order to define the full range of suitable levels for the storage of industrial

biomass destined for cellulosic biofuels. The feedstock used in this wet storage study was corn

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stover, which is an agricultural residue and includes the above ground biomass after corn grain is

harvested. Corn stover at seven different moisture contents (15-75%) was incubated under

anaerobic and aerobic conditions at two temperature levels (23oC and 37

oC) for 0, 21, 90 and

220 days.

The impact of wet storage was evaluated through its effect on dry matter loss; feedstock

composition; reactivity of corn stover fibers, after wet storage, to enzymes; and production of

ethanol, which was used as a model fuel. The impact of organic acids was examined specifically

through their effect on fiber reactivity; their interaction with subsequent liquid hot water

pretreatment process; and interference with ethanol fermentation, when Saccharomyces

cerevisiae is used as the fermenting microbe. A process cost model was developed, using results

from this study, to explore the cost implications of wet storage and effect on ethanol production.

Results from this study show that the most influential factor with respect to change in feedstock

composition during anaerobic wet storage is storage duration. Generally, there were no

significant differences in feedstock composition and in feedstock response to subsequent

downstream processes when corn stover was stored at moisture levels of 35% to 65%. Although

maximum dry matter loss observed under anaerobic storage was approximately 9%, the average

loss at 220-day storage period was less than 3%. Total organic acid content after wet storage was

up to 9.1%. Hemicellulose degradation during wet storage, which is an indication of the

pretreatment capability of organic acids, ranged from ~6% to ~30%.

Some key findings from this study are: (1) the extent of organic acid pretreatment during storage

was not adequate to serve as sole pretreatment, implying post storage pretreatment would still be

necessary; (2) the organic acid profile that develops during storage is considerably changed

during subsequent pretreatment. The amounts and types of acid after pretreatment depends on

whether feedstock was dried after storage, washed before pretreatment, or used “as is”, that is

without any processing before pretreatment; (3) acetic acid amounts greater than 6% g/g dry

basis can inhibit ethanol fermentation if butyric acid is also present. But these high

concentrations are observed only in 75% moisture feedstock; (4) if samples stored at 75% are

excluded, the organic acids produced during wet storage had no inhibitory effect on ethanol

fermentation and in fact enhanced the yield by a mean factor of ~1.11; (5) modeling output

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showed that at moisture levels ≤ 35%, the minimum delivery cost of wet storage feedstock was

lower than cost of dry storage at 25% moisture. (6) A well preserved dry biomass storage system

is likely less expensive than high moisture (≥ 45%) wet storage systems in terms of feedstock

delivery cost. Considering findings 5) and 6), the optimum wet storage system for biomass is

likely to be approximately 35 - 40% moisture, which is lower than for animal feed. This lower

optimum is primarily driven by transportation costs of the additional water at higher moisture

levels, and will vary with the distance biomass needs to move to a biorefinery.

This study is useful in providing feedstock suppliers and feedstock buyers with a good

understanding of wet storage systems and their impact on feedstock composition, feedstock

logistics and downstream processing outcomes. Several major concerns about the

appropriateness of wet storage systems for the biofuel industry have been addressed. The

information and model from this study provides a basis for comparison of wet storage and

conventional dry storage systems and will facilitate the cost effective adoption of wet storage for

biofuel production where appropriate. In addition, the results from this study can be used in

developing quality indices to facilitate fair trade between feedstock suppliers and buyers.

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Table of contents

List of figures .................................................................................................................................. x

List of tables ................................................................................................................................. xiii

Acknowledgements ...................................................................................................................... xvi

Chapter 1 ......................................................................................................................................... 1

Introduction ..................................................................................................................................... 1

1.1 Background ......................................................................................................................................... 1

1.2 Research hypotheses and goals .......................................................................................................... 4

1.3 Organization and overview of chapters .............................................................................................. 5

1.4 Significance of research ...................................................................................................................... 5

1.5 References .......................................................................................................................................... 7

Chapter 2 ......................................................................................................................................... 9

Literature review ............................................................................................................................. 9

2.1 Wet storage ......................................................................................................................................... 9

2.1.1 Ensilage: wet storage of forage crops for animal feed .............................................................. 11

2.1.2 The Ritter process: wet storage of bagasse for the pulp and paper industry ........................... 16

2.2 Adapting a wet storage system for biofuel production of agricultural residue................................ 16

2.3 Investigations into ensilage for biofuel production .......................................................................... 19

2.4 The challenge of maintaining crop value consistency across the board .......................................... 27

2.5 Defining feedstock value and quality and the need for quality index .............................................. 29

2.6 Conventional pretreatment and possible pretreatment mechanisms during ensilage ................... 31

2.6.1 Some linkages within cell wall matrix and response to various reactions................................. 33

2.6.2 Pretreatment mechanism of ensilage ........................................................................................ 37

2.7 Modeling wet storage processes and logistics ................................................................................. 39

2.8 Summary of state of the art .............................................................................................................. 40

2.9 References ........................................................................................................................................ 42

Chapter 3 ....................................................................................................................................... 54

Corn stover evaluation after wet storage: relating storage conditions to storage outcomes ......... 54

3.1 Introduction ...................................................................................................................................... 55

3.2 Methodology ..................................................................................................................................... 58

3.2.1 Stover description and storage .................................................................................................. 58

3.2.2 Dry matter loss (DML) ................................................................................................................ 59

3.2.3 Organic acid profile and pH........................................................................................................ 60

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3.2.4 Corn stover compositional analysis ........................................................................................... 61

3.2.4 Data analysis .............................................................................................................................. 62

3.3 Results and Discussion ...................................................................................................................... 62

3.3.1 Anaerobic indicator strips .......................................................................................................... 62

3.3.2 Dry matter loss ........................................................................................................................... 63

3.3.3 pH of storage and control samples ............................................................................................ 75

3.3.4 Organic acid profiles during anaerobic storage ......................................................................... 79

3.3.5 Corn stover composition before and after storage ................................................................... 84

3.4 Conclusions ....................................................................................................................................... 91

3.5 References ........................................................................................................................................ 92

Chapter 4 ....................................................................................................................................... 97

Corn stover reactivity to cellulolytic enzymes after wet storage .................................................. 97

4.1 Introduction ...................................................................................................................................... 98

4.2 Materials and Methodology ........................................................................................................... 100

4.2.1 Stover description and storage ................................................................................................ 100

4.2.2 Corn stover compositional analysis ......................................................................................... 101

4.2.3 Organic acids and fiber reactivity test ..................................................................................... 101

4.2.4 Data analysis ............................................................................................................................ 104

4.3 Results and Discussion .................................................................................................................... 105

4.3.1 Organic acid profile and cluster analysis .................................................................................. 105

4.3.2 Fiber reactivity of pretreated washed PA stover ..................................................................... 109

4.3.3 Fiber reactivity of corn stover without pretreatment ............................................................. 112

4.3.4 Fiber reactivity of dry stover without pretreatment as affected by storage duration ............ 115

4.3.5 Relating organic acid cluster with fiber reactivity cluster ........................................................ 117

4.4 Conclusions ..................................................................................................................................... 121

4.5 References ...................................................................................................................................... 122

Chapter 5 ..................................................................................................................................... 125

Impact of the organic acids produced during wet biomass storage on pretreatment and

bioconversion of corn stover to ethanol ...................................................................................... 125

5.1 Introduction .................................................................................................................................... 125

5.2 Materials and methods ................................................................................................................... 128

5.2.1 Stover description and storage ................................................................................................ 128

5.2.2 Organic acid measurements and pretreatment ...................................................................... 129

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5.2.3 Simultaneous fermentation and saccharification .................................................................... 132

5.2.4 Data analysis ............................................................................................................................ 133

5.3 Results and discussion .................................................................................................................... 133

5.3.1 Pretreatment pH ...................................................................................................................... 133

5.3.2 Glucan and xylan removal ........................................................................................................ 135

5.3.3 Organic acids and inhibitors from pretreatment ..................................................................... 137

5.3.4 Fermentation ........................................................................................................................... 145

5.4 Conclusions ..................................................................................................................................... 156

5.6 References ...................................................................................................................................... 157

Chapter 6 ..................................................................................................................................... 161

Post storage handling and processing of wet stored stover: effects of drying ............................ 161

6.1 Introduction .................................................................................................................................... 161

6.2 Materials and methodology ............................................................................................................ 164

6.2.1 Corn stover description and storage ........................................................................................ 164

6.2.2 Organic acids, pretreatment, inhibitors and sugar removal .................................................... 165

6.2.3 Fermentation ........................................................................................................................... 165

6.2.4 Data Analysis ............................................................................................................................ 166

6.3 Results and discussion .................................................................................................................... 166

6.3.1 Pretreatment pH ...................................................................................................................... 166

6.3.2 Organic acids, HMF and Furfural .............................................................................................. 169

6.3.3 Sugar removal .......................................................................................................................... 177

6.3.4 Ethanol yield ............................................................................................................................ 181

6.4 Conclusions ..................................................................................................................................... 185

6.5 References ...................................................................................................................................... 186

Chapter 7 ..................................................................................................................................... 188

Quality indices and model: predicting biofuel yield and cost based on storage conditions ....... 188

7.1 Introduction .................................................................................................................................... 188

7.2 Methodology ................................................................................................................................... 192

7.2.1 Classification for quality index or indices................................................................................. 192

7.2.2 Modeling Approach.................................................................................................................. 192

7.2.3 Notes on some model process components ............................................................................ 198

7.3 Results ............................................................................................................................................. 202

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7.3.1 Quality index ............................................................................................................................ 202

7.3.2 Process cost model – field to farm gate outputs ..................................................................... 203

7.3.3 Ethanol prediction and cost model – biorefinery outputs ....................................................... 205

7.3.4 Model validation ...................................................................................................................... 207

7.3.5 Sensitivity analysis ................................................................................................................... 213

7.3.6 Comparison with other observations and predictions from literature ................................... 214

7.4 Conclusions ..................................................................................................................................... 220

7.5 References ...................................................................................................................................... 220

Chapter 8 ..................................................................................................................................... 226

Conclusions ................................................................................................................................. 226

8.1 Overview ......................................................................................................................................... 226

8.2 Experimental approach ................................................................................................................... 227

8.3 Key findings ..................................................................................................................................... 228

8.4 Potential applications ..................................................................................................................... 230

8.5 Recommendations or direction for future research ....................................................................... 230

APPENDIX A: General overview of research ............................................................................ 232

APPENDIX B: Corn stover - relating storage conditions to outcomes ...................................... 235

APPENDIX C: Corn stover reactivity to cellulolytic enzymes .................................................. 247

APPENDIX D: Impact of wet storage organic acids on pretreatment and bioconversion of corn

stover to ethanol .......................................................................................................................... 280

APPENDIX E: Post storage handling and processing of wet stored stover ............................... 300

APPENDIX F: Quality indices and model ................................................................................. 316

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List of figures

Figure 2.1: Moisture content and recommended silo system ....................................................... 12

Figure 2.2: Some silage organic acids with corresponding chain length, pKa (numeric values in

boxes) and inhibitory capability.................................................................................................... 14

Figure 2.3: Diagrammatic representations of generalized Lignin-carbohydrate linkages and

susceptibility to oxidative, alkaline and hydrothermal treatment. ................................................ 34

Figure 2.4: Effect of pH on some structural components and linkages of plant cell wall ............ 36

Figure 2. 5: TEM (Transmission Electron Microscopy) micrographs comparing unensiled,

ensiled and dilute acid pretreated .................................................................................................. 38

Figure 2.6 Microscopic images comparing unensiled and ensiled corn ....................................... 38

Figure 3.1: Dry matter loss from aerobic wet storage. ................................................................. 64

Figure 3.3: Comparing Monod (red squares) and Droop (green triangles) models to gravimetric

dry matter loss data ....................................................................................................................... 74

Figure3.4: pH values for different aerobic storage durations and across moisture levels ............ 75

Figure 3.5: pH values for different anaerobic storage durations and across moisture levels ....... 79

Figure 4.1: Organic acid profile showing means of IA and PA stover at different moisture levels

..................................................................................................................................................... 107

Figure 4.2: Xylan and glucan removal during LHW pretreatment of ensiled (day 220) and

unensiled (day 0) PA stover ........................................................................................................ 110

Figure 4.3: Mean glucose yield ( % theoretical) of pretreated washed PA corn stover fiber at

different enzyme loadings ........................................................................................................... 111

Figure 4.5: Glucose yield of fiber reactivity test, with 15 FPU/g glucan cellulose enzyme

loading, without pretreatment. .................................................................................................... 114

Figure 5.1: Main pretreatment acids in the extracts of unwashed stover at the various

pretreatment retention times ....................................................................................................... 138

Figure 5.2: The dominant three acids in the pretreatment extract from various storage moisture

levels ........................................................................................................................................... 140

Figure 5.3: Change in amounts of organic acids from unwashed wet stored samples and total

amount of acids in pretreatment extracts .................................................................................... 141

Figure 5.5: Comparing means ethanol yields of dry ground stover ............................................ 155

Figure 6.2: Organic acid profile of “as is” (top) and dried (below) Day 0 and Day 220 samples

before pretreatment ..................................................................................................................... 170

Figure 6.3: Furfural and HMF generated during LHW pretreatment. ........................................ 176

Figure 6.4: Glucan and Xylan removal, as % of initial amount present, across moisture during

LHW pretreatment. ..................................................................................................................... 178

Figure 6.5: Lumped comparison of ethanol yield of unwashed samples and washed “as is” and

dried stover.................................................................................................................................. 182

Figure 7.3: Qualitative relationship between organic acids from wet storage of corn stover, dry

matter loss, glucose and ethanol yields ....................................................................................... 203

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Figure7.4: Sample output window of process cost model (part 1). ............................................ 204

Figure 7.5: Sample output window of ethanol prediction model (part 2). .................................. 207

Figure 7.6: Comparison of predicted and observed values of acetic acid (top) and isobutyric acid

concentration after wet storage. .................................................................................................. 209

Figure 7.7: Comparison of predicted and observed values of dry matter loss after aerobic and

wet anaerobic storage .................................................................................................................. 210

Figure 7.8: Comparison of predicted and observed values of ethanol yield. ............................. 211

Figure 7.9: Sensitivity analysis showing effect of various parameters on minimum feedstock

delivery cost of some wet and dry storage systems. ................................................................... 218

Figure 7.10: Sensitivity analysis showing effect of various storage systems on ethanol

profitability. ................................................................................................................................ 219

Figure A1: Schematic overview of research ............................................................................... 233

Figure A2: Detailed flow chart of research showing number of samples analyzed at each stage

..................................................................................................................................................... 234

Figure B1: Process Chart and experimental plan for studying effect of storage conditions on

storage outcome .......................................................................................................................... 235

Figure B2: Storage samples showing aerobic filter lids, anaerobic indicator, data logger and

molding ....................................................................................................................................... 236

Figure B3: Some physical changes in aerobic samples during storage ...................................... 236

Figure B4: Some precautions taken to minimize errors in dry matter determination ................. 237

Figure B5: Relating Lactic acid and acetic acid to moisture content ......................................... 243

Figure C1: Microscopic images comparing unensiled and ensiled corn stover .......................... 247

Figure C2: TEM (Transmission Electron Microscopy) micrographs comparing unensiled, ensiled

and dilute acid pretreated stover ................................................................................................. 248

Figure C3: Process Chart and experimental plan for fiber reactivity test and correlation with

organic acid. ................................................................................................................................ 249

Figure C5: Sample of “as is” corn stover after washing (A) for fiber reaction compared to

unwashed “as is” stover (B) ........................................................................................................ 251

Figure C6: Washed corn stover in centrifuge tubes for hydrolysis at 15% solid loading .......... 251

Figure C7: Relating hemicellulose degradation during storage and hydrolytic glucose yield ... 252

Figure C8: Ward cluster (using squared Euclidean distance) grouping of PA using mean values

of glucose yields at 2, 5, 15 FPU/g glucan ................................................................................. 263

Figure C9: Relationship between organic acids grouping and fiber reactivity grouping of PA corn

stover without pretreatment and grouped using glucose yield from four different enzyme

loadings ....................................................................................................................................... 264

Figure C10: Relating glucose from fiber reactivity grouping of PA corn stover without

pretreatment with lactic acid ....................................................................................................... 265

Figure C11: Relationship between organic acids grouping and fiber reactivity grouping of PA

and IA corn stover without pretreatment and grouped using actual values of glucose yields .... 266

Figure C12: Verifying YSI reliability of quantification ............................................................. 268

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Figure C13: Variation in total organic acids and relationship with storage moisture ................ 269

Figure D1: Flowchart of sample processing and analysis for determining effect of storage

organic acids on ethanol fermentation process ........................................................................... 280

Figure D2: Regression relationships between ethanol yield and concentration of potential

inhibitors in the fermentation broth of unensiled stover ............................................................. 298

Figure D3: Regression relationships between ethanol yield and concentration of potential

inhibitors in the fermentation broth of ensiled stover ................................................................. 299

Figure E1: Experimental design and analysis for examining impact of drying on ethanol yield 301

Figure E2: Ethanol dependency on main organic acids generated during pretreatment of

unwashed “as is” samples. .......................................................................................................... 314


unwashed dried samples ............................................................................................................. 315

Figure F1: Model input interface (Part 1. Process cost modeling ending with feedstock delivery

cost) ............................................................................................................................................. 320

Figure F2: Model input interface – truncated. (Part 2. Ethanol prediction and cost modeling

ending with ethanol production cost) .......................................................................................... 321

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List of tables

Table 2.1: Various investigations into ensilage for biofuel production: conditions and outcomes

....................................................................................................................................................... 26

Table 3.1: Regression fits for anaerobic wet storage at different temperature and durations ...... 67

Table 3.2: Estimated parameters and R-square values from fitting Monod and Droop models to

dry matter loss data ....................................................................................................................... 73

Table 3.3: Differences in organic acids in anaerobic storage across stover types, moisture levels,

durations and temperatures ........................................................................................................... 83

Table 3.4: Composition (% dry basis) of IA stover before and after anaerobic storage ............... 89

Table 3.5: Comparing average percentage change, on mass basis, in anaerobic storage samples

by groups with reference to Day 0 ................................................................................................ 90

Table 4.1: Differences in organic acids levels across moisture levels and stover type .............. 108

Table 4.2: Fiber reactivity results (glucose yield as percentage of theoretical) of dry stover for

other storage conditions .............................................................................................................. 116

Table 4.3: Differences in the three organic acid groupings and correlation of individual acids

with glucose ................................................................................................................................ 120

Table 5.1 Relating hydrogen ion concentration to acetyl hydrolysis during pretreatment ......... 135

Table 5.2: Furfural and HMF generated during liquid hot water pretreatment of unwashed corn

stover ........................................................................................................................................... 144

Table 5.3*: Fermentation yields, on percent theoretical basis, at the different pretreatment

retention times ............................................................................................................................. 150

Table 6.1: pH of “as is” and dried stover at different pretreatment retention times ................... 169

Table 6.2: Organic acid profile of Day 220 stover after pretreatment with acids listed by

decreasing inhibitory potential .................................................................................................... 173

Table 6.3: Xylan and glucan removal (% of amounts initially present) at different pretreatment

retention time .............................................................................................................................. 180

Table 6.4: Lumped summary profile of dried and “as is” samples after LHW pretreatment ..... 183

Table 7.1: Comparison of some model outputs of six storage configurations ............................ 205

Table 7.2: Comparing corn stover dry matter loss prediction of model to other studies after 7

months of storage ........................................................................................................................ 216

Table 7.3: Delivery cost comparison of model estimates and other studies ............................... 217

Table F1: Null hypotheses tested in this research ....................................................................... 232

Table B1: Moisture content and dry matter losses in Fall Harvested, IA, stover ....................... 238

Table B2: Moisture content and dry matter losses in Spring Harvested, PA, stover .................. 240

Table B3: pH values of moisture adjusted corn stover before and after storage ........................ 241

Table B4: Stoichiometry of some silage fermentation reactions ................................................ 244

Table C1: Xylan removal in pretreated PA stover assuming no xylan degradation during storage

and 5% xylan degradation during storage ................................................................................... 253

Table C2: Glucose yield of non-pretreated corn stover after fiber reactivity test ...................... 254

Table C3: Glucose and total organic acid - amount, ranking and cluster group ......................... 255

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Table C4: pH data of storage (Day 220) and control (day 0) samples ....................................... 256

Table C5: Cluster grouping based on organic acid content of individual IA and PA storage

samples ........................................................................................................................................ 257

Table C6: Cluster grouping based on glucose yield of individual IA and PA samples hydrolyzed

at cellulase enzyme loadings of 0 and 15 FPU /g glucan ........................................................... 259

Table C7: Another variant of cluster grouping based on percentage glucose yield of individual IA

and PA samples ........................................................................................................................... 261

Table C8: Cluster grouping based on mean glucose yield of PA samples hydrolyzed at cellulase

enzyme loadings of 0, 2, 5 and 15 FPU/g glucan. ...................................................................... 262

Table C9: Properties of organic acids identified in ensiled corn stover ..................................... 267

Table D1: Experimental design for examining effect of organic acids on fermentation ........... 281

Table D2: Sugar removal and inhibitors generated during pretreatment of unwashed and washed

37oC samples .............................................................................................................................. 283

Table D3: Sugar removal and inhibitors generated during pretreatment of unwashed 23oC

samples ........................................................................................................................................ 284

Table D4: Organic acids and pH after pretreatment of unwashed 23oC samples ....................... 285

Table D5: Organic acids and pH after pretreatment of unwashed and washed 37oC samples .. 287

Table D6: Concentration of potential inhibitors in pretreatment extract of unwashed samples

stored at 23oC ............................................................................................................................. 288

Table D7: Concentration of potential inhibitors in pretreatment extract of unwashed and washed

samples stored at 37oC ................................................................................................................ 289

Table D8: Summary stats on concentration of potential inhibitors in unwashed samples stored at

23oC............................................................................................................................................. 290

Table D9: Differences in pH before and after pretreatment. ...................................................... 291

Table D10: Ethanol yield and inhibitor concentrations† in pretreated unwashed stover fermented

with pretreatment extract ............................................................................................................ 292

Table D11: Ethanol yield and inhibitor concentrations† in pretreated unwashed stover stored at

37oC fermented with pretreatment extract .................................................................................. 293

Table D12: Ethanol yield from pretreated unwashed and washed stover fermented without

pretreatment extract and potential ethanol lost with extract ....................................................... 294

Table D13: Correlation between Ethanol yield (% theoretical) and potential inhibitors generated

during pretreatment of unwashed unensiled stover .................................................................... 296


during pretreatment of unwashed ensiled stover stored for 220 days ......................................... 297

Table E1: pH of corn stover before and after liquid hot water pretreatment at 190oC .............. 302

Table E2: Organic acid profile of dried and “as is” stover before pretreatment ......................... 303

Table E3: Organic acid profile of unwashed Day 220 samples after pretreatment .................... 304

Table E4: Organic acid profiled of unwashed Day 0 samples after pretreatment ...................... 305

Table E5: Organic acid profile of washed “as is” samples after pretreatment ........................... 306

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Table E6: Organic acid profile of Day 0 stover after pretreatment with acids listed by decreasing

inhibitory potential ...................................................................................................................... 307

Table E7: Amounts of Furfural and HMF generated in LHW pretreatment of dried and “as is”

stover ........................................................................................................................................... 308

Table E8: Glucan removal (as % of initial amount present) after pretreatment of dried and “as is”

stover ........................................................................................................................................... 309

Table E9: Xylan removal (as % of initial amount present) after pretreatment of dried and “as is”

stover ........................................................................................................................................... 310

Table E10: Pretreatment products of washed dried stover at 15 minutes retention time ........... 311

Table E11: Pretreatment product and fermentation yield of dried ground “as received” stover 312

Table E12: Ethanol yields (percentage of theoretical) of dried and “as is” stover ..................... 313

Table F1: Ethanol yield classification using pretreatment acids and storage acids .................... 316

Table F2: Comparing pretreatment acids of dry and “as is” sample used in ethanol yield

classification. .............................................................................................................................. 317

Table F3: Dry matter loss grouping using storage organic acids................................................ 317

Table F4: Glucose yield, grouping based on storage organic acids ............................................ 318

Table F5: General observation in amounts of organic acids after pretreatment of “As is”

samples* and corresponding storage acids ................................................................................. 319

Table F6: Dry matter loss relationship for dry storage used in the model .................................. 322

Table F7: Dry matter loss relationship for wet storage used in the model ................................. 323

Table F8: Regression equations developed from Chapter 3 and from literature used in process

cost estimation ............................................................................................................................ 324

Table F9: Regression equations developed from this study and used ethanol cost and prediction

model........................................................................................................................................... 326

Table F10: Field operation custom rates and losses used in model ............................................ 328

Table F11: Breakdown of project investment cost used in model .............................................. 329

Table F12: Breakdown of project operating cost used in model ................................................ 330

Table F13: Evaluating test error by comparing with a prediction error of zero using the t-test . 331

Table F14:Predicted and observed ethanol yield values of “as is” stover with corresponding test

errors ........................................................................................................................................... 331

Table F15: Predicted and observed ethanol yield values of dried stover with corresponding test

errors ........................................................................................................................................... 333

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Acknowledgements

Except the LORD had helped me, I would have toiled in vain – Psalm 127: 1

I committed to the LORD everything I did, and all my plans succeeded – Proverb 16:3

I am especially grateful to Dr. Tom Richard, my advisor, for his support and guidance

throughout my program and research at Penn State. I want to thank him for giving me the

opportunity to work with him. It has been a long time at Penn State but with his assistance, I had

all the resources I needed to do my research in a stress-free environment. It was refreshing to

have an advisor, whose in-depth knowledge of this field of study, informed as well as provided

valuable input, critique and feedback that shaped the onset and outcome of this research.

I also want to express my gratitude to Dr. Megan Marshall for her support and friendship; for

helping me get acquainted with most of the laboratory techniques and for the valuable feedbacks

she gave during the formulation of ideas for this research. I also want to thank her for the

sleepless night we spent together in lab setting up the storage experiments, at the beginning of

this research.

To my committee members, Dr. Evelyn Thomchick, Dr. Jude Liu and Dr. Paul Heinemann, I

also owe lots of gratitude. I want to especially thank them for their understanding, patience and

support. I want to thank them for taking time to go through my work and for their valuable

feedback.

My thanks to former and current members of the Bioenergy Research group in Richard’s lab for

their help whenever needed. Thanks to Kay Dimarco for assisting with some of the lab work; to

Senorpe Asem-Hiable and Grace Darabor, fellow graduate students who gave their time to help

at some critical times. I want to also extend my thanks to the undergraduates who have assisted

at various stages of this research: Rosa, Jimmy, Eric, Kelly, Grace and Sadie. I want to thank

Roderick Thomas for assisting with the ASE temperature test and data collection; and Randall

Bock for letting me se his mechanical press for my fiber reactivity experiments and for general

lab assistance. Thanks to Brain Macafee of the Farm Operations unit and to Travis Edwards of

the Dairy Research & Education Center for their assistance in getting the PA stover used in this

research and for the chopping service.

To my parents and siblings, I say a big “thank you” for their love and support. Another big

thanks to my husband, Kwame Essien, for his patience and support and my daughter, Esi-

Gyapeaba, for her understanding and for always praying that “God help my mama to complete

her dissertation and finish school soon.”

Finally, I want to thank the Idaho National Laboratory of the U.S. Department of Energy for

funding this research work.

xvii

But I devoted myself to knowledge and study; I was determined to find wisdom and

the answers to my questions, and to learn how wicked and foolish stupidity is.

Ecclesiastes 7:25 (GNT)

You will become wise, and your knowledge will give you pleasure. Your insight and

understanding will protect you and prevent you from doing the wrong thing.

Proverbs 2:10-22 (GNT)

1

Chapter 1

Introduction

1.1 Background

An ideal biomass storage system for biofuel production is expected to conserve both the quantity

and quality of the feedstock stored. An even better option would be an improvement in feedstock

quality. Wet storage has the potential capability to enhance feedstock quality under the right

storage conditions. Part of the purpose for the series of experimental investigations in this current

study, and documented in Chapters 3 to 7, was to determine the biomass storage conditions that

meets the expectation of maximum biofuel yield at minimum cost. Although wet storage has

gained recognition as a biofuel storage option, it is not readily embraced by feedstock buyers or

even suppliers. All currently operating cellulosic ethanol demonstration facilities depend on dry

feedstock. This is because the impacts of wet storage systems on supply chain management and

biorefinery output have not been well understood or documented. Biorefineries need to be

assured that wet storage feedstocks will be tolerated in their conversion process, and understand

how such feedstocks compare with biomass from conventional dry storage. At present this

information is missing in literature. One of the benefits of this research is to document and share

this information, helping both feedstock suppliers and buyers make informed decisions on the

use of wet storage for biofuel production.

Wet storage is the storage of materials, in this case plant biomass, at high moisture levels and

under conditions that slow down or inhibit active microbial activities or reactions. One attractive

strategy for wet storage is the ensilage process, an anaerobic process where natural acidogenic

microorganisms convert small amounts of residual sugars to organic acids to effect preservation

by lowering pH.

A compelling factor in studying the impacts of wet storage of corn stover on subsequent

bioconversion to biofuel is the conflicting potential that wet storage systems hold. On the one

hand, the organic acids produced during storage under anaerobic conditions have the potential to

improve feedstock quality by altering the plant cell wall structure so that enzymes can have

2

access to structural sugars (cellulose and hemicelluloses) and convert them to simple sugars that

can be fermented to biofuel. On the other hand, these same acids can inhibit the microbial

processes responsible for biofuel production, thereby reducing the amount of biofuel that can be

produced from the feedstock. The interest in understanding the implications and feasibility of

wet storage for biofuel production is not only of theoretical value but has practical significance,

especially with respect to the commercialization and sustainability of cellulosic biofuel

production. Cellulosic biofuel is biofuel from non-food plant resources. Cellulosic biofuel

production is expected to play a significant role in the United States’ journey to energy security.

The Energy Independence and Security Act of 2007 proposed a progressively increasing target

for cellulosic biofuel, up to 16 billion gallons by the year 2022. The Department of Energy

predicts a potential for up to 60 billion gallons by 2030 based on potentially available feedstock

from the billion ton study (Perlack et al., 2005; EERE, 2008). Accounting for an estimated

60% increase in energy consumption by 2030, this target would constitute 30% of the United

States’ transportation fuel needs.

The high rise in the cost of transportation fuels, in spring of 2008 into the summer and autumn of

the same year not only raised global concerns but reinforced the importance of the goal of energy

independence. The simultaneous global recession reinforced economic and political connections

with energy. The impression of the ‘crisis’ on the everyday life of Americans was critical

enough that for a while alternative energy pathways to U.S. energy independence became the

focus of the presidential campaigns and in fact was defined as a national priority by Obama, and

proposed as a backbone for economic stimulus and recovery.

Renewable energy accounted for about 8% of U.S total energy supply in 2009, of which biomass

energy accounted for 50% and was therefore less than 4% of the total U. S. energy supply (EIA,

2010). Current biomass energy resources consist mainly of wood and wood waste, municipal

waste, sludge waste and alcohol fuels. The main form of biomass energy utilization is for

electricity (biopower) and thermal applications (bioheat) with less than 1% from alcohol fuel and

biodiesel (EIA, 2009). Although this statistic suggests a small contribution of biofuels in meeting

U.S. energy demands, the strategic importance of biofuels springs from its ability to satisfy a

particular and challenging niche: the transportation sector. Energy consumption in the

3

transportation industry, which is ~ 97% dependent on petroleum, accounts for two-thirds of

petroleum demand and a fourth of the total energy consumption in the U.S., and has always been

the underpinning element of fuel crises experienced in the U.S (Wyman, 1999, EIA, 2007).

Biofuel production in the U.S. is currently dominated by ethanol from corn grain and has been

subjected to serious debates over issues of food security and environmental and ecological

sustainability. Cellulosic biofuels, which are the preferred alternatives, are still not operational at

full commercial scale. As noted by Hess et al. (2007), long term sustainability and economic

competitiveness of the cellulosic biofuel industry can only be possible if techno-economic issues

surrounding feedstock acquisition are addressed. These issues are categorized as supply chain

management or biomass logistics and they encompass harvesting, collection, storage, and

transportation of biomass feedstocks from the widely distributed farms and forests where they

are produced to the large and centralized biorefineries that are expected to be necessary to

achieve economies of scale.

This research addresses several aspects of biomass logistics related to storage. The series of

experiments described and discussed in Chapters 3 to 7 provide both characterization of the wet

storage process and an evaluation of the impacts of wet storage on the bioconversion of corn

stover to ethanol, which is used as a model biofuel.

Wet storage has a number of advantages over dry storage as a biofuel feedstock storage system.

It is suitable for the humid climates that are typical of the major U.S. agricultural zones. This

climatic condition often restricts the number of days harvesting is possible, can prolong field

drying, and may result in material deterioration before the biomass reaches a state that is dry

enough for storage. The Southeastern climate, which is warm in addition to being humid, can

accelerate these risks of microbial deterioration. Furthermore, field drying is associated with

extensive soil contamination (often 2% to 5%, and can exceed 10% on a dry basis), which is

undesirable and could interfere with subsequent downstream processing of feedstock by either

biological or thermochemical means, and thus increase total biofuel production cost. Biological

conversion is more compatible with wet storage than thermochemical processes, and thus will be

the focus of downstream impacts in this investigation. For biological conversion, downstream

processes typically include pretreatment, hydrolysis and fermentation. Pretreatment is the

4

alteration of the lignocellulosic structure so plant cell wall degrading enzymes can have access to

structural sugars (mainly cellulose and hemicelluloses); hydrolysis is the conversion of these

structural sugars to simple sugars, and fermentation is conversion of the simple sugars to ethanol,

other alcohols such as butanol, or hydrocarbon biofuels. An example of the effect of

contaminants on downstream process would be evident during dilute acid pretreatment of

biomass; the pretreatment would be less effective because the contaminants neutralize the acid.

The additional time and resources needed to assure a proper pretreatment outcome, together with

the larger size equipment needed to accommodate the contaminants, all translate to increases in

overall production cost. A one-pass harvesting system is expected to address the issue of

contamination, but in humid regions would not be compatible with dry storage during most of

the year. In addition, wet storage may have the potential to provide some pretreatment

functionality, which is a necessary step in lignocellulosic feedstock processing.

1.2 Research hypotheses and goals

The working hypothesis for this research was that wet storage under different conditions alters

feedstock structure and composition in different ways and to different degrees but generally has a

significant and positive net effect on downstream processes. The ways and degrees to which

feedstock composition and structure are altered will determine the quality and hence the value of

the feedstock to the biorefinery. (See Appendix A for specific null hypotheses tested.)

The main goals of this research were to (1) Investigate and characterize the impacts of wet

storage of corn stover on downstream processes like pretreatment, hydrolysis and biofuel

fermentation (2) Develop quality indices that can be used in predicting feedstock composition

and quality of ensiled corn stover under various storage conditions (3) Develop a model to

predict ethanol yield and cost. (4) Provide a basis for comparing wet and dry storage systems.

These goals were achieved for the most part, and new directions were identified for additional

research. Feedstock was characterized based on fiber reactivity, its response to hydrolytic

enzymes, and ethanol yields. This characterization was focused mainly on impacts of storage

acids. A numerical index was not developed in this research, but classifications that can inform

decisions on feedstock quality are provided. A model was developed to predict storage losses,

5

feedstock delivery cost, ethanol yield and minimum ethanol selling price (MESP). This model

also provides a platform for comparing wet and dry storage outcomes.

1.3 Organization and overview of chapters

In examining the impacts of wet storage on the bioconversion process, the research was carried

out in four phases. See Appendix A for the overall research plan and an outline of the

experimental design. The first phase, documented in Chapter 3, involved setting up the storage

units and collection/analysis of storage data, including dry matter losses, the organic acid profiles

associated with various wet storage conditions, and feedstock composition. The second phase

(Chapter 4) investigates and characterizes the effect of the silage organic acid profile on

feedstock structure through a fiber reactivity assay. Feedstock fibers after wet storage were tested

to see how reactive they were to hydrolytic enzymes with and without pretreatment. The

outcome of this test was glucose yield, and this yield was compared both among the various

treatments and to corresponding controls that were subjected to the same assay. Chapters 5 and 6

document the third phase, which involved conversion of feedstock to ethanol, used as a model

biofuel. Chapter 5 focuses on the potential of silage organic acids to enhance or inhibit

fermentation through washing of feedstock and fermentation with and without pretreatment

extracts. Chapter 6 focuses on the effect of drying silage before further downstream processing.

Regression relationships developed from the results from these three phases were used in the

development of the two part model described in Chapter 7. This modeling process constitutes the

fourth phase. The model offers several storage options and can be used in predicting feedstock

delivery cost, predict the biofuel yield and economic viability of the various storage types and

configurations analyzed. Chapter 8 summarizes the main findings from this research and

provides recommendations for future investigations.

1.4 Significance of research

From the time of harvest, fresh biomass begins to undergo undesirable changes that impact its

preservation and subsequent processing, even while transported in wagons or left wilting and

then drying in the field. Differences in exposure to sun, wind, and soil increase the heterogeneity

of the biomass and affect downstream availability. A storage system should ideally minimize

6

these undesirable changes, especially dry matter loss and increased biomass heterogeneity, while

supporting downstream processing. The compositional or chemical changes that result from wet

or dry storage methods have not been characterized, and will likely indicate important

distinctions between dry and wet storage systems. While it is expected that wet storage systems

will initially be adopted in humid regions, if wet storage is eventually also considered for drier

climates a characterization of these tradeoffs will be important there as well.

This research provides such a characterization across a gradient of moisture conditions,

specifically assessing the effect of organic acids on feedstock composition and quality as well as

sugar and biofuel yields. By addressing some of the key biomass logistic issues relating to

storage, this research also provides technical and economic data and knowledge that can help

facilitate the commercialization and sustainability of the emerging cellulosic biofuel industry. In

addition, this research provides a simple and quick predictive tool that relates storage conditions

and organic acid profiles to the dry matter loss resulting from different storage conditions and to

ethanol yield and production cost. This research also provides a system to classify storage

outcomes that could lay the foundation for the development of a feedstock quality predictive

tool. Such a tool would provide a basis for feedstock suppliers and buyers (both of whom are key

players, with critical roles in the commercial viability of lignocellulosic biofuel production) to

predict feedstock quality, and as a result, create fair and efficient markets for this new renewable

energy commodity. This concern for fairness is reflected in the Energy Policy Act of 2005,

section 942(e)(3), that states funding priority would be given to projects that fairly reward

feedstock suppliers. The sensitivity of biomass quality to feedstock and storage conditions

makes it difficult to predict the value for any given feedstock after storage, thus constituting a

common concern for both suppliers and buyers. A tool like this would bring both parties to a

shared appreciation of the value of the feedstock.

The Energy Policy Act of 2005 also stipulates, in section 942(a)(4) and (e)(2), that funding

priority would be given to projects in which the small feedstock producers are full participants or

equity partners in the development of the cellulosic biofuel industry. Another important section

of the Act is 946(a)(1) in which grants would be offered to agricultural producers who can

demonstrate cost effective cellulosic biomass innovations in the preprocessing of feedstock,

7

including chemical or biochemical treatments to add value and lower the cost of subsequent

processing at the biorefinery. Wet storage presents a perfect platform for achieving these goals

by providing opportunities for manipulation of some storage factors or the use of additives that

can facilitate pretreatment or preprocessing of biomass to enhance its value at the refinery. This

value-added processing can be performed at the farm level by feedstock producers, many of

whom may already be familiar with the basics of ensilage, providing a simple but effective

technology that can be adopted quickly at the scale needed for rapid growth of a large scale

biofuel industry.

1.5 References

EERE. 2008. Biomass Program 2007 Accomplishments Report. Available at

http://www1.eere.energy.gov/biomass/pdfs/program_accomplishments_introduction_fina

l.pdf Accessed 22 July 2009.

EIA. 2007. History of Energy in the United States: 1635-2000. Annual Energy Review (AER).

DOE Energy Information Administration. Available at

http://www.eia.doe.gov/emeu/aer/eh/frame.html Accessed 7 February 2009

EIA. 2009. Renewable Energy Consumption and Electricity Preliminary Statistics 2008.

Available at

http://www.eia.doe.gov/cneaf/alternate/page/renew_energy_consump/rea_prereport.html

Accessed 14 August 2009.

EIA. 2010. Renewable Energy Consumption and Electricity Preliminary Statistics 2009.

Available at

http://www.eia.gov/cneaf/alternate/page/renew_energy_consump/pretrends09.pdf

Accessed 14 October 2010.

Energy Policy Act of 2005. 2005. One Hundred Tenth Congress of the United States of America

Available at http://frwebgate.access.gpo.gov/cgi-

bin/getdoc.cgi?dbname=109_cong_bills&docid=f:h6enr.txt.pdf Accessed 2 February

2009.

Energy Policy Act of 2007. 2007. One Hundred Ninth Congress of the United States of America



2009.

http://www1.eere.energy.gov/biomass/pdfs/program_accomplishments_introduction_final.pdf

http://www1.eere.energy.gov/biomass/pdfs/program_accomplishments_introduction_final.pdf

http://www.eia.doe.gov/emeu/aer/eh/frame.html%20%20Accessed%207%20February%202009

http://www.eia.doe.gov/cneaf/alternate/page/renew_energy_consump/rea_prereport.html%20Accessed%2014%20August%202009

http://www.eia.doe.gov/cneaf/alternate/page/renew_energy_consump/rea_prereport.html%20Accessed%2014%20August%202009

http://frwebgate.access.gpo.gov/cgi-bin/getdoc.cgi?dbname=109_cong_bills&docid=f:h6enr.txt.pdf




8

Hess, J. R., C. T. Wright, and K. L. Kenney. 2007. Cellulosic biomass feedstocks and logistics

for ethanol production. Biofuels, Bioprod. Bioref. 1:181–190

Perlack, R.D., L.L. Wright, A.F. Turhollow, R.L. Graham, D.J. Stokes, and D. C. Erbach. 2005.

Biomass as feedstock for bioenergy and bioproducts industry: the technical feasibility of

a billion-ton annual supply. Oak Ridge National Laboratory DOE/GO-102995-2135,

ORNL/TM-2006/66. Available at

http://feedstockreview.ornl.gov/pdf/billion_ton_vision.pdf

Wyman, C. 1999. Biomass ethanol: technical progress, opportunities, and commercial

challenges. Annu. Rev. Energy Environ. 24:189–226 Available at

http://www.wilsoncenter.org/news/docs/Biomass%20ethanol.pdf Accessed 02 February

2009.

http://feedstockreview.ornl.gov/pdf/billion_ton_vision.pdf

http://www.wilsoncenter.org/news/docs/Biomass%20ethanol.pdf

9

Chapter 2

Literature review

2.1 Wet storage

For purposes of biofuel production, wet storage of cellulosic biomass would occur at moisture

levels that are normally conducive to active microbial growth. In contrast, dry storage occurs at

or below the moisture levels where the low water activity inhibits microbial activity and prevents

deterioration of the material. For some foods and feedstocks the boundary between dry

(preserved) and wet (microbially active) zones is well defined and documented, although the

gravimetric moisture content of this boundary can vary considerably between different materials.

That is because these moisture levels have a theoretical basis in water activity, which correlates

to relative humidity and is also related to matrix potential, and indicates the energy necessary to

extract water from the surrounding environment. Usually for grains, storing at moisture contents

greater than 15 - 18% would be termed wet, as opposed to 13 -15% which is typically required

for long term dry storage. For agricultural residues in bales, dry storage typically requires

moisture content less than or equal to 20 - 25% (Atchison and Hettenhaus, 2004; Shinners et al.,

2007); where possible, 15 to 16 % is recommended for large round or big square bales and 17 to

18% for small square bales (Hancock, 2009). When biomass feedstock is stored at moisture

content greater than or equal to 25%, it can generally be classified as wet storage. At these

moisture levels the actions of microbes on the biomass have great potential to affect the integrity

of the material, through decomposition or oxidation, thereby reducing the quality and shelf life.

Wet storage from this perspective would look like an undesirable system, but various

mechanisms and strategies are available to prevent deterioration and maintain biomass quality. If

well designed, biomass degradation would not only be prevented, but quality and shelf life can

be enhanced.

Wet storage for biofuel production is worth considering for several reasons. Agricultural residues

could have moisture contents as high as 70% at the time of grain harvest, depending on the

harvest date. In humid climates, a wet storage option can provide harvest scheduling flexibility

by reducing or eliminating the need for field drying. Sometimes, as in the case of winter energy

10

double crops, it may be desirable to harvest before the lignification stage; this would imply

harvesting at higher moisture. In such cases, a storage system for feedstocks received at moisture

levels greater than 25% would be desirable, and could save time, energy and other resources.

Different forms of wet storage practices have been adopted for various industries and

applications. These include but are not limited to: (1) Ensilage, (2) Sprinkling systems, (3) the

Ritter process, and (4) the FERLAB self-ventilating process. Although all these processes are

briefly discussed below, ensilage and the Ritter process will be discussed in more detail in

subsequent sections since they are the most established wet storage systems for non-woody

biomass.

Ensilage of fresh fodder is the wet storage method that is most familiar to both farmers and the

public. It has been practiced at least as far back as early 19th century in Europe and later in the

U.S. (Bailey, 1911), and is the animal feed version of acid fermenting food preservation methods

for pickles, yoghurt, and kimchi that date back thousands of years. This method is traditionally

used to preserve the freshness and improve the nutritional value of green forage crops stored

through non-growing seasons for animal feed. In the forestry industry, wet storage is also a

common practice in many European and western countries. This involves continuous sprinkling

of water with differing intensities as deemed appropriate, or climate-adapted sprinkling in which

sprinkling is controlled based on the environmental conditions and evaporation dynamics of the

forest resource. The idea is to maintain moisture contents above 50% in order to preserve the

freshness of the wood as well as protect it from microbial or biological attack; this is

accomplished through creating anaerobic or oxygen deficient conditions through the formation

of a protective water film on the wood surface (oxygen diffuses 10,000 times slower in water

than in air) and the slower reaction rate due to the latent and evaporative cooling effects

associated with sprinkling (Jonsson, 2004). In Sweden, 84% of sawmills depend on wet storage

for preservation of the original quality of pulpwood and timber, which could be stored several

years before usage (Jonsson, 2004). This form of wet storage was found most useful in the U.S.

in the aftermath of hurricane Katrina, when 19 billion board feet (~ 45 million m3) of timber

spread over five million acres in Mississippi, Louisiana and Alabama were destroyed (Alt, 2005).

This resulted in a drastic reduction in timber prices due to abundant supply on the market, as well

11

as deterioration of the forest biomass due to the logistics involved in getting the resource to

manufacturing and markets, which was not timely enough. This single storm event thus

necessitated the establishment of additional twenty-six wet storage yards to preserve the

materials for up to two years (Alt, 2005; MBJ, 2006, Cooper, 2007). Another area where wet

storage is a common and successfully applied practice is the storage of baggase for the pulping

industry. This process can sometimes be prone to rapid deterioration due to the high residual

sugar content and large exposed area of the pile. The Ritter process (Atchison and Hettenhaus,

2004) is a modified wet storage system for baggase, and is most probably an adaptation of the

wet storage of woody biomass described above to non woody biomass. The feedstock is

saturated to its water holding capacity for storage and is maintained through the storage period

by recirculation of water through the pile (Atchison and Hettenhaus, 2004). Another storage

method for bagasse is the FERLAB wet storage (Grozdits, 1997), which is a biologically treated,

self-ventilating pile of wet baled bagasse. Size reduction of wet bagasse collected after sugar

extraction is followed by treatment with a proprietary mixture of thermophilic microbes, then

baled and stacked with ventilation channels between bales. The microorganisms quickly ferment

the residual sugar, stabilizing the pile and reducing long term fermentation losses as well as

preventing deterioration while the self-ventilation process dries up the material. By the mid of

the fifth month, moisture content is reduced from 55% to 25% ensuring long time storage. This

storage system supposedly addresses the rapid deterioration or losses that routinely occur in

bagasse before field drying can accomplished, due to the high moisture and sugar content.

Although FERLAB starts out as a wet storage system in the long run it is a dry storage system.

2.1.1 Ensilage: wet storage of forage crops for animal feed

Ensilage is the most commonly practiced wet storage system and usually requires some form of

enclosure to create anaerobic conditions. The anaerobic conditions limit the types and population

of microorganisms present and the metabolic pathways expressed, hence reducing energy

extraction from the material. The success of ensilage is hinged on the production of lactic acid

from readily fermentable sugars present in the feedstock or from supplemental sugars provided

as an additive or through enzymatic activities. The lactic acid reduces the pH to levels that

eradicate most spoilage organisms. Lactic acid dominance is expected within seven days of

12

ensilage and by twenty-one days silage stability is ensured. Ensilage compares favorably to dry

storage because of lower harvest and associated field losses, lower dry matter loss during

storage, a potential increase in nutritional yield and value and the overall forage quality. These

advantages, however, depend on proper management (Jones et al., 2004; Knicky, 2005).

Recommended moisture contents for forage crops in horizontal silos (bunkers), vertical silos

(conventional tower silos), limited oxygen silos and silo bags are 65-70%, 63-68%, 55-60% and

65% respectively (Jones et al., 2004). Figure 2.1 illustrates these moisture content ranges

recommended for various silo types. Depending on the silage facility available, moisture content

may have to be controlled through delayed harvest or prewilting (Egget et al., 1993; Jones et al.,

2004).

Figure 2.1: Moisture content and recommended silo system (Schroeder, 2004)

13

Silage organic acids: biofuel fermentation stimulant or inhibitor

Organic acids from both in-situ and external fermentations have long been used in the food

industry as preservatives due to their antimicrobial or inhibitory effects (Lund and Eklund,

2000). This suggests that the acidic conditions in silages could also serve as potential

impediment to the biofuel fermentation process. The inhibitory or antimicrobial effects are

mainly consequences of changes in internal pH due to assimilation and accumulation of organic

acids in the cytoplasm. This is made possible as a result of the lipophilic nature of organic acids

and the fact that lipids constitute a major part of the cell membrane and the cell wall. The

decrease in internal pH has the potential to denature or inactivate enzymes responsible for

various metabolic functions, and can also lead to reduction in growth rate or impairment of

mechanisms required to produce various microbial products, whether undesirable or of interest to

humans. In some cases, and depending on the acid concentration, cell death is inevitable.

The modes of inhibitory actions of various organic acids are different, and their impacts on

various microorganisms are also different (Lund and Eklund, 2000). The inhibitory capacity of

organic acids primarily depends on the level of dissociation. The lipid bilayer, which envelopes

the cell, acts as a barrier and does not allow the passage of ions across the cell membrane, so the

dissociated forms of organic acid have little impact. Thus the inhibitory capacity of an organic

acid is almost solely due to the concentration of the undissociated form of the acid. The

undissociated form of organic acid increases exponentially with decrease in pH, and vice-versa,

within a fairly narrow range around the pKa of that acid (Palmqvist et al., 1996; Branen, 2000).

Although the pH of the fermentation medium is a critical factor in organic acid inhibition, it is

not the sole criterion (Branen, 2002). This is evident by the more inhibitory nature of organic

acids as compared to strong mineral acids; the later cannot be assimilated into the cell because of

their non-lipophilic nature (Lund and Eklund, 2000). Generally, bacteria are more susceptible to

effects of organic acids at low pH than yeast or mold (Lund and Eklund, 2000). In addition to the

level of dissociation, the organic acid chain length can also affect their inhibitory capacity.

Ronnau et al. (1989) observed in an experiment with guinea pigs that the absorption rate of the

undissociated form of an acid across the distal colon (part of the descending section of the large

intestine) increases with chain length of short chain fatty acids. Branen (2002) noted that acids

14

with chain lengths less than or equal to seven carbons are more effective inhibitors at lower pH,

while chain lengths from nine to twelve are more inhibitory at neutral or higher pH. This implies

the longer chain acids have higher undissociated fractions and may not need a decrease in pH to

maintain this form, consistent with Ronnau et al.’s (1989) observation. However, this conclusion

is undercut by Branen’s (2002) comparison of the levels of inhibition of carbon chains less than

seven; this comparison showed that the levels of inhibition of the organic acids in question were

more aligned with their respective pKa value than chain length (see Figure 2.2).

Figure 2.2: Some silage organic acids with corresponding chain length, pKa (numeric values in

boxes) and inhibitory capability.

As mentioned earlier, different microorganisms will respond differently to different acids at

different concentrations and different conditions. For instance, the inhibitory effect of organic

acids on some microbes may depend on the concentration of metal ions present and on factors

like concentration of sugars, the amounts and types of nutrients, inoculum size or initial

microbial population (Lund and Eklund, 2000; Palmqvist and Hahn-Hagerdal, 2000; Stenberg et

al., 2000). Palmqvist et al. (1996) observed that the inhibitory effect of acetic acid on willow

15

fermentation for ethanol production was not significant except in the presence of sugars from the

pretreatment extract. There are a number of other studies (Palmqvist et al., 1996; Taherzadeh et

al. 1997; Koegel et al., 1997; Zaldivar and Ingram, 1999; Palmqvist and Hahn-Hägerdal , 2000;

Klinke at al., 2004; Knauf and Kraus, 2006) on the effects of organic acids especially with

reference to Saccharomyces cerevisiae (yeast) – which is the most common microbe used for

ethanol fermentation. Most of these studies have focused on the organic acids produced during

pretreatment of lignocellulosic feedstocks.

Depending on the feedstock and the pretreatment method, different acids could be produced.

Some of these acids include acetic, lactic, formic, levulinic, glycolic, cinnamic, 4-

hydroxybenzoic. It is generally recognized that these acids have the potential to inhibit microbial

activity; however, at lower concentrations their effects could also be positive. Observations by

Taherzadeh et al. (1997) and Thomas et al. (2002) indicate that low concentrations of acetic acid

could act as an ethanol stimulant during fermentation, increasing yield by up to 20% if

concentrations were lower than 0.05% (w/v) at pH 4.5, while these same concentrations will

inhibit growth at pH of 2.5. At intracellular pH of 3 to 4, intracellular concentrations of acetic

acid greater than 0.05% (w/v) will inhibit ethanol production, microbial growth, or both, while

lactic acid greater than 0.8% could lead to cell death depending on type of yeast (Ingledew,

2003). Zaldivar and Ingram (1999) showed that a lower concentration of acetic acid will result in

100% inhibition of E. coli and Z. mobilis. Lactic acid, on the other hand, resulted in microbial

growth but did not improve ethanol yield (Thomas et al., 2002). A similar ethanol stimulating

effect was reported by Torija et al. (2003) who observed that organic acids commonly present in

grapes were responsible for the completion of the fermentation process as well as enhanced

ethanol yields. None of the controls (i.e. without organic acid of any sort) were able to ferment

all the sugars, and fermentation was suspended after 21 days. Among the acids tested – tartaric,

citric, malic and succinic – with a Saccharomyces cerevisiae wine strain, all but succinic acid

helped complete the fermentation (Torija et al., 2003). Zaldivar and Ingram (1999), Thomas et

al. (2002) and Torija et al. (2003) studies showed the effect of organic acid on ethanol production

is dependent on the fermentation conditions especially initial pH, extracellular-intracellular pH

gradient, temperature, other chemicals present, the type and amount of organic acid present – in

both dissociated and undissociated forms. The minimum inhibitory concentrations (MIC), which

16

is the concentration at which growth is completely inhibited, of the various organic acids are

different for different microorganisms and under different conditions.

2.1.2 The Ritter process: wet storage of bagasse for the pulp and paper industry

The Ritter method of storage is an established storage method that was first commercialized in

1950 (Atchison and Hettenhaus, 2004) and specifically designed for storage of baggase for the

pulp and paper industry. This method could have a potential application for the storage of other

types of biomass and in particular, stover for biofuel production (Atchison and Hettenhaus,

2004). The Ritter process is an open-pile with typical storage capacity of about 250,000 dry tons

per pile and densities of about 200 - 225 Kg/m3. The feedstock is washed to remove dirt and

foreign materials, shredded, slurried to about 3% solids and distributed through a pipe to the

storage pad. The material is thus saturated beyond its water holding capacity and drains to about

75 -80% moisture where it stabilizes. The water from the drain is continuously recirculated

through the pile, maintaining the high moisture content which serves to control microbiological

activities within the pile (Hunter, 2007). Pile height is usually between 20 m - 40 m. At this

height, the pile becomes very compacted by the weight of its own overburden, preventing

oxygen penetration beyond one meter from the surface. Anaerobic conditions are thus created

beneath the surface, and the pH drops to about pH 4 or lower. Dry matter loss remains a

contentious issue (Liese and Walter, 1978; Young and Akhtar, 1998) but is usually claimed to be

less than 5% (Atchison and Hettenhaus, 2004). Although Liese and Walter (1978) and Young

and Akhtar (1998) admit the core of the pile is very well preserved in conventional Ritter

process, they argue that large losses occur due to the residue sugar content, large exposed

surface, and air infiltration to a depth of up to 3 m as opposed to the 1 m suggested by Atchison

and Hettenhaus (2004). Part of this discrepancy may result from transport of dissolved oxygen

associated with the recirculating water, which may accumulate oxygen at the surface of the pile.

2.2 Adapting a wet storage system for biofuel production of agricultural residue

The encouraging results from different wet storage systems in other industries interested in the

preservation and enhancement of cellulosic end product quality make it an option to be

considered for the biofuel industry. Because of the low bulk density, the wet storage systems that

17

are most applicable to the herbaceous biofuel industry are the Ritter process and Ensilage. There

are many attractions for the adoption of wet storage as opposed to dry storage for biofuel

production. For example, wet storage might be a better option for warm, humid climates where it

is difficult to achieve drying rates that are timely enough to prevent deterioration of the feedstock

before and during storage. High dry matter loss due to inadequate drying or moisture adsorption

is one of the main disadvantages of dry storage, and is particularly problematic in humid

climates. Second, field drying, which is often required for dry storage, is associated with

appreciable soil contamination since the feedstocks are usually left on the field, turned and

mixed with surface material, and may sometimes become muddy before getting dry

(Schechinger, 2008). Soil contamination could interfere with eventual processing of the

feedstock to fuels; it can increase handling and transportation cost, increase the cost of

processing equipment, reduce process efficiency and facilitate wear of equipment as well as

create a disposal problem (Schechinger, 2008). Third, there are increased risks of fire outbreak

with dry storage either through spontaneous combustion or through accidental contact with an

igniter. And fourth, dry storage is likely to take up more space, up to ten times what is required

for wet storage (Kram, 2008) depending on the storage design and compaction density; dry

storage requires ventilation alleys and a limited stacking height to allow heat to dissipate and

prevent spontaneous combustion. Richard et al. (2001) proposed the adaptation of the ensilage as

a storage method to reduce the risk of fire outbreaks associated with dry storage of biomass,

while minimizing loss of the carbohydrate content of the biomass. In addition to preventing fire

outbreaks, ensilage may serve as an avenue for in situ pretreatment of the biomass to enhance

downstream biofuel fermentation processes (Linden et al., 1987; Richard et al., 2001). In contrast

to ensilage, which requires some form of enclosure, the Ritter method is an exposed or opened

system yet internally anaerobic with an overall dry matter loss that could be similar to ensilage.

The Ritter process might, therefore, save cost in terms of holding structures but would cost more

in terms of operational facilities.

Although the Ritter process has been a great success for the storage of baggase for the pulp and

paper industry, that success may not be transferable to the storage of corn stover for the biofuel

industry without modification of the process. One relevant concern is whether the desirable

qualities for the pulp and paper industry are similar to those of the biofuel industry. The pre-

18

storage processing requirement of the Ritter process, which includes shredding with hammer mill

and vigorous washing, could reduce the water soluble carbohydrate in corn stover to levels that

may not promote lactic acid fermentation and rapid reduction in pH for feedstock preservation.

Water soluble carbohydrate (WSC) levels in corn stover are lower than that of bagasse and are

not likely to exceed 5% if harvested after grain is matured (Ren, 2006; Pordesimo et al., 2007;

Chen, 2009); even at 3.3% it is not likely for lactic acid to dominate (Ren, 2006). However, the

presumed necessity of adequate initial water soluble carbohydrates for achieving low pH and dry

matter loss through lactic acid fermentation is brought into question by the Ritter process.

Analysis of Ritter piles showed no lactic acid producing microbes nor was there any lactic acid

detected (Atchison and Hettenhaus, 2004), yet pH values and dry matter loss were similar to and

within recommended range of well designed ensilage. The organic acid profile of the Ritter

process is dominated by acetic, propionic and butyric acids, contrasting with conventional

knowledge about the importance of lactic acid in ensilage. Richard et al. (2001) and Ren (2006)

showed that preservation of ensiled corn stover could still be possible with acetic acid dominant

silage, or even without lactic acid, consistent with the results of Atchison and Hetterhaus (2004)

for the Ritter process. Although washing in the Ritter process may not affect the preservation of

the feedstock, the ‘water solubles’, which are mainly sugars, are washed out. These water

solubles accounts for up to 10% storage loss, in addition to the up to 5% fiber loss in the Ritter

process (Atchison and Hettenhaus, 2004), and could thus constitute a significant loss in the

biofuel industry depending on the feedstock. Nadeau et al. (1996) observed that rinsing ensiled

samples could result in up to 8% loss of cellulose and up to 15% loss of hemicelluloses.

Although, the preceding discussion does not suggest strong technical advantages for a ‘biofuel

Ritter process’, the process has been demonstrated on corn stover with success for a storage

duration exceeding two years (Kram, 2008). This success is primarily with respect to feedstock

preservation, without consideration for the impact on downstream processes, and economic costs

need to be factored in as well. The conflicting standards of quality and indicators of successful

storage from the Ritter and ensilage process have a number of implications: (1) the chemistry

underlying the successes of wet storage is still not well understood; (2) the unique structural and

chemical composition of different biomass feedstocks could dictate that different microbial

populations would out compete the rest and stabilize the pile, resulting in different results even at

19

same moisture content and pH; (3) an end use goal is necessary to clearly define what conditions

constitute good practice; and (4) the conventional criteria that determine quality of feedstock for

the animal or paper & pulp industry are not necessarily appropriate to designing a wet storage

system for corn stover as a feedstock for biofuel production

2.3 Investigations into ensilage for biofuel production

Table 2.1 describes prior investigations into the potential of silage as a biofuel feedstock,

including using various feedstocks and additives. The following are relevant findings from a

number of these studies.

Hydrolytic capacity of silage and effect of endogenous sugars

Linden et al. (1987) showed that ensilage could serve as a low severity pretreatment method if

extended over a long period of time. They investigated the effect of microbially-treated ensiled

and unensiled sweet sorghum on fiber reactivity, quantified using hydrolytic yield. The sorghum

had been pressed and wilted prior to storage, which was up to 155 days. The post-storage

processes included sodium hydroxide (NaOH) extraction as pretreatment, enzymatic hydrolysis,

pasteurization and fermentation. Although endogenous sugars were higher for day zero samples

compared to ensiled samples, without pretreatment, subsequent hydrolysis of ensiled samples

resulted in up to 69% more sugars compared to day zero samples. The authors, however, noted

that hydrolytic yield decreased for silage samples that were beyond thirty-two (32) days of

storage.

A similar but contrasting observation of quality degradation with time was made by Ren (2006)

who investigated the use of ensiled material for production of particle boards. Although the

ensiled material generally improved the mechanical properties and dimensional stability of the

manufactured particleboards relative to boards made from unensiled stover, this improvement

was lower at 189 days of ensilage compared to 21 days. Specific quality indicators including

modulus of rupture (MOR), modulus of elasticity (MOE), and internal bond (IB) were lower at

189 days, while thickness swell (TS) and water adsorption measurements were higher. The

former group could imply that the fiber became softer or more degradable over time, and the

latter measurements imply that the ensiled feedstock may allow better absorption and access for

20

enzymes, and more effective interaction with other elements of biochemical reactions. These

expectations lead to the contrast between Linden et al. (1987) and Ren’s (2006)

recommendations, since it would be expected that softer, easily degradable, better absorbing

fibers should yield more hydrolytic or fermentable products, but be less useful for use in

structural composites.

The relationship between hydrolytic yield in Linden et al. (1987), which was up to 42% of

theoretical yield, and silage duration was not linearly proportional. This 42% is higher than what

is typical in non-pretreated materials, which typically yield less than 20% of the theoretical yield

(Lynd, 1996; Mosier et al., 2005a; Zheng et al., 2009). Overall sugar yield (endogenous sugar

combined with hydrolytic sugars) indicated that in some cases, day zero material contained more

sugars compared to ensiled material.

After Linden et al.’s (1987) pressed sorghum samples were pretreated using NaOH, the

unensiled samples released more sugars (P < 0.005), but wilted sorghum showed no significant

difference (P = 0.135) over the ensilage period. Although Linden et al.’s (1987) results were not

consistent over the entire ensilage duration, they resulted in four observations. The first supports

the inhibitory nature of endogenous sugar. Second, the contribution of soluble carbohydrates to

the overall sugar yield could be significant, resulting in up to 17% more sugars depending on

ensilage duration, and the relative contribution of endogenous sugar is substrate dependent. For

instance, sweet sorghum has readily fermentable (endogenous) sugars comprising 40% of the dry

weight (Henk and Linden, 1994), while most lignocellulose biomass contains less than 5%

fermentable sugar. Third, without conventional pretreatment, hydrolysis of ensilage yielded less

than 45% of the sugar present in the feedstock. Finally, the results support the notion that the

acidic condition in silage could serve as pretreatment. This was evident from the percentage of

holocellulose (hemicelluloses and cellulose content) hydrolyzed without pretreatment compared

to unensiled samples. The difficulties in drawing inference from Linden et al.’s (1987) results

include (1) silage samples for each period had no replicates, hence it is not certain whether the

inconsistency in sugar yield with time is due to errors or is an actual reflection of the silage

process, and (2) the fermentation results, 0.37g ethanol/g available carbohydrate, disregarded

21

sorghum treatment (pressed or wilted) as well as ensilage period, which might suggest ethanol

yield is independent of these factors.

Enzyme addition, pretreatment and fiber digestibility

In another investigation using sweet sorghum, Henk and Linden (1994) showed that ensilage

could increase the reactivity of the feedstock to hydrolysis. Samples were inoculated with

Lactobacillus plantarum and Streptococcus faecium and then treated with enzymes (5IU/g DM)

before ensiling at 20oC for 60 days. Henk and Linden (1994) observed that although more

glucose was produced during ensilage in enzyme-treated samples compared to the controls

(silage without enzyme addition), the lignocellulosic fibers of control silage were more

responsive to post-silage or conventional hydrolytic reactions compared to enzyme-treated silage

and unensiled samples. Enzyme-treated samples had about 65% more glucose from in-situ silage

reaction than control samples but had about 41% less glucose during subsequent convention

hydrolysis. The post-storage response of enzyme treated samples to further degradation is

consistent with findings of Sheperd et al. (1995) and Nadeau et al. (1996) in which biomass

showed increased fiber degradation during ensilage, but experienced decreased in vitro

digestibility compared to control silage; enzyme treated samples still maintained an overall

higher degradation level as in the Linden et al. (1994) studies. The reduction in subsequent

degradation or hydrolysis is supported by a model developed by Chesson (1993), which showed

preferential hydrolysis of exposed polysaccharide and eventual build-up of lignin-carbohydrate

complexes left from the secondary cell wall, together with those of the primary cell wall. This

build-up occurred at the surface and is extremely resistant to biological attacks, thus altering the

chemical nature of the cell wall surface and reducing the hydrolytic effect of enzymes (Chesson,

1993). Conventional hydrolytic reactions show similar reductions in reactivity, but this is

normally attributed to reduced enzyme activity. Decreased enzyme activity, however, is not a

suitable explanation of reduced reactivity in subsequent hydrolysis after ensilage, since the

feedstock is being exposed to ‘new’ enzymes.

In another study, Ren (2006) showed enzyme treatment of corn stover silage resulted in more

cellulose degradation, evening out the degradation of the two structural carbohydrates (cellulose

and hemicelluloses) of the silage and increasing the WSC. In typical silage, there is preferential

22

degradation of hemicelluloses over cellulose. Hemicellulose degradation from enzyme-treated

samples were not that different from samples with no enzyme addition. The removal of

hemicelluloses facilitated hydrolysis and increase in hydrolytic yields in a similar manner for

both control and enzyme-treated samples, suggesting the positive results were not necessarily

influenced by enzyme treatment but rather the outcome of the naturally occurring

saccharification process during ensilage (Ren, 2006).

Chemically aided silage for ethanol production

Digman et al. (2007) studied ensiled switchgrass and reed canary grass by exploring some

chemically manipulated pretreatment opportunities the ensilage process could potentially

support. Their hypothesis was that the comparatively high severity of conventional pretreatment

methods, requiring very short reaction times, could be replaced by very low severity

pretreatment over a longer storage period. This is the same assumption that prompted the

investigations and suggestions by Linden et al. (1987) and Richard et al. (2001) that ensilage

could serve the dual purpose of storage and pretreatment. Digman et al.’s (2007) investigation

did not rely on the potential pretreatment capability of natural ensilage but instead applied, as

additive before ensilage, chemicals like sulfuric acid (H2SO4) and calcium hydroxide (Ca(OH)2)

that are known to improve forage digestibility. Digman et al. (2007) also employed conventional

pretreatment methods after ensiled storage. Their results showed that the controls, which had no

chemical additives, had cellulose yields of 33% and 15% for reed canary grass and switchgrass

respectively, while the acid and alkaline pretreated samples had cellulose yields of 54-79% for

reed canary grass and 24-42% for switchgrass. However, to get yields above 70% requires high

chemical loadings. For example, reed canary grass would require greater than 9% H2SO4 wt/wt

dry basis or about 15% Ca(OH)2 to achieve high yields. These loading rates are extremely high

compared to conventional pretreatment that occurs at elevated temperatures. For example,

typical conditions for dilute acid pretreatment are H2SO4 of ≤ 1% at 120 - 200oC for a few

minutes to up to 1 hour. The 33% recovery of cellulose from reed canary grass without chemical

addition or subsequent hydrolysis shows ensilage does have an impact on the feedstock structure.

The results also serve to buttress evidence of the success of pretreatment as applied to different

feedstocks. This success has been observed in spite of the challenge of feedstock variability, a

23

challenge that would no doubt impact on the applicability of wet storage as a natural alternative

pretreatment medium for biofuel applications.

Variations in using different feedstocks

An investigation into the use of five different residual feedstocks (Triticale hay, triticale straw,

cotton stalks, wheat straw and barley) for ethanol production (Chen at al. 2007) provides

additional evidence that silage effects and the impact on biofuel yield is dependent on the type

and composition of feedstock. The feedstocks were ensiled for 96 days with and without enzyme

additive. The enzyme additive was composed of fungal and bacterial alpha-amylase together

with hemicellulases and cellulases. Results indicated that four of the feedstocks (excluding

triticale hay) showed no significant differences in hydrolytic responses among samples.

However, these feedstocks all showed significant differences in hydrolytic yields of ensiled and

unensiled samples, with the former having about 6% more yield. On the other hand, hydrolytic

sugar yields of enzyme-treated ensiled triticale hay were not significantly different from those of

unensiled samples, indicating no impact of ensilage with this feedstock. A possible explanation

for the unique behavior of triticale hay could be due to its composition. Hay is less lignified due

to the comparatively early harvest date, and hence contains more non-structural carbohydrates.

The other feedstocks (straws) that are harvested after seed maturity are more lignified and

fibrous, and hence more recalcitrant (Chen et al., 2007).

Comparing chemical pretreatment and silage pretreatment effects

In a similar investigation conducted by Chen et al. (2007a), hydrolysis and ethanol yields from

chemical pretreatment of various feedstocks were compared to those obtained from enzyme-

treated ensiled feedstocks. Although subsequent chemical pretreatment gave hydrolytic yields of

~25 -37%, which seems higher than results from ensiled samples (21 -30%), statistical tests

indicate these values are not significantly different (P = 0.291). The feedstocks used in this study

included pearl millet hay, sweet sorghum hay, triticale/wheat hay and triticale/wheat straw.

Ethanol yields were 41% - 56% of theoretical yield based on hydrolyzed sugar content and also

showed no significant difference between chemically pretreated samples and enzyme-treated

silages. These parallel results of ensilage and chemically treated feedstock, although low in

24

absolute yield, is impressive as it suggests that ensilage had similar impact on the feedstock as

did the chemical treatment, hence supporting the concept that ensilage can serve as pretreatment.

It could also suggest there is an opportunity for ensilage to be competitive with conventional

pretreatment methods for some feedstocks and conditions. The question, therefore, of how silage

can be modified or controlled to achieve maximum efficiency becomes relevant.

Ensilage makes pretreatment more effective

Thomsen et al. (2008) investigated whole-crop maize silage without additives for ethanol

production. Although these results failed to support the efficacy of ensilage as a stand-alone

pretreatment, they showed that pretreatment yields as well as ethanol yield were remarkably

improved as a result of ensilage. Their results showed that hydrolyzing ensiled samples without

pretreatment resulted in very low glucose and hemicelluloses yields, each being recovered at less

than 20% of the total sugar in the silage. This result contrasts with that of Linden et al. (1987)

and may be the result of the different feedstocks used, which could affect the ensilability and the

organic acid profile. Another factor that could have influenced the sugar yield is the post silage

processing; in Thomsen et al.’s (2008) studies, the silage was dried and ground before further

processing; this was not the case with Linden et al. (1987). Thomsen et al.’s (2008) results from

pretreated ensiled samples using wet oxidation and hydrothermal treatments were, however, very

positive. The results indicated that hydrothermal pretreatment at 185oC for 15 minutes yielded

the best results, with close to 100% glucan yield and up to 89% hemicelluloses. The

hydrothermal pretreatment condition in essence is liquid hot water pretreatment. No significant

levels of inhibitors were present. The results suggest that ensilage could provide opportunities to

eliminate conventional hydrolysis before fermentation, with most of the sugars still being

released. Ethanol yield was reported to be 98% of theoretical yield after pretreatment. It is not

certain whether the very high yields obtained were unique to ensiled material, since Thomsen et

al. (2008) did not provide any data on unensiled whole corn as a control under the same

pretreatment conditions. One of the drawbacks to this experiment is that the use of whole-crop

silage is not very representative of pure lignocellulosic material since the weight of the grains,

mainly starch, constituent up to 50% of the whole plant on dry basis (Shinners et al., 2003). It is

also not likely that whole-crop corn would be used as feedstock for lignocellulosic ethanol since

25

this would ignite the food-fuel debates. Considering the high composition of starch in whole-

crop maize silage (~16%) and the relatively low lignin content due to early harvest, Thomsen et

al.’s (2008) low sugar yield from non-pretreated silage does not present an optimistic view of the

potential of ensilage to act as a pretreatment tool. However, their assertion that ensilage

significantly enhances pretreatment is shared by Chen (2009), who observed xylose removal was

55-65% for unensiled stover and 65-80% for ensiled stover after a reduced severity dilute acid

pretreatment (0.8%w/w H2SO4 at 121oC for 30 minutes).

26

Table 2.1: Silage feedstock, conditions and outcomes

Table 2.1: Various investigations into ensilage for biofuel production: conditions and outcomes

27

2.4 The challenge of maintaining crop value consistency across the board

One of the challenges of using cellulosic feedstock for biofuel production is the varying response

of different feedstocks to the processes involved, from storage to final product. This could be

partly attributed to the heterogeneity of the initial feedstocks, which even for a single crop

consist of different plant parts varying in relative proportion as well as structural and chemical

composition, and changing dynamically as the plant grows and senesces. Many different species

and varieties of feedstocks exist, which also result in varying structural and chemical

composition. Varieties may differ in growth conditions, time of maturity, dry matter content, and

nutrient and dry matter distribution. Pordesimo et al. (2005) argue that the main cause of

heterogeneity and variation in feedstock quality and energy content is not so much affected by

the variety as it is by the proportion of anatomical fractions collected, which could have

distinctly different chemical composition (Sheehan et al., 2001), and the time of harvest. This

notion was supported by a study by Muck et al. (2008) on ensilage of different corn stover

cultivars, in which the differences in ethanol potential were not of practical significance. Sluiter

et al. (2000), however, showed stover varieties differing in structural carbohydrate composition

over a very wide range of ~ 45 – 69%, which in turn is expected to affect the feedstock quality

and process economics, changing the minimum ethanol selling price (MESP) by $0.018/gal for

every 1% difference in carbohydrate content (Mark et al., 2003).

There is a general agreement that the time and method of harvest, collection and storage affects

the chemical and structural composition (Sheehan et al. 2001; DoKyoung et al., 2007). The

buffering capacity (ability to resist change in pH or acidobasic reactions), of plants is affected by

the time of harvest and increases with maturity (Knicky, 2005; Raclavská et al., 2007). This is

because as the plant advances in age, components that influence the buffering capacity like water

and WSC decrease and buffering compounds like lignin, ash, organic acids increase. Chen

(2009) found great disparity between corn stover of the same variety harvested in two successive

years harvested around the same time period. In the first year, moisture content was between 18-

46%, WSC was 3-5% and dry matter loss was 3-12% whereas in the second year moisture

content was between 22 -58%, WSC was 1.6 -2.5% and dry matter loss was less than 4%. The

high dry matter loss in the first year silage was attributed to the absence of cobs, hence

28

supporting Pordesimo et al.’s (2005) claim that anatomical fractions account for most feedstock

quality variability.

Plants are also sensitive to variation in environmental and weather conditions during growing

and harvest seasons, as well as the chemical or nutritional composition of the soil on which they

are grown; these factors can affect the plant composition or the relative proportions of the

various parts of the plant. In some cases and depending on the plant, these factors could result in

accumulation of chemical (or toxins) in the plants or certain parts of the plant (Kallenbach et al.

2001; Proven and Pitt, 2003; Hartwig, 2005). Nitrates are converted to nitrites and then ammonia

during anaerobic digestion or fermentation. Nitrate does not affect the degree of digestion but

affects the rate negatively. Nitrite inhibits microbial growth, with particularly severe effects on

cellulolytic and xylanolytic microbes (Marias et al., 1988) and similar impacst on cellulase and

xylanase activities. From an animal feed perspective, when nitrate accumulation occurs, and for

that matter other chemical accumulations, ensilage is recommended since it can break down the

chemicals and reduce their toxic effect by 30 -90% depending on the silage moisture content

(Hartwig, 2005; Min and Leep, 2007; Mickan, 2008). The advantage that ensilage presents then

is that the inhibitory effect of these contaminants during ethanol fermentation is minimized or

altogether eliminated, although other inhibitors like acetic acid accumulate during ensilage.

Another pre-storage process that might account for variation in storage outcomes for similar or

different feedstock is the cutting height and the length of cut. In cropping practices for livestock

feed, different cutting heights may be used with different forage crop, and are an important in

determining the possibility or ease of regrowth. In addition, depending on the harvest date, the

height of cut may affect the quality or composition (MDC, 2009; Kimbrough, 2009) and the

relative proportion of plant components present in the feedstock. For instance, the normal cutting

height for corn silage is 101-152 mm. But when increased to 254 – 505 mm the composition is

substantially different, with the latter having lower lignin and higher starch and energy content

(Jones et al., 2004). Since the moisture gradient and any accumulated chemical gradient is

usually formed axially along the stem, it becomes apparent that the height of cut might affect

post harvest processes. There is also the possibility of height of cut affecting microbial

population. When the crop is harvested from the ground or cut at heights close to the ground,

29

there is higher potential of increased microbial contamination through soil inoculation (Norell et

al., 2007).

In general, there are lots of factors that can cause plants of the same variety to differ in quality.

Some of these factors can be controlled while others cannot. For agricultural residues the stage at

which the plant is harvested cannot be fully controlled, since it would depend on the maturity

and harvest of the desirable food part. The time of harvest of residue may also be beyond the

farmer’s or collector’s control since this may depend on the weather and on the availability of

harvesting equipment or labor, and sometimes on other farm operations of higher priority. Also

associated with time of harvest is weather variation, which, cannot be controlled either.

Flooding could increase spoilage microbial population in the crop, a killing frost could affect the

drying mode, while rain and snow could alter the proportion of bound and free water. Given all

the other sources of variability, restriction of the crop varieties that can be grown may not be a

practical idea, and uniformity of soil fertility across farms is not likely to be achievable. This

implies there will always be variation in crop composition, even within the same variety, that

will affect feedstock composition. However, practices that are within human control (e.g. crop

species, harvest and storage method) could be managed so that although the feedstock undergoes

changes, the desirable components or compositions are maintained or consistent.

2.5 Defining feedstock value and quality and the need for quality index

Evaluation by industrial requirements

In the preceding section, crop related factors that could lead to variations in process outcome

were discussed. However, when considering wet storage in particular, additional sources of

variation can be expected. The nature of the raw material, storage conditions, as well as the

silage treatment could determine the ability of feedstocks to successfully complete the silage

process (Knicky, 2005). In addition, during ensilage, the feedstock might be constantly

undergoing changes through microbial or chemical reactions. Although microbial stability and

hence silage stability is expected at some point during silage (normally within 21 days), chemical

reactions are expected throughout the storage period, however mild or slow. Based on reasons

given in an earlier section, outcomes can be very different as a result.

30

Although ensiled feedstocks could have similar total digestible fiber, in actuality, the silage

fermentation characteristics and end product may have a modifying effect on the degree of

digestibility (Rook and Gill, 1990; Steen et al. 1998). For example, Muck et al. (2008) observed

a negative correlation between structural carbohydrates and simultaneous saccharification and

catabolism (SSC) values of some ensiled stover, indicating the presence or conservation of

sugars does not necessary result in accessibility or fermentability. There is also the possibility of

the silo losing its integrity hence, allowing some degree of air infiltration that might lead to

unexpected outcomes if not noticed or corrected on time. Another important reason why silage

presents more challenges than dry storage in terms of value or quality expectation is that there

are so many different types of additives that could be used during the process, and they could

result in different outcomes. In the forage industry, silage process quality indicators include pH,

dry matter loss and fermentation acid profile (Jones et al., 2004). Silage product or outcome

quality indicators include aerobic stability, crude protein content, Acid detergent Fiber (ADF)

and Neutral Detergent Fiber (NDF), fiber digestibility and intake potential. All of these

parameters are relevant to the biofuel industry except intake potential; however the acceptable

silage acid profile and level of dry matter loss tolerance might differ for both industries. Some of

the parameters that define feedstock quality in the forage industry can be combined into a single

index. Two of these indices are Relative Feed Value (RFV) and Relative Forage Quality (RFQ),

both of which compare forage qualities and can be used in ranking forages based on digestibility

after silage (Jeranyama and Garcia, 2004). The former evaluates feedstock quality based on the

overall amount of digestible fibers present, while the later evaluates quality based on the

digestibility of the digestible fibers present. A similar index, using appropriate criteria, would be

necessary for the biofuel industry.

The importance of a quality index is seen at two fronts. First, section 942(e)(2) &(3) of the

Energy Policy Act (2005) requires a strategic agreement be in place to fairly reward feedstock

suppliers. Second, feedstock sellers and their biorefinery customers must get their money’s worth

for feedstock marketed, so quality is a mutual concern. Strategic agreements and purchase

contracts between the two parties would be facilitated by a common understanding of what

constitutes the ‘merchandize’; is it the dry weight of whatever feedstock in question, digestible

fibers, any kind of fiber, potential sugars, fermentable sugars, etc. Since it is possible that only

31

certain components of the feedstock would be desirable, it would be necessary to monitor

process outcomes and develop a quantitative and qualitative index that can be appreciated by

both parties. For instance, enzyme additives could reduce fiber response to subsequent

hydrolysis, so if feedstock preservation during ensilage or aerobic stability after storage is not

assured, then the value of such feedstock might be lower. This is because the readily available

sugars derived from in-situ silage hydrolysis may be degraded, and so the overall sugar yield

from enzyme-treated sample may be considerably less, annulling the benefits of the treatment

and affecting the profitability of downstream ethanol production. Another example could be the

acid profile of the silage and its effect on downstream processes. As discussed earlier, there are

advantages to ensilage that could reduce some downstream (pretreatment) cost which would be

beneficial to the purchaser. On the other hand if neutralization of organic acids and/or inhibitors

present in silage is required before fermentation, this will add to process cost. Determination of

silage quality using tradition methods could be both time and money consuming. A predictive

system or model that takes into account the factors affecting quality variability would facilitate

fair transactions between feedstock suppliers and buyers. Developing a feedstock quality index is

almost a necessity for wet storage systems, which are more sensitive to storage conditions, than

it is for conventional under-the-shed dry storage.

2.6 Conventional pretreatment and possible pretreatment mechanisms during ensilage

Mechanisms of conventional pretreatment methods are still not well understood, and neither are

the pretreatment mechanisms of ensilage, which is still at an exploratory stage. The purpose of

pretreatment is to remove barriers to effective hydrolysis (chemical or enzymatic) of cellulose or

enable easy access to structural sugars in the feedstock by enzymes. This can be achieved by

altering the structure of the material or by removing some or all of the structural components that

may obstruct the process. Conventional pretreatment methods account for about a third of

processing cost (Wyman, 1999). There are a number of pretreatment technologies in existence,

broadly classified into physical or mechanical, biological, chemical and thermo- or physio-

chemical methods. Some of these have tens of variants depending on the reagents used or the

level and conditions at which they are applied. Leading pretreatment technologies include (1)

dilute acid (2) flowthrough (3) controlled pH hot water (4) Ammonia Fiber Explosion, AFEX (5)

32

Ammonia Recycle Pretreatment, ARP and (6) Lime (Wyman et al., 2003). In the past, dilute

acid pretreatment was the most studied and the most favored, however, the AFEX process is

currently gaining considerable attention. This is mainly due to the lower temperature

requirement, comparatively high yield, the potential for high solid loading, the absence of

inhibitors, the ease of recovery of reagent, and the elimination of a detoxification step after

pretreatment (Wyman et al., 2003). The controlled pH hot water method is also very appealing as

it does not require addition of any reagent and does not produce significant fermentation

inhibitors. However, controlled pH hot water has the lowest hydrolytic sugar yield of all the six

leading pretreatment technologies. While reduced sugar yield is a disadvantage, the ethanol

yields for all six leading technologies are high, ranging from 73 -98% (Wyman et al., 2003). The

absence of inhibitors is very important since the purpose of creating access to cellulose would be

defeated if the microorganisms required to aid subsequent processes cannot survive the

conditions created through pretreatment.

One of the challenges in the pretreatment of lignocellulosic feedstock is that there is not one

single pretreatment method that is robust enough to be consistently effective for all feedstock.

Optimum pretreatment conditions for any specific technology as well as outcomes may vary with

feedstock. For instance AFEX pretreatment resulted in ~98% hydrolytic sugars when used on

corn stover but in just over 50% when used on poplar at the same enzyme loading and a

residence time of three days (Wyman, 2008). Another example is dilute acid pretreatment:

conditions that are suitable for corn stover are usually ineffective for alfalfa pretreatment because

of its higher buffering capacity to acid; alfalfa therefore would require more acid, while the

comparatively high sugar content of alfalfa leads to production of furans that inhibit subsequent

ethanol fermentation (NAFA, 2007). The difficulty in achieving a robust pretreatment could be

partly attributed to the lack of fundamental understanding of the chemistry at work in

pretreatment and the impacts of reactor design and material on process outcome (NREL, 2008).

More recently, there have been claims that ionic liquid pretreatment and SPROL (Sulfite

Pretreatment to overcome Recalcitrance Of Lignocelluloses) technologies are just as effective

and more robust than leading conventional pretreatment methods, and are applicable to hard

woods, soft woods and agricultural residues alike (Pan et al., 2009). These processes may need to

be evaluated on techno-economic level to determine their industrial feasibility.

33

2.6.1 Some linkages within cell wall matrix and response to various reactions

Although the mechanisms of lignocellulosic bonds and of pretreatments are not well understood,

there are some ideas of structural formulations that suggest potential reactions and inform

expectations from various chemical or biological processes. For example, it is supposed that

lignin is covalently linked to cell wall matrix polysaccharides through ether bonds and ester

bonds, which are the most evident (Chesson, 1993; White et al., 1993) and through glycosidic

bonds. They are also linked through hydroxycinnamic or cinnamic acid bridges, methyl, acetyl

and phenolic acids and substances, or with uronic acid residues (Nevins, 1993; Theander and

Westerlund, 1993; White et al., 1993). Ester linkages are alkali-cleavable and alkali-labile; they

connect hydroxycinnamic acids (ferulyl or p-coumaryl residues) to polysaccharides (usually

pentoses, galacturonic acid residues, glucoronoarabinoxylan), while phenyl glycosidic linkages

associated with pentoses, glucose, and benzyl ether bonds are alkali-stable (Chesson, 1993;

Theander and Westerlund, 1993). Figure 2.3 shows a diagrammatic representation of lignin-

carbohydrate linkages. In herbaceous plants, Lignin-Carbohydrate Complexes (LCC) in primary

cell walls are almost absent and are alkali-resistant, while LCC in secondary cell walls can be

completely disrupted by NaOH. The alkali-stable linkages in herbaceous crops are in higher

proportion than the alkali-labile linkages (Chesson, 1993). Alkali can also influence uronosyl

constituents of hemicelluloses and β-elimination of acidic sugars (Nevins, 1993). However,

gycosidic bonds, including those in LCC, though not cleavable by alkali are acid-labile and can

also be affected by hydrothermal treatments (Chesson, 1993; Theander and Westerlund, 1993).

Acid hydrolysis results in partial hydrolysis of glycosidic bonds, and can result in degradation of

lignin at temperatures above 180oC (Chesson, 1993). To achieve a greater degree of hydrolysis,

strong concentrated acid is required. However, the disadvantage of concentrated acid is that it

degrades all sugars (Lowe, 1937) so there is a tendency to lose already present monosaccharides.

The reactivity of aqueous acid with cellulose (hydrolysis) is random, unlike alkali which is

systematic. Alkali attacks glycosidic ether bonds between anhydroglucose units of cellulose,

although this is often limited to the last unit of the chain due to resistivity of cellulose to alkali

treatments (Tímár-Balázsy and Eastop, 1998). Acids permeate the amorphous regions of

cellulose where hydrolysis begins. Depending on the temperature, duration, type and

concentration of acid, the reaction may proceed to crystalline region. At ambient temperatures,

34

acid hydrolysis is limited to the amorphous region (Tímár-Balázsy and Eastop, 1998). The

mechanism of acid hydrolysis is dependent on release and concentration of hydrogen ions (H+)

from the acid, which combines with water to break the carbon-1-oxygen glycosidic ether bond,

the rate depending on the H+ concentration, moisture content, temperature and accessibility of

cleaving sites (Tímár-Balázsy and Eastop, 1998). Hemicellulose hydrolysis is also facilitated by

lower pH or higher concentration of H+, although a rapid drop in pH could reduce enzymatic

hydrolysis of hemicelluloses (Muck, 1996). Figure 2.4 show some effects of pH on some cell

structures and linkages. It is noteworthy that pH is not the only factor that influences the rate or

extent of degradation of cell wall matrices.

Figure 2.3: Diagrammatic representations of generalized Lignin-carbohydrate linkages and

susceptibility to oxidative, alkaline and hydrothermal treatment. Solid lines represent glycosidic

linkages, dash lines represent alkali-labile (ester) linkages and dotted lines indicate ether bonding

(Chesson, 1993)

Another bond of interest in cell wall analysis is the hydrogen bond (H-bond). Hydrogen bonds

are pervasive in cell walls and play a critical role through water in keeping structures together in

complexes (Stoddart, 1983; Ayers, 2009). They can be found in structural water (water molecule

acting as a bridge between other molecules), between amino acids, between proteins and lignin,

between proteins and polysaccharides, between cellulosic and non- cellulosic polysaccharides,

35

and within cellulosic matrices. The intra- and intermolecular hydrogen bonds are responsible for

regular crystalline arrangements of glucan chains (Nevins, 1993). In addition, hydrogen bonds

help enzymes bind to their substrate (Kimball, 2003). Hydrogen bonds can be broken at elevated

temperatures or with Alkali, and the H-bonds between cellulose and strongly associated

hemicelluloses can be effectively broken by Alkali (Nevins, 1993; Theander and Westerlund,

1993).

The knowledge of the various linkages and their response to acidic and alkali reactions, though

limited, should inform any pretreatment strategy in protecting or maintaining structural integrity

of all sugars by breaking the bonds rather than degrading the sugars. Chesson (1993) suggests

that the least effective way in manipulating cell wall degradation is the disruption of lignin-

carbohydrate complex by hydrolysis of glycosidic linkages, which not only exist between lignin

and carbohydrates or between two carbohydrates but also within the carbohydrates. Acid

pretreatment of high severity could therefore be undesirable. In addition, breaking bonds that

release monomers might call for concern since the simple sugars released are more likely to be

degraded, especially if the feedstock would undergo subsequent chemical processes before

fermentation. Ensilage could provide a mild condition for pretreatment of lignocellulosic

feedstocks instead of the comparatively fast and severe conversional methods. If proven effective

and could also provide an avenue for manipulation of cell wall degradation.

36

Figure 2.4: Effect of pH on some structural components and linkages of plant cell wall

* (Stoddart, 2007) Rhamnogalacturonan II (RG-II) is the only boron containing polysaccharide of biological origin.

It contains unsual gylcosyl groups (~30) and O-acetyl substituent and is found mainly in primary cell wall as part of

pectin material (Aman, 1993; Nevins, 1993; O’Neil et al., 1996) and constituent about 4% of the primary cell wall

(Anonymous, 2009?). Pectins are responsible for adhesion between cell walls, and have similar degradation pattern

as hemicelluloses (Stoddart, 1983; White et al., 1993).

** (O’Neil et al. 1996) Borate esters cross-link pectic polysaccharide rhamnogalacturonan II (RG-II) within

homogalacturonan chains.

*** (Chesson, 1993; White et al., 1993) Gylcosidic bonds between different polysaccharides, between

polysaccharides and lignin or in different parts of the cell wall could be different/behave differently hence response

to acid treatment, extent and rate of degradation will also differ. The degree of degradation is also dependent on the

strength of acid, which would dictate the hydrogen concentration, the resident time and temperature. For instance

glycosidic bonds in neutral sugar side chains are very sensitive to hydrolysis than in main chain (Voragen et al.,

1995) and weak acids do not have any appreciable effect on monosaccharides e.g. glucose, fructose, dextrose, and

hydrolytic action on disaccharides is slow (Lowe, 1937).

37

2.6.2 Pretreatment mechanism of ensilage

Ensilage as a pretreatment tool has not been extensively investigated, however, it can be inferred

from discussions in earlier sections that clearly defining the mechanism or impact of ensilage

could be complex. Like conventional pretreatment methods, it is not likely that ensilage will

have the same effect on every feedstock.

A number of studies (McDonald et al. 1991; Muck, 1996; Richard et al., 2001; Ren et al. 2006)

have shown that the acidic condition of ensilage degrades the hemicellulose component of

biomass. The removal of hemicelluloses is comparable to the pretreatment mechanisms of

conventional acid or liquid hot water pretreatment in which the effectiveness of the process is

associated with the amount of hemicelluloses removed (Wyman, 1999; Sun and Cheng, 2002;

Mosier et al. 2005a). The degradation of hemicelluloses increases the surface area and allows

enzymes access to other structural components through the openings created by their removal.

Levels of hemicellulose degradation in silages can range from 0.5% or less (Muck, 1996) to as

high as 54% depending on the crop type and storage conditions (Ren, 2006). These

inconsistencies in degradation not only reflect the heterogeneity of biomass feedstocks, but also

the sensitivity of wet storage to different conditions, and perhaps also the complexity it presents

as a storage system.

The acidic conditions in silages, which are comparatively mild, are advantageous in preserving

sugar monomers while disrupting glycosidic bonds, and impact on other structural bonds as well

(see Figure 2.4). A microscopic investigation of ensiled feedstock by Donohoe et al. (2009)

showed some alteration in cell wall structure of the feedstock, although not as extensive as in

dilute acid pretreatment. Comparison with field-dried, senesced feedstock showed ensiled

feedstock had a looser cell wall structure (see Figures 2.5 and 2.6).

38

Figure 2. 5: TEM (Transmission Electron Microscopy) micrographs comparing unensiled,

ensiled and dilute acid pretreated (Adapted from Donohoe et al. 2009).

Figure 2.6 Microscopic images comparing unensiled and ensiled corn at x100 (left) and x300

(right) magnifications. (Adapted from Oleskowicz-Popiel et al. 2010).

39

It is almost indisputable that endogenous acid productions in silages have some effect on

feedstock structure. However, the practical significance of the effect needs to be characterized

and quantified.

Observations from various studies

(Chen, 2009) observed that although there were no significant changes in lignin content during

ensilage, polysaccharide content was progressively changing with a corresponding increase in

WSC denoting possible hydrolytic reactions in enzyme-ensiled samples. Ren (2006) observed

cellulose and hemicelluloses decomposition of 2-6% and 7-17% respectively in silage controls

(without enzymes) suggesting that plain silage has some pretreatment and hydrolytic potential.

Also in support of the hydrolytic potential of ensilage are Linden et al. (1987) Hen and Linden

(1994), Sheperd et al. (1995), Nadeau et al. (1996), Richard et al. (2001), and Digman et al.

(2007). Nadeau et al. (1996) showed that lignin was solubilized during rumen digestion of

ensiled forage, and this solubilization was possibly caused by modification of the ligno-

hemicellulose complex during silage hydrolysis of cellulose.

2.7 Modeling wet storage processes and logistics

A number of ensilage (wet storage) process models have been developed to simulate the

biochemical and microbial processes and changes taking place during storage. These models

(Pitt et al., 1985; Pitt, 1986, Mani et al., 2006; Ren, 2006; Chen, 2009) look at dynamic behavior

of silage microbes, dynamics of pH and water soluble carbohydrates, the effect of air infiltration

on dry matter losses, rate of dry matter loss, hydrolysis of feed stock when enzymes are used as

additives and the prediction of silage quality based on microbial population and silage acids.

These models are useful in elucidating the wet storage process and define general expectation of

the storage process. However, a greater concern is the impact of wet storage on biomass logistics

process. Logistics is a greater concern because wet storage for biofuel production has been

proposed as a value enhancing storage option. The transportation of low value water over long

distances to a biorefinery, however presents justifiable economic concerns.

A number of biomass supply studies, projects and logistics models have been developed to

elucidate and in some cases address biomass supply chain challenges. Most of these models are

40

prescriptive in nature suggesting or recommending the nature of expected collaborative

relationship and task distribution/coordination among players in the lignocellulosic biofuel

industry. These models also predict potential scenarios that would make techno-economic sense

in the production of ethanol. Generally, the whole logistics chain is considered, from harvest to

delivery at the biorefinery. These models usually embrace multiple farms and storage units

required to meet biorefinery annual feedstock demands. Some models place emphasis on

particular section of the supply chain, e.g. Ebadian et al. (2013) focus on analyzing storage

systems and their impact on cost incurred by various actors in the supply chain. Most models

assume farmer ownership of specialized harvesting equipment, which in farms less than 500

acres, may not be economically advisable (Brechbill and Wallace, 2008). One limitation in

current logistics models is the focus on dry storage systems. Also, in cases where moisture

content or dry matter loss inputs/parameters are required, most models use a single value.

Logistics model output estimates in the literature include: biomass delivery cost (Sokhansanj et

al., 2006; Kumar and Ileleji, 2009; Morey et al., 2010); Farm-gate or storage to biorefinery truck

traffic (Kumar and Ileleji, 2009); cost effectiveness of different feedstock types and formats

(Kumar and Ileleji, 2009; Ebadian et al., 2011); energy input and carbon/GHG emissions

(Sokhansanj et al., 2006; Morey et al., 2010); dry matter loss and feedstock moisture

(Sokhansanj et al., 2006); duration of logistics operations (Sokhansanj et al., 2006; Ebadian et

al., 2011); optimum number and optimum locations of storage systems, optimum radius of

supply or optimum locations and capacity of the biorefinery (Judd et al., 2010; Marvin et al.,

2011; Brownell and Liu, 2012; Cundiff and Grisso, 2012).

2.8 Summary of state of the art

Cellulosic biofuel research has been in progress for over 35 years. However, not until the last

decade and half has the research focus shifted from overcoming the recalcitrant nature of

feedstock to other issues necessary for the sustainability and techno-economic feasibility

required to facilitate industrial commercialization. One of the areas that have received

considerable attention in the last 8 to 10 years is feedstock acquisition and logistics, which has

exposed the immense challenges that collection, storage and transportation of feedstock present.

One of the ways in which these challenges can be addressed is through the adaptation of wet

41

storage systems, traditionally known as ensilage. Some of the major attractions of wet storage are

the prevention of potential fires associated with dry storage, the natural pretreatment ability of

silage, and the possibilities it holds for manipulation of feedstock to enhance downstream

processing – especially through very low severity pretreatment over the storage period.

Only a few studies have investigated silage application in the biofuel industry both as storage

and as a value enhancing pretreatment tool for ethanol production. These few studies show

ensilage has the ability to reduce dry matter losses, loosen cell wall structures, hydrolyze

cellulose and hemicelluloses and increase fiber reactivity. Some of the research findings thus far

indicate the possibility of obtaining up to 42% sugar yield for ensiled feedstock without

subsequent pretreatment. However, the interactions of silage products like organic acids with

downstream processes have not been characterized. Structural images show ensilage probably

affects feedstock in a similar manner to dilute acid pretreatment, although to a lesser extent.

More detailed quantitative investigations to fully characterize the impact on solid fractions and

downstream processes are needed. Previous studies all suggest that it would rarely be possible

for silage systems to be manipulated so that subsequent pretreatment and hydrolysis would not

be necessary. There is some promise, though, that ensilage could enhance the impact of

conventional pretreatment of feedstock and perhaps even eradicate the need for subsequent

hydrolysis (Thomsen et al., 2008).

Although, ensilage has great potential as a value-enhancing storage system, results have not

always been consistent. This is particularly seen in the differences in response of different

feedstocks to ensilage. This is not surprising, considering the highly heterogeneous composition

within and among feedstocks, which makes even the more familiar conventional material

handling and pretreatment processes a challenge. These different responses raise the question of

silage quality and fairness in transaction between producers and buyers. Some issues that might

still need to be addressed to establish the potential of silage include: (1) What are the optimum

conditions at which ensilage could serve as effective pretreatment tool, and can yields

comparable to conventional pretreatment methods be realized? (2) Do the products of ensilage

enhance or inhibit downstream processes and to what extent? How are the effects exhibited and

at what stage of the downstream process: reduced hydrolytic yields, delayed fermentation or

42

reduced fermentation yields? (4) What are the possibilities of modifying ensilage so that

subsequent pretreatment or hydrolysis is no longer required? (5) Are there are any consistencies

within the inconsistencies presented by various feedstock, handling methods, process and

environmental factors? (6) How can these inconsistencies be eliminated or managed to reduce

upstream process challenges and ensure more predictable downstream process outcomes (7)

What is the right post storage processing to complement the partial pretreatment or hydrolysis

achieved through ensilage, so as to maximize the cost saving potential of silage altered

feedstock? (8) How can we evaluate quality of ensilage and accurately predict ethanol yield (9)

What is an appropriate and robust standard or quality indicator that can facilitate fair transaction

between producers and buyers.

This dissertation intends to address some of these issues by investigating how wet storage, in

particular organic acid production in unamended silages, affects downstream processing and how

that can translate to cost savings or other benefits. Although fresh feedstock is ideally the best for

bioprocessing, it is not a practical option for seasonal feedstocks. There is, however, the

possibility that ensilage can attain values superior to the original fresh crop in the future

(Charmley, 2000) if systematically designed.

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Chapter 3

Corn stover evaluation after wet storage: relating storage conditions to

storage outcomes

Abstract

As interest in wet storage of biomass feedstock for biofuel production increases, so do concerns

about feedstock quality under various storage conditions. Of particular concern is the impact of

different storage moisture levels on dry matter loss and biomass composition. Challenges in

meeting quality requirements could discourage farmers’ participation in supply chains and

impact biofuel feedstock cost and supply. This would potentially affect the commercial viability

of the industry. A major question is whether farmers have to control the moisture of biomass

going into storage as well as storage conditions in order to achieve desired feedstock quality.

This is important as it would define the harvesting and collection window, since harvesting at the

proper moisture is less costly than trying to change biomass moisture after harvest by either

drying or wetting. This paper addresses this question and a broader logistic concern by

examining corn stover under different wet storage conditions. The results shows anaerobic wet

storage (e.g. ensilage) offers farmers more flexibility than aerobic dry storage (e.g. hay), since

feedstock compositional changes under anaerobic conditions are more consistent under a wider

range of moisture levels and storage conditions, and dry matter loss is generally less. Since

transportation cost is likely to increase with moisture and quality of feedstock is likely to remain

the same, the restrictions on moisture in anaerobic storage systems will be influenced by

economic tradeoffs [of transportation] rather than feedstock quality. Under a decentralized on-

farm biofuel processing system, storage moisture would be of little significance both in terms of

quality and economics. Such a decentralized system is currently not techno-economically

feasible.

Key words: Corn stover, ensilage, wet storage, dry matter loss, hemicelluloses, biofuels, organic

acids

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3.1 Introduction

Corn stover is a lignocelluosic feedstock abundantly accessible in the U.S. Stover is the above

ground residue after the grains are harvested from corn plants, and it constitutes about a fourth of

the total agricultural residue produced in the U.S. Lignocellulosic feedstocks are non-food, plant-

based resources, and compare favorably relative to corn grain in most environmental

sustainability analyses of biofuel production (Farrel et al., 2006; Edwards et al., 2008; Fagione et

al., 2008; Searchinger et al., 2008; Liska et al., 2009). These lignocellulosic sources include

agricultural residues, forest products/residues, and energy crops. Lignocellulosic fuels are

expected to play a key role in U.S. energy security by reducing dependence on foreign oil in the

future. Until the last decade, most lignocellulosic biofuel research has focused primarily on

enzyme prospecting and manipulation, pretreatment options and fermentation technologies, all

aimed at economically maximizing biofuel output from the highly recalcitrant lignocellulosic

feedstocks. Implicit to this prioritization was the assumption that large supplies of feedstock

would be inexpensive and readily available whenever the technical challenges of conversion

technologies were solved. This assumption overlooked logistics and supply chain management

issues, which embraces techno-economic challenges of production, harvesting, collection,

storage, and transportation to the refinery. The critical influences of these factors are seen in

estimates of feedstock cost, which constitutes the largest cost component, 35-50% of total

production cost (Lynd, 1996; Hess et al. 2007). More recent estimates, for 2010, estimated

feedstock cost as 27% of ethanol selling price and projected its share to be 34% in 2012

(Humbird et al., 2011). These issues must be addressed to ensure long time sustainability of

lignocellulosic biofuels, economic competitiveness and the rate at which the industry grows

(Hess et al. 2007; Murano, 2007; Bevill, 2011; DOE/EERE, 2012).

This study, therefore, focuses on aspects of logistics related to wet storage, and its impacts on

feedstock composition and variability. Wet storage systems from several other industries (e.g.

livestock, pulp & paper, timber) have demonstrated effective preservation and enhanced end

product quality, and suggest strategies that should be considered for the biofuel industry.

Because of the low bulk density, the wet storage systems that are most applicable to the

herbaceous biofuel industry are the Ritter process and ensilage. Wet storage offers flexibility in

harvest times under different field moistures and is compatible with narrow harvest windows.

56

Richard et al. (2001) proposed the adaptation of ensilage as a wet storage method to reduce the

risk of fire outbreaks associated with dry storage of biomass and to minimize loss in

carbohydrate content of the biomass. In addition, wet storage can potentially alter feedstock

structure to enhance downstream processes. For instance, particleboard manufactured from

ensiled stover stored for 21 days showed improvements over unensiled stover with respect to

mechanical properties and dimensional stability measured by ANSI standards. However, at 189

days of storage, mechanical strength decreased and water absorption increased (Ren et al., 2006).

These outcomes at longer duration, while problematic for the particleboard industry, may be

desirable in the biofuel industry by facilitating hydrolytic reactions and requiring less severe

pretreatment downstream. Furthermore, the space requirements for wet storage could be up to

ten times less than dry storage, depending on the storage design and compaction density (Kram,

2008).

Ensilage has been used in the animal feed industry for preservation and feed enhancement for

centuries. Recommended moisture levels for ensiled storage range from 40 – 70%, depend on the

storage structure (Jones et al., 2004). Feedstock is compressed, either mechanically or by gravity

and covered to ensure anaerobic condition. Specific guidelines on cut height, chop length, filling

rate, density, moisture, additives, etc. have been developed to maintain and improve feedstock

quality and digestibility (Jones et al., 2004). Studies specific to the biofuel industry have also

suggested, indirectly, that structural changes occur during ensilage that are or may be beneficial

to subsequent processing (Linden et al., 1987; Ren, 2006; Thomsen et al., 2008; Chen, 2009). In

this study, these supposed changes are examined directly and evaluated for their significance.

The Ritter process, traditionally used for baggase storage for the pulp and paper industry, has

also been explored as an option for the biofuel industry (Atchison and Hettenhaus, 2004). This

process involves stacking water-saturated biomass in large open piles that are maintained

through recirculating of water through the pile. Anaerobic conditions are created through water

saturation recirculations, as water has a very low diffusion coefficient for oxygen. The surface of

the pile, however, is exposed to some level of aerobic activity that can result in degradation/ dry

matter loss. Applying the Ritter process to corn stover showed that some of the assumptions for

57

feedstock quality in the pulp and paper as well as livestock industry may not necessary apply to

biofuel. For instance, lactic acid dominance is recognized as necessary for well-preserved

feedstock in the livestock industry (Piltz and Kaiser, 2003). However, analysis of Ritter piles

showed there were no lactic acid producing microbes nor was there any lactic acid detected

(Atchison and Hettenhaus, 2004), yet pH values and dry matter loss were similar to and within

recommended range of well designed ensilage. Ren (2006) also observed results in which no

lactic acid was produced in plain corn stover silages. However, Ren’s (2006) pH values (5.02 -

5.23) obtained for ensiled corn stover were higher than those obtained in Atchison and

Hettenhaus’s (2004) corn stover Ritter pile (pH of 3.50 – 4.00). Fermentation products of the

corn stover Ritter process were acetic, propionic and butyric acids. Another difference related to

end use is that water soluble constituents, including soluble sugars, are undesirable in the pulp

and paper industry but essential intermediates in ethanol fermentation. The implications are: (1)

the end use goal should define what conditions constitute good practice, and (2) an appropriate

wet storage condition or outcome for biofuel production may not fit the conventional criteria that

determine quality of feedstock for the animal or paper & pulp industry.

Two main issues arise when considering wet storage options for biofuels. First, should there be

standardized conditions of storage to meet quality requirements for biofuel? Second, can wet

storage for biofuel purposes successfully embrace moisture levels outside what is typical to

accommodate the varied conditions at different regions, different cropping seasons, and different

harvest times or field moistures? The more restricted storage conditions are, the more the

demand on farmers’ attention and time for monitoring and management. This could also result in

higher cost, therefore making wet storage less attractive.

In this study, corn stover was analyzed after wet storage to determine how different storage

conditions affected dry matter loss, organic acid profiles and feedstock composition. These

outcomes can be used in defining feedstock quality with respect to biochemical biofuel

production. Of particular interest is the effect of moisture, which varies widely with location and

harvest time, and which in the livestock industry is noted to affect storage outcomes. Storage

moisture levels covering this wide range of potential harvest conditions were examined to

account for variations across different regions and fields. These includes levels outside the

58

typical 55 to 70% moisture for traditional silage process (Jones et al., 2004), in order to

determine the degree of variability in storage outcome and suitability for biofuel industry.

Experimental plans and experimental data are available in Appendix B.

3.2 Methodology

3.2.1 Stover description and storage

Corn stover was obtained in 2008 from the US Department of Energy’s Idaho National Lab (IA

(Iowa) stover) and the Penn State Dairy farm (PA (Pennsylvania) stover). The IA stover, Pioneer

brand 34A20, was conventionally harvested in fall of 2007 from the Boyd plot near Boone, IA

and field-dried for about 3 -5 days. It was raked, baled, transported to Idaho and stored indoors

with a tarp cover to prevent dust accumulation. Particle size reduction to 1 inch minus (less or

equal to 25.4 mm) was carried out in the early summer of 2008. The PA stover, Dekalb DKC54-

46, was left in the field after grain harvest in the fall of 2007 where it was exposed to weather

through the winter of 2007, and was then harvested in spring of 2008.

For both stover sources the particle size distribution was analyzed using the Penn State Forage

Particle Separator. The IA stover had ~26 % of particles greater than 19 mm, 26 % were

between 8 and 19 mm, 31 % were between 1.18 and 8 mm and 17 % were less than 1.18 mm.

The PA stover had a similar distribution; ~26 % of particles greater than 19 mm, 26 % were

between 8 and 19 mm, 37 % were between 1.18 and 8 mm and 11 % were less than 1.18 mm. A

description of this particle size separation method can be found in Heinrichs and Kononoff

(2002).

The initial moisture contents of the IA-fall harvested and PA-spring harvester stovers, ~7% and

~30% respectively, were adjusted to seven moisture levels, from 15% to 75%, in increments of

10%. All moisture measurements are reported on a wet basis (mass water/(mass of water plus dry

biomass). Moisture adjustment was accomplished by spraying with appropriate amount of water,

mixing and leaving overnight to be well absorbed into fibers. For PA 25% moisture, the stover as

received was dried and rewetted to achieve the desired moisture. The moisture adjustments were

within ±2 percentage units of the target moisture levels at the start of the storage experiments.

Corn stover was packed at a density of about 159 dry Kg/m3 in 1 pint (0.00047 m

3) glass canning

59

jars that were tightly sealed with a metal lid to create anaerobic conditions or covered with

Whatman No.1 paper held in place with a metal ring to create aerobic conditions. BBLTM

Anaerobic indicator strips (Becton, Dickenson & Co., Sparks, MD) were placed at the bottom

and near the top of a third of the storage jars, to check the aerobic conditions of storage. Storage

durations investigated were 0, 21, 90 and 220 days at 37oC and ambient room temperature (~23 ±

1oC). Aerobic samples were stored for up to 90 days and anaerobic for up to 220 days, although

only IA stover had 90-day anaerobic storage and at 37oC. PA samples did not include the 15%

moisture condition. Each storage condition was stored in triplicate, making a total of 519

samples. After storage, samples were analyzed for changes in organic acids and pH. Samples

were then dried to constant weight in a HotPack convection oven at 55oC, ground using a 2 mm

screen on a Wiley Mill (Model 4, Thomas Scientific, Swedesboro, NJ) and stored at room

temperature in sterile airtight Whirl-Pak bags (Nasco, Fort Atkinson, Wisc.) prior to

compositional analysis.

3.2.2 Dry matter loss (DML)

Dry matter loss was determined gravimetrically and was also estimated from the organic acids

produced during anaerobic wet storage. For the gravimetric method, weight and moisture content

of each sample was taken before and after storage. Moisture content was determined by drying in

a HotPack convection oven at 55oC to constant weight. Dried samples were placed in Nalgene

®

desiccator cabinet for moisture free cooling before weighing. This was to reduce possible errors

in weighing due to relative humidity/ moisture absorption from atmosphere. The percentage dry

matter loss was calculated using Equation 1. Initial weight and moisture fraction refers to day 0.

(1)

Dry matter loss was also estimated from organic acids using Equation 2. Dry matter loss in this

case is associated mainly with carbon dioxide evolution during acid fermentation and to a lesser

extent through hydrogen and water produced in some silage fermentation processes. The

60

fermentation reaction stoichiometries were used to measure the amounts of carbon dioxide,

hydrogen and water liberated for every mass of acid produced. This method would be referred to

as stoichio-gasometric method. See Appendix B for more details on this method, potential

microbial reactions and the selected stoichiometries used in dry matter loss calculation.

∑ ( (

)) ∑ ( (

)) ∑ ( (

))

(2)

Where W=Mass of acid in silage; OA = individual organic acid; m = no. of moles; MM = molar mass; DM = dry

matter

3.2.3 Organic acid profile and pH

The organic acid profile represents the types and amounts of water extractable acids present in

feedstock. Soluble extracts for pH and organic acid measurements were collected before and

after storage, before samples were dried and ground as required for compositional analysis of

other constituents. Stover samples were thoroughly mixed before sub-sampling, and deionized

water was added at a ratio of 1:10, wet basis. The mixtures were shaken for 30 minutes at 200

rpm using a Barnstead SHKA 2000 open air platform shaker (Barnstead International, Dubuque,

IA) after which the extracts were filtered through Whatman No.1 paper. The pH of extracts was

determine using SevenEasy S20 pH meter (Mettler-Toledo International Inc, Columbus, OH)

and calibrated with standard buffers 2, 4 and 7 for anaerobic samples and 4, 7 and 10 for aerobic

samples. The collected extracts were filtered again using 0.2 m PTFE filters, diluted 20-fold

and analyzed using Dionex ICS 3000 ion exclusion chromatography (Thermo Fisher Scientific

Inc., Dionex ICS 3000, Sunnyvale, CA) for types and amount of organic acids. Separation was

performed at 30oC using IonPac ICE-AS1 guard (4 x 50 mm) and analytical (4 x 250 mm)

columns with 100 mM methanesulfonic acid eluent at a flow rate of 0.16 mL/min. Organic acids

were detected with a photodiode array detector (Thermo Fisher Scientific Inc., Dionex UVD

340U, Sunnyvale, CA) at a wavelength of 210 nm. Thirteen different potential acids (lactic,

acetic, butyric, pyruvate, isobutyric, valeric, isovaleric, propionic, tartaric, malic, formic, citric,

succinic) were used as standards.

61

3.2.4 Corn stover compositional analysis

Corn stover composition before and after storage was determined by quantitative saccharification

using adapted biomass analytical methods from the U.S. National Renewable Energy Laboratory

(NREL). These methods can be found at

http://www.nrel.gov/biomass/analytical_procedures.html and include: preparation of samples for

compositional analysis (Hames et al. 2008); determination of extractives in biomass (Sluiter et

al., 2008a); determination of structural carbohydrates and lignin in biomass (Sluiter et al.,

2008b); and determination of ash in biomass (Sluiter et al., 2008c). Samples were dried at 55oC

instead of the recommended 45oC. Water and ethanol extractions were accomplished using

Dionex Accelerated Solvent Extraction 350 (ASE 350) (Thermo Fisher Scientific Inc., Dionex

ASE 350, Sunnyvale, CA) system. Extracts were dried to sludge using BUCHI multivapor p-12

system (BUCHI Labortechnik AG, Switzerland) before placing in SHEL LAB vacuum oven

(Sheldon manufacturing Inc. Cornelius, OR) at 45oC, 25 in Hg, for at least 24 hours to complete

the drying. Sugar composition was analyzed using Dionex IC 3000 Ion Exclusion

Chromatography (ICE). Sugars were separated by high pH anion exchange at 30oC using

CarboPac PA20 guard (3 x 30 mm) and analytical (3 x 150 mm) columns with 2 mM sodium

hydroxide (NaOH) at a flow rate of 0.5 ml/min. Monosaccharides were detected by pulsed

amperometric [electrochemical] detection at gold working electrodes, using quadruple

waveform.

Each individual component of biomass composition was calculated as a percentage of the whole

biomass on a dry basis (0% moisture). The percentage change from day zero was calculated as

follows (Equation 3):

(Mass basis)

( )

(3)

http://www.nrel.gov/biomass/analytical_procedures.html

62

3.2.4 Data analysis

Results were analyzed using statistical tools including analysis of variance (ANOVA), Tukey's

multiple comparison test and regression. All statistical tests were assessed using a significance

level, α, of 0.05. Statistical software used for these analyses was Minitab 14 (Minitab Inc., State

College, PA). Graphs were generated in Microsoft Excel 2010.

3.3 Results and Discussion

3.3.1 Anaerobic indicator strips

The anaerobic indicator strips were originally blue under aerobic conditions and turn white under

anaerobic conditions. These colors could readily be seen through the glass walls of the storage

jars. Observation of the strips showed that anaerobic conditions were maintained for all

anaerobic storage samples except for one replicate of the day 21, 45% moisture PA sample

stored at 37oC. Although anaerobic conditions existed at the beginning for this sample, the strips

turned bluish by the end of two weeks indicating air infiltration had occurred. On the other hand,

aerobic samples did not all maintain the blue color. The 25% moisture samples were consistently

blue both top and bottom. The 35% samples maintained aerobic conditions throughout the jar for

the first week, after which the bottom indicator started showing variations, with some indicators

pale blue or whitish blue. For 45 – 75% moisture, by the fourth day of storage, both top and

bottom indicators where turning whitish. This result is consistent with rapid degradation under

these moisture contents, which in turn would have consumed oxygen more quickly than it could

diffuse into the vessel through the Whatman filter paper in the lid. In some cases, the bottom

indicators were partly blue and partly white indicating degradation rates, oxygen diffusion and/or

distribution through the jar was not uniform. By the second week of storage, almost all indicators

both bottom and near top for the high moisture aerobic samples were white indicating anaerobic

conditions during the aerobic storage. The exceptions were some PA samples of 45% moisture,

which maintained some pale blue color. At the end of storage duration some of the white strips

had turn blue again, which could be a result of slower degradation, or of moisture loss

contributing to increased porosity and greater permeability for air movement.

63

3.3.2 Dry matter loss

Wet aerobic storage

Generally, dry matter loss (DML) under aerobic conditions increased with moisture (p < 0.0001)

and duration (p < 0.0001), as illustrated in Figure 1. The average dry matter loss was ~ 15 % and

23% dry basis at day 21 and day 90 respectively. The maximum dry matter loss was ~43% at

day 90. At 23oC, dry matter losses were not significantly different for IA and PA stover types (p:

0.225 for day 21 and 0.395 for day 90), however, at 37oC, losses in IA stover were significantly

higher than PA stover (p: 0.009 for day 21 and 0.019 for day 90). Within stover type, however,

there were no significant differences in dry matter loss for each storage duration at the two

temperatures (23oC and 37

oC) (p > 0.05). For both IA stover stored at 37

oC_90 days and PA

stover stored at 23oC_90 days, dry matter losses at each storage moisture level were all

significantly different from each other (p: both IA and PA < 0.0001). At other storage

temperatures and durations, the IA and PA stover samples that were not significantly different

generally clustered into the following moisture groupings: 15 - 25%; 35% (sometimes including

45%), 45 - 65% (sometimes including 75%), and 75%. These groupings were defined using a one

way ANOVA in conjunction with Tukey test to find means that are significantly different. The

values in brackets have confidence intervals that embrace at least two other samples that have

significantly different means. These results suggest that DML can be generally categorized into

three groups: low DML for the driest (15% -25% moisture) aerobic storage conditions, higher

DML for a moderate moisture aerobic storage condition (35%), and very high DML for medium

and high moisture (45%) aerobic conditions. Moisture is the most influential determinant of

DML, accounting for ~55% of the variability in lumped analysis of DML for both IA and PA

stover, and combined with duration this accounts for ~ 69% of variability. These two factors

overshadow the effect of temperature. Lines of best fit (Figure 1) show that at day 21, very high

moisture samples generally had lower DML, possibly due to anaerobic conditions created by

rapid initial degradation and excess water restricting oxygen diffusion. However, by day 90,

degradation would have slowed and much of the moisture would have been lost due to gas

exchange in these aerobic jars, creating a more aerobic condition and leading to more DML.

64

Figure 3.1: Dry matter loss from aerobic wet storage. Purple = day 21; Brown = day 90; Plain

=23oC; diagonal wall =37

oC

Temperature Duration (days) Best Fit Dry matter loss = R2

23oC 90 Linear 0.7127 x Moisture - 9.2045 0.93


23oC 21 Poly -0.0135 x Moisture 2 + 1.4983 x Moisture - 22.106 0.87


Temperature Duration (days) Best Fit Dry matter loss = R2



23oC 21 Poly -0.0095 x Moisture

2 + 1.2328 x Moisture - 23.209 0.98

37oC 21 Poly -0.0098 x Moisture 2 + 1.2340 x Moisture - 21.211 0.93

0

10

20

30

40

50

15 25 35 45 55 65 75

Dry m

atte

r los

s (%

dry

bas

is)

Nominal storage moisture (%)

0

10

20

30

40

50

15 25 35 45 55 65 75

Dry m

atte

r los

s (%

dry

bas

is)

Norminal storage moisture (%)

Spring harvested, PA stover

Fall harvested, IA stover

65

Anaerobic storage

Under anaerobic conditions, gravimetric dry matter loss was generally not significantly different

(p = 0.102) for fall harvested (IA) stover and spring harvested (PA) stover, with average dry

matter losses of 1.6% and 2.3% respectively. Gravimetric measurements of dry matter loss at

these low levels is quite difficult due to the heterogeneous moisture content of samples before

and after storage. Estimates using the stoichio-gasometric method were 1.3% and 1.8%

respectively, and with greater consistency among replicates, the DML from the latter biomass

type (spring harvested PA stover) was significantly higher than the fall harvested IA stover (p <

0.0001). Moisture content of feedstock at the end of storage was within ±1 – 2 percentage points

from starting moisture (see Appendix B). With respect to temperature, there were no significant

differences (p = 0.870) at the two levels tested (23oC and 37

oC), both within and across stover

types. Storage duration was a significant factor, with dry matter losses at 220 days higher than

losses at 21 days (p < 0.0001). On average, gravimetric dry matter loss at 220 days of storage

was less than 3%, compared to less than 1% at 21 days of storage. The maximum dry matter loss

measured gravimetrically was about 9% at day 220 (23oC_ 45%, PA stover). The more

consistent and replicable stoichio-gasometric losses averaged just slightly more than 1% on day

21, and were still less than 2% at day 220. Using this method, maximum dry matter loss was ~

4% at day 220 (23oC_ 35%, IA stover).

The effect of storage moisture on dry matter losses of the IA stover was significant for samples

stored for 21 days (both temperatures) and samples stored for 220 days at 37oC. However, there

was no significant difference in dry matter loss across moisture for the samples stored at 23oC for

220 days (p = 0.472). At 23oC_ 21 days, dry matter loss was not significantly different for all

moisture levels except at the 65% level (p = 0.032). At 37oC, for both day 21 and day 220, dry

matter losses at the various moisture levels overlap but can be generally grouped as: 15-25%

(sometimes including 35%); 35-65% (sometimes including 75%); and 75% (p = 0.006 for day

21and 0.003 for day 220). For PA stover, there were no significant differences in dry matter

losses across the different moisture contents for each storage temperature and duration, except

for storage at 37oC for 220 days. Even at this temperature and duration, there was no significant

difference in losses at 35 – 65% (and sometimes 75%) moisture levels, similar to what was

66

observed with the IA stover. As with aerobic samples, these groupings were defined using a one

way ANOVA in conjunction with Tukey test to find means that are significantly different. The

values in bracket have confidence intervals that embrace at least two other samples that have

significantly different means. Table 1 shows regression relationships at the various storage

conditions as well as dry matter loss groupings.

67

Temperature Duration (days) Best Fit Dry matter loss = R2Dry matter loss groupings

23oC 220 Poly -0.2054 x Moisture2 + 1.8567 x Moisture - 1.4962 0.86 (15%), (25 (65-75)%), (35 (45-55)%), (45-75%)

37oC 220 Poly -0.1338 x Moisture2 + 1.3862 x Moisture - 1.3329 0.80 (15%, (25, 45, (65-75)%), (25-75%)

23oC 21 Poly -0.1381 x Moisture2 + 1.3223 x Moisture - 1.2148 0.79 (15 - 25%), (35-75%)

37oC 21 Poly -0.0690 x Moisture2 + 0.6603 x Moisture - 0.5291 0.60 (15 -75%)

23oC 220 Poly "-0.1869 x Moisture2 + 1.6236 x Moisture - 0.701 0.41 (15 - 75%)

37oC 220 Linear 0.9698 x Moisture - 2.0843 0.75 (15-25 (35)%), (35-65 (75)%), (75%)

23oC 21 Poly -0.1187 x Moisture2 + 1.2359 x Moisture - 2.0828 0.30 (15-55 (75)%), (65%)

37oC 21 Linear 1.0779 x Moisture - 3.386 0.83 (15-25 (35)%), (35-65 (75)%), (75%)

23oC 220 Poly -0.1215 x Moisture2 + 1.1620 x Moisture - 0.2288 0.48 (25, 45, (65-75)%), (35-75%)

37oC 220 Poly -0.0749 x Moisture2 + 0.7033 x Moisture + 0.2373 0.23 (25-75%)

23oC 21 Poly - 0.1373 x Moisture2 + 1.943 x Moisture -0.6146 0.63 (25, (55-75)%), (35, (45-65)%), (55-75%)

37oC 21 Poly - 0.0554 x Moisture2 + 0.4815 x Moisture + 0.7592 0.21 (25-75%)

23oC 220 Poly -0.2484 x Moisture2 + 2.229 x Moisture - 0.8691 0.37 (25-75%)

37oC 220 Linear 0.9463 x Moisture - 0.6571 0.84 (25%), (35-65 (75)%), (75%)

23oC 21 Poly 0.1504 x Moisture2 - 0.8574 x Moisture+ 1.5457 0.25 (25-75%)

37oC 21 Poly 0.0234 x Moisture2 - 0.1109 x Moisture + 0.4852 0.07 (25-75%)

IA stover

PA stover

Stoichio-

gasometric

Gravimetric

Stoichio-

gasometric

Gravimetric

Table 3.1: Regression fits for anaerobic wet storage at different temperature and durations*

* The dry matter losses in the table are grouped by storage moisture. The grouping is based on analysis using one way

ANOVA in conjunction with Tukey test to find means that are significantly different. The values in brackets are not

significantly different from preceding values but are also not significantly different from at least one other value, whose mean

dry matter loss is significantly different from that of the preceding value.

The dry matter loss equations were derived from regressions of the mean dry matter losses.

68

Unlike the aerobic samples, gravimetric dry matter losses under anaerobic conditions were more

variable but generally low (Figure 2). Stoichio-gasometric dry matter losses were reasonable,

less variable and generally lower than amounts obtained through gravimetric methods. The

higher values in the latter may be due to potential losses of organic acids during drying which

would decrease the mass of feedstock. In butyric acid fermentation, additional losses from

protein degradation are expected and could be accounted for through the loss of ammonia or

CO2. Such protein losses were not taken into account in this Stoichio-gasometric estimation.

69

Figure 3.2: Dry matter loss from anaerobic wet storage. Stoichio-gasometric dry matter loss is

indicated with black error bars: Purple = day 21; Orange = day 220; Plain colors indicate 23oC;

diagonal patterns indicate 37oC. Empty bars with red dots emphasizing their mean values

indicate gravimetric dry matter losses.

70

Wide variations in dry matter loss within samples stored at the same moisture and same duration

were also observed by Chen (2009). Chen’s estimates of DML ranged from less than 2% to 12%

for stover stored at 60% moisture for 21 days. Some of Chen’s stover were harvested at different

dates, and the wide range of dry matter losses are indicative of variability not fully accounted for

by storage moisture. The results, as with this current study, shows moisture and temperature are

not good predictors of dry matter loss under anaerobic conditions. Some samples had negative

dry matter losses, which could be due to variability and/or inaccuracies in moisture values during

attempt to select lumped representative samples from Day 0, when compared to what the actual

moisture of each sample in storage would have been at Day 0. This variability could be as a

result of the heterogeneous nature of feedstock. Measured moisture contents were within ± 2% of

target moisture, and a representative sample that deviates even with such a small margin from the

actual sample, could easily lead to inaccuracies in calculations of dry matter losses especially for

the higher moisture treatments.

Dry matter loss regression fit

Linear regression of dry matter loss against moisture content for aerobic samples indicated that

the relationship between these two variables was significant for each stover type (IA stover and

PA stover) and each storage condition: 23oC for 21 days; 23

oC for 90 days; 37

oC for 21 days;

37oC for 90 days (p < 0.001). Moisture accounted for 49 to 98% and 66 to 98% of the variation

in dry matter loss for IA and PA stover respectively. Although a linear model is significant, from

Figure 1, it can be observed that a 2nd

order polynomial model presents a better fit for 21 days of

aerobic storage, with dry matter loss peaking around 55% moisture. Coefficients of

determination at this storage duration for a linear model were about 52% at 23oC for IA stover,

while for PA stover they were 71% at 23oC and 79% at 37

oC; for the same storage duration with

a polynomial fit the coefficients of determination were 87% at 23oC for IA stover, while for PA

stover they were 93% at 23oC and 98% at 37

oC. The stable dry matter loss at 65% and 75%

moisture predicted by the polynomial model fits the data well, and is much lower than predicted

by the linear model at these higher moisture levels. Relatively constant dry matter loss at higher

moisture levels is and indication that moisture is no longer limiting, and any decreases from the

maximum dry matter loss could be due to prolonged anoxic conditions created by the high

71

moisture and the very slow diffusion of oxygen through water (approximately four orders of

magnitude slower than through air). By day 220, a number of factors could extend the oxygen

front from top to bottom and allow fully aerobic conditions. These could include natural loss of

moisture over time, increased for the higher storage temperature of 37oC, as that water loss

creates more pore space for air movement. A lumped analysis, disregarding stover type, storage

temperature and duration, showed a significant linear estimation of dry matter loss (p < 0.0001)

although moisture accounted for only 50% of the variation. Equation 4 shows the lumped

relation between dry matter loss and moisture under aerobic storage.

(4)

Although the differences in dry matter loss across moisture levels were almost non-existent for

anaerobic samples due to variability within samples, linear regression of the means gave

reasonable coefficients of determination (R2: ~75 -83%) for storage at 37

oC. The polynomial

model gave R2 values that were 1-2 % higher than those of the linear model. The lines of best fit

were generally worse at 23oC compared to 37

oC. Table 1 shows only the best fit equations. A

lumped analysis showed that under anaerobic conditions moisture accounted for about only 9%

of the variation in dry matter loss, while duration accounted for ~16% and temperature’s role

was not significant.

Fitting a Monod and Droop curve to DML data

The dry matter loss polynomial fit suggests that at higher moisture content, dry matter loss

decreases. This is not in conformity with general expectation that DML increases with storage

moisture, although this is not necessarily a linear relation (McGechan , 1990; Buckmaster, 1992;

Emery and Mosier 2012). To address this possibility, an asymptotic fit is evaluated that would

allow dry matter loss to plateau above a certain moisture level. The Monod and Droop models

were fitted to the dry matter loss data and the model parameters were estimated using the least

squares method. Two constraints applied were (1) 0 ≤ dry matter loss ≤ 100, and (2) 0 ≤ moisture

≤ 75. Generally, the Droop model fit the data better than the Monod model, although both

models were poor fit for the anaerobic results. Dry matter loss under aerobic conditions gave

72

better R-square values for these two models compared to the corresponding regressions for

anaerobic samples. Table 2 shows the estimated parameters for the Monod and Droop models

and Figure 3 compares the estimated DML for some conditions with actual gravimetric

measurements.

The Monod and Droop models each have two estimated parameters. The best fit equations for

each model are provided in Table 2. The Monod “K”, which is a parameter representing the

moisture level at which maximum dry matter loss is halved, was 75% for almost all aerobic and

anaerobic conditions. The maximum dry matter loss (DMLmax) is the second estimated parameter

of the Monod equation. The implication of having a Monod “K” of 75%, as suggested by the

Monod model assumptions, is that when moisture levels are lower than 75% moisture, dry matter

loss can be estimated using a linear relationship with DMLmax/K as the slope. The DMLmax

estimate from the Monod equation for aerobic storage was up to 70% for IA stover and 63% for

PA stover, compared to the actual 43% and 39% maximum observed for IA and PA stover

respectively. These maximum values were observed at 75% moisture levels at day 90 and are

more than half the predicted maximum. The Monod model therefore generally underestimates

dry matter loss but suggest the possibility of higher losses. The Monod “K” also suggests that as

long as moisture is ≤ 75%, the possibility of dry matter losses greater than 43% is very low.

The Droop “K”, which is a parameter representing the moisture content at which dry matter loss

is zero, also gave reasonable values under aerobic conditions, indicating that at moisture levels of

less than 15 to 24% dry matter loss is expected to be zero. The other parameter estimated for the

Droop model was DMLmax which is also maximum dry matter loss. Applied to microbial growth

functions, the Monod DMLmax and the Droop DMLmax are different, with the former defined by

external substrates while the latter defined by internal nutrients/substrates. In this analysis we are

using the form of these curves rather than attempting to describe microbial growth rate

mechanisms, so this parameter can be assumed to represent the same thing, maximum dry matter

loss. The maximum dry matter losses estimated from the Droop model were lower than those

estimated from Monod and also closer to values observed in this study.

For anaerobic, ensiled storage, fitting the Monod and Droop model to dry matter loss estimated

by the stoichio-gasometric method gave better results than for the gravimetric estimates of DML

73

for IA stover. Droop R-square was above 53% for 21 and 220–day storage at 23oC and

approximately 49% for 220-day storage at 37oC. The R-square values for PA stover were all

below 19%. Both models gave maximum dry matter loss under anaerobic conditions to be less

than 5%. Droop’s “K”, that is moisture content at which dry matter loss is zero, was less than

16%.

Table 3.2: Estimated parameters and R-square values from fitting Monod and Droop models to

dry matter loss data

* K value means difference things for the different models. Monod = moisture content at which maximum dry

matter loss (DMLmax) is halved; Droop = moisture content at which dry matter loss is 0.

** Aerobic samples were stored for 21 and 90 days not 220 days

Monod Droop Monod Droop Monod Droop Monod Droop

IA 1.73 1.46 4.03 2.70 3.04 3.92 6.16 4.93

PA 3.47 1.83 0.59 0.59 3.59 3.59 9.77 7.06

IA 75.0 22.8 75.0 26.2 18.1 8.2 75.0 21.8

PA 75.0 12.1 0.0 0.1 0.0 0.0 75.0 20.8

IA 56.52 53.27 142.63 104.61 80.27 80.18 102.99 72.70

PA 102.03 104.51 39.49 39.49 74.22 74.22 55.52 48.81

IA 0.06 0.12 0.17 0.39 0.04 0.04 0.30 0.50

PA 0.03 0.01 0.00 0.00 0.00 0.00 0.37 0.44

IA 3.27 2.29 1.69 1.30 3.43 2.98 4.48 2.80

PA 1.69 1.70 1.69 1.72 2.78 2.72 1.93 1.91

IA 58.41 14.59 43.87 13.31 33.49 13.29 75.00 15.45

PA 2.98 2.98 1.19 1.85 12.06 8.47 6.95 5.50

IA 8.25 6.27 12.27 11.83 11.72 8.34 15.09 12.33

PA 4.87 4.87 3.64 3.63 4.65 4.37 6.45 6.41

IA 0.38 0.53 0.11 0.14 0.36 0.55 0.38 0.49

PA 0.01 0.01 0.00 0.01 0.13 0.18 0.02 0.03

IA 37.29 23.69 51.92 33.14 69.95 45.62 65.86 42.48

PA 32.21 24.51 34.45 24.32 63.12 52.15 44.62 34.91

IA 75.00 15.98 75.00 16.50 75.00 17.08 75.00 16.88

PA 75.00 21.96 75.00 19.96 75.00 24.13 75.00 22.73

IA 649.89 454.52 536.93 326.01 1278.24 747.20 981.91 652.56

PA 239.32 82.04 168.07 66.03 1091.78 145.42 417.33 76.19

IA 0.53 0.67 0.76 0.85 0.72 0.84 0.75 0.83

PA 0.61 0.86 0.64 0.87 0.66 0.95 0.66 0.94

Gravimetric Aerobic**

DMLmax

K*

Sum of

squared

R2

Stoichio-gasometric Anaerobic

DMLmax

K*

Sum of

squared

R2

Gravimetric Anaerobic

DMLmax

K*

Sum of

squared

R2

Day 21_23oC Day 21_37oC Day 220_23oC Day 220_37oC

𝑀𝑜𝑛𝑜𝑑: 𝐷𝑀𝐿 𝐷𝑀𝐿𝑚𝑎𝑥

𝑀𝑜𝑖𝑠𝑡𝑢𝑟𝑒 𝑐𝑜𝑛𝑡𝑒𝑛𝑡

𝐾 𝑀𝑜𝑖𝑠𝑡𝑢𝑟𝑒 𝑐𝑜𝑛𝑡𝑒𝑛𝑡 𝐷𝑟𝑜𝑜𝑝: 𝐷𝑀𝐿 𝐷𝑀𝐿𝑚𝑎𝑥 (

𝐾

𝑀𝑜𝑖𝑠𝑡𝑢𝑟𝑒 𝑐𝑜𝑛𝑡𝑒𝑛𝑡)

74

-4

-2

0

2

4

6

8

10

0 20 40 60 80

Dry

ma

tte

r lo

ss (

%)

Norminal storage moisture content (%)

21D_22C

-4

-2

0

2

4

6

8

10

0 20 40 60 80

Dry

ma

tte

r lo

ss (

%)


220D_22C

-4

-2

0

2

4

6

8

10

0 20 40 60 80D

ry m

att

er

loss

(%

)


21D_37C

-4

-2

0

2

4

6

8

10

0 20 40 60 80

Dry

ma

tte

r lo

ss (

%)


220D_37C

0

5

10

15

20

25

30

35

40

45

0 20 40 60 80

Dry

ma

tte

r lo

ss (

%)


21D_22C

0

5

10

15

20

25

30

35

40

45

0 20 40 60 80

Dry

ma

tte

r lo

ss (

%)


90D_22C

0

5

10

15

20

25

30

35

40

45

0 20 40 60 80

Dry

ma

tte

r lo

ss (

%)


21D_37C

0

5

10

15

20

25

30

35

40

45

0 20 40 60 80

Dry

ma

tte

r lo

ss (

%)


90D_37C

Spring Harvested, PA stover. AEROBIC STORAGE

Fall Harvested, IA stover. ANAEROBIC STORAGE

Figure 3.3: Comparing Monod (red squares) and Droop (green triangles) models to gravimetric dry matter loss data (blue circles)

75

3.3.3 pH of storage and control samples

pH of aerobic samples

Both IA and PA samples stored under aerobic conditions had alkaline pH values, which were

significantly higher than pH of Day 0 samples (p < 0.0001). The high pH (> 7) shown in Figure 4

indicates the absence of organic acids.

Figure3.4: pH values for different aerobic storage durations and across moisture levels for PA

stover (left) and IA stover (right)

Triangles = 22oC; circles =37

oC; orange = day zero; blue =day 21; brown= day 90. Samples

grouped together are not significantly different.

Typically, it could be expected that under aerobic condition, water soluble, readily available

sugars that are the primary initial substrate for aerobic and anaerobic microbes, would be

oxidized completely to carbon-dioxide. Sugar metabolism is neutral with respect to hydrogen

and hydroxyl ions, so should not result in a more alkaline pH. However, by the first sampling

period on Day 21 there are two reactions that would likely initiate within the first few days to

increase alkalinity: (1) the decomposition of protein compounds, leading to the formation of

ammonia or (2) conversion of organic acids or salts of organic acids to carbonates or

bicarbonates by alkali-forming bacteria (Ayers and Rupp, 1918). The first reaction, resulting in

ammonia alkalinity, is well recognized in composting systems and commonly drives up the pH

to 8.5 or 9.0 (Tiquia et al., 2002; Richard, 2003; Richard, 2004). For the second reaction, there

are at least three routes through which organic acids can be produced in wet aerobic storage.

5.5

6.0

6.5

7.0

7.5

8.0

8.5

9.0

9.5

5 15 25 35 45 55 65 75

pH

Storage moisture (%)

p = 0.004

p <0.0001

p <0.0001

p <0.0001

5.5

6.0

6.5

7.0

7.5

8.0

8.5

9.0

9.5

5 15 25 35 45 55 65 75

pH


p <0.0001

p <0.0001

p <0.0001

p <0.0001

76

First, organic acids can be produced under aerobic conditions through oxidative fermentation of

sugars (Hugh and Leifson, 1953). All microbes found on harvested crops or forage and which

form the natural microflora of silage are facultative aerobes or anaerobes except for Clostridium

species, which are generally obligate anaerobes and Acetobacter, which are obligate aerobes.

Acetobacter, a genus of acetic acid forming bacteria, are notable for converting ethanol to acetic

acid, however some species utilize sugars as substrate for acetic acid production. All lactic acid

bacteria can engage in oxidative fermentation to produce acids. Heterolactic species of the

genera Leuconostoc and Streptococcus will produce acetic acid instead of lactic or instead of

alcohol or other volatile acids whereas homolactic species like Lactobacillus plantarum will

produce lactates in high sugar environments and acetate when glucose level decreases (Condon,

1987). Other microbes with this oxidative fermentation capability and which can be potentially

found on harvested crops include Escherichia coli, Aerobacter aerogenes (Enterobacter

aerogenes) and some paracolon bacillus (Hugh and Leifson, 1953). Second, some Alkali-

forming bacteria (e.g. mostly belonging to the family Bacillaceae and Enterobacteriaceae) are

capable of sugar fermentation as well. The reaction of the acids produced from sugars and

ammonia produced from protein results in the formation of salts of organic acids, which are then

converted to alkali (Ayers and Rupp, 1918). Third, high moisture aerobic storage will behave

like a composting system, which goes through a series of pH changes due to decomposition and

varying metabolic activities as oxygen status changes within rapidly degrading particles (Richard

2004) and as successions of new microbial communities evolve that are better adapted to the new

conditions created (Battcock and Azam-Ali, 1998; Brinton, 1998; Richard, 2004). In most cases,

this starts with rapid oxygen consumption and anaerobic conditions within particles that

encourage acid formation, and then a decomposition of the acid by emergent fungi communities,

which result in alkali reactions. Based on observations of the anaerobic indicators at day 21 when

pH was first measured during aerobic storage, most aerobic samples had recently been or were

still partially in an anaerobic state. This weakens the argument for oxidative fermentation and

may favor the compost theory, where the high moisture forms pockets of anaerobic micro-

environments (Richard 2004).

77

Generally, IA samples had higher pH than the PA samples (p < 0.0001). The average pH for day

21 across the two storage temperatures was 8.3 for IA sample and 7.5 for PA samples. The

average pH for both stover types decreased significantly during aerobic storage to 7.9 (p = 0.028)

and 7.2 (p = 0.007) respectively by day 90. This decrease in pH could be due to the self-adjusting

pH observed in composting systems, where pH eventually levels off to a more neutral pH over

time (Brinton, 1998). The general trend suggests higher moistures had higher pH. With respect to

the two temperature conditions, the pH of samples stored at 23oC were significantly higher than

those stored at 37oC (p = 0.028 for IA stover and < 0.0001 for PA stover).

pH of anaerobic samples

The pH values on day 0 were 6.01 ± 0.08 and 6.07 ± 0.34 for IA and PA stover respectively, and

dropped significantly (p < 0.0001) to 4.01 ± 0.68 and 4.60 ± 0.36 by day 21 (Figure 5). Average

pH values after 220 days of anaerobic storage were 4.40 ± 0.31 for IA stover and 4.40 ± 0.58 for

PA stover. At day 220, there was no significant difference in the pH of IA and PA stover (p =

0.770) in contrast to day 0 and 21 where the pH of IA stovers were higher (p < 0.0001). The

slightly lower initial pH of the PA stover at Day 0 compared to IA stover pH at Day 0 could be

due to the more moist nature of the PA feedstock prior to adjusting moisture for the storage

experiments, ~30% moisture compared to ~7% in the IA stover. At 30% moisture the PA stover

could have been experiencing low levels of acidogenic fermentations, while the very dry

conditions of the IA stover could have resulted in loss of any organic acids previously formed. In

addition to having a slightly lower pH, PA stover showed a decrease in pH with increasing

moisture level on Day 0 after moisture was adjusted for storage. This trend was not observed in

the IA stover, which had similar pH across moisture for Day 0 samples, with exception of 65%

moisture which was significantly lower (p < 0.0001). Also for IA stover, pH values at the three

storage durations (day 21, 90 and 220) were not significantly different (p = 0.210), whereas in

PA stover, pH increased at day 220 above day 21 (p < 0.0001). The increase in pH of PA stover

after day 21 is possibly due to a compromise in storage integrity; although observing this

increase across-the-board makes this doubtful as the glass jars and metal lids appeared to be

sealed well, and separate containers were used for each storage duration so the long term samples

were not opened at the earlier sampling dates. This increase in pH for the PA stover did not

78

however affect the lactic acid concentration of the day 220 samples stored at 23oC, although at

37oC the lactic acid concentrations were generally lower than observed in the IA stover. For

each storage temperature (23oC and 37

oC), the pH at day 21 was not significantly different from

the pH at day 220. This was true across the full range of moisture levels for both IA and PA

stovers (p: IA = 0.916 and 0.456; PA = 0.384 and 0.300 for day 21 and 220 respectively).

In general the impact of storage moisture on pH was not striking. For PA stover, there was no

difference in pH across moisture at day 220, while at day 21, the only difference in pH was at

25% moisture, which was slightly higher. This was true even though there was evidence of

Clostridium fermentation in some of the high moisture samples, as seen from levels of butyric

acid above 0.5% of dry matter, which normally results from lactic acid degradation and causes

an increase in pH (McDonald et al. 1991; Jones et al., 2004). IA stover, while it showed no

significant difference in pH for 45% – 65% moisture levels for days 21, 90 and 220, had a higher

pH at moisture levels outside this range (15% - 35% and 75%). These more extreme moisture

samples of IA stover were not significantly different from each other except at Days 21 and 90,

when the pH at 15% and 25% was higher.

The pH values in this study are generally comparable to values for whole corn silage (grain and

stover) whose pH range is typically 3.5 – 4.5 when ensiled at 60 to 75% moisture (Jones et al.,

2004), and are somewhat lower than some prior observations for corn stover silage (Richard et

al., 2001; Shinners et al., 2007) indicating that the silage process in these experiments was

robust.

79

Figure 3.5: pH values for different anaerobic storage durations and across moisture levels for PA

stover (left) and IA stover (right)

Triangles = 22oC; circles =37

oC; orange = day zero; blue =day 21; brown= day 90; green = day

220. Samples grouped together are not significantly different.

3.3.4 Organic acid profiles during anaerobic storage

The organic acids identified in the ensiled storage samples included lactic, acetic, butyric,

isobutyric and propionic acids (see Figure 6 and Table 3). Low levels of tartaric and malic acids

were also identified in a number of samples, but were difficult to quantify due to their short

retention times near the elution of the system void volume and hence were not used in the current

analysis. Generally, total acids increased with moisture and duration (p < 0.0001) and were not

significantly different at the two storage temperatures of 23oC and 37

oC (p = 0.454). Organic

acids were virtually absent in the unensiled (Day 0) control samples of fall harvested (IA) stover.

As can be observed from Figure 6, these IA unensiled control samples had only two acid types;

lactic and traces of isobutyric acid, with means totaling ~0.1% (all acid results are reported on a

stover dry matter basis). The unensiled (Day 0) control samples of PA stover had some lactic and

acetic acid, with means totaling ~0.3%. Organic acids at 15% moisture for both Day 21 and Day

220 were similar to those of the control samples (Day 0). Lactic and acetic acids were present in

all wet storage samples with the exception of some Day 220 samples stored at 75% moisture,

where lactic acid is absent in both stover types (IA or PA). This was likely due to secondary

3.0

3.5

4.0

4.5

5.0

5.5

6.0

6.5

7.0

7.5

5 15 25 35 45 55 65 75

pH


P<0.0001

P=0.340

P<0.0001

3.0

3.5

4.0

4.5

5.0

5.5

6.0

6.5

7.0

7.5

5 15 25 35 45 55 65 75

pH


P<0.0001

P<0.0001

P<0.0001

80

fermentations by clostridia, common in high moisture silages (McDonald et al. 1991; Jones et al.,

2004). Butyric and propionic acids were present only in high moisture silage samples and

increased with storage duration. Even in these high moisture conditions the propionic acids

concentration was less than 1% for all samples. Traces of isobutyric were present at all moisture

levels in IA stover and were significantly higher (p < 0.0001) at higher moisture levels,

exceeding 2% in some of the Day 220 75% moisture samples. Figure 6 shows the organic acid

distribution at the different storage durations and moisture levels. At moisture levels of 25% –

45%, there were generally no significant differences in the amounts of the different acids among

stover types and across temperature (See Table 3). At moisture levels outside the 25% -45%

range, there were a few observations in which concentrations of organic acids were significantly

different across temperature. For the high moisture samples (55 -75%), those stored at 37oC had

relatively more volatile fatty acids (acetic, isobutyric and butyric) and less lactic acid. At 37oC,

acetic and butyric acid metabolisms are favored above lactic acid metabolism because most

lactic acid bacteria have optimum temperature of 18 – 22oC. Analysis of variance showed the

mean values of the various organic acids at the different moisture levels were all not equal. Table

3 shows the effect of the various storage factors on organic acid content. All factors had

significant effect on lactic acid for both IA and PA stovers, but there was no significant

interaction between temperature and moisture or duration for IA stover and no interaction among

the three factors for PA stover. The difference in lactic acid across moisture was mainly at the

extreme moisture levels, and the difference across duration was observed mainly at 37oC. For

acetic acid, although all storage factors had a significant effect on the amount of acetic acid in

PA stover, only moisture and duration had an effect in the IA stover. There was a distinctly non-

linear relationship of lactic acid concentrations with moisture, while this relationship for acetic

acid was more linear. See Appendix B, Figure B5 for an illustration of the graphical relationship

between these two acids and moisture.

Total amount of organic acids in samples stored at 55% to 65% moisture (i.e. the typical

moisture levels for conventional silage) were somewhat lower (~2.5% to 9.1% on a dry weight

basis) than the amounts in whole-crop corn silage, which are typically around 5 -10% (Kung,

2008), but within range found in other corn stover silage studies. In previous studies on naturally

81

ensiled corn stover silage acetic acid was dominant, up to 2.5% - 3.0% (Richard et al., 2001;

Ren, 2006). This was not the case in current study, where lactic acid was generally dominant; up

to ~4.9% for IA stover and up to ~3.5% for PA stover. Lactic acid concentrations, at moisture

levels of 25% – 45% for PA samples ranged from 57 – 94% of total acids. For higher moisture

PA stover at longer duration (day 220), the lactic acid concentrations were generally less than

50% of the total acid. For the IA stover, lactic acid concentrations after storage were generally

greater than 50% and up to 85% of total acid, which was consistent across storage durations for

moisture levels of 25 -65%. At 75% moisture, acetic and butyric acids were equally dominant in

IA stover at day 220, 37oC., while for PA stover at day 220 acetic acid was dominant at 37

oC

and butyric acid was dominant at 23oC storage temperature.

The inhibitory effect of these acids on microbial degradation during storage and on downstream

biofuel fermentation depends on a number of factors such as substrate concentration,

fermentation pH and temperature. These factors determine both the overall acid concentration

and the relative amount of undissociated acids. The inhibitory effect of organic acids also

depends on the composition of the fermentation broth including factors like concentrations of

metal ions, sugars, amounts and types of nutrients, and inoculum size or initial microbial

population (Lund and Eklund, 2000; Palmqvist and Hahn-Hagerdal, 2000; Stenberg et al., 2000).

Microbial intracellular pH as well as nutrient up-take rate is also crucial (Zaldivar and Ingram,

1999; Thomas et al., 2002; Torija et al., 2003)

Assuming a 30% solids loading rate, the concentration of total acids for the maximum values

observed in this study will be just above 2%. Assuming lactic acid dominates at 50% of total

acids, at a fermentation pH of 4.5 only ~ 18% of this lactic acid will be undissociated and

potentially inhibitory. This is equivalent to a concentration of 0.18%, and when considering the

higher pKa of the other acids would sum to a much lower concentration than the 0.8%

intracellular accumulation noted by Knauf and Kraus (2006) to possibly cause cell death in yeast.

Thus it can be assumed then that the organic acids concentrations in silage are not likely to

inhibit or constitute any significant inhibition in downstream processes.

82

Figure 3.6: Organic acid profile of IA and PA stovers at various anaerobic storage conditions.

0

1

2

3

4

5

6

7

8

0 0 0 0 0 0

21

21

21

21

21

21

22

0

22

0

22

0

22

0

22

0

22

0 0 0 0 0 0 0

21

21

21

21

21

21

22

0

22

0

22

0

22

0

22

0

22

0

25 35 45 55 65 75 25 35 45 55 65 75 25 35 45 55 65 75 25 35 45 55 65 75 25 35 45 55 65 75 25 35 45 55 65 75

Lactic Acetic Propionic Isobutyric Butyric

Spring Harvested, PA Stover

0

1

2

3

4

5

6

7

8

0 0 0 0 0 0

21

21

21

21

21

21

22

0

22

0

22

0

22

0

22

0

22

0 0 0 0 0 0 0

21

21

21

21

21

21

22

0

22

0

22

0

22

0

22

0

22

0

25 35 45 55 65 75 25 35 45 55 65 75 25 35 45 55 65 75 25 35 45 55 65 75 25 35 45 55 65 75 25 35 45 55 65 75

Lactic Acetic Propionic Isobutyric Butyric

37C

Fall Harvested, IA Stover 23C

83

23oC 37oC 23oC 37oC 23oC 37oC 23oC 37oC 23oC 37oC

IA-25 0.27 ± 0.23 0.11 ± 0.19 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.06 + 0.06 0.03 ± 0.05 0.00 ± 0.00 0.00 ± 0.00

PA-25 0.75 ± 0.15 1.15 ± 0.36 0.26 ± 0.06 0.25 ± 0.09 0.00 ± 0.00 0.00 ± 0.00 0.00 + 0.00 0.06 ± 0.11 0.00 ± 0.00 0.00 ± 0.00

IA-35 1.76 ± 0.46 0.7 ± 0.66 0.74 ± 0.63 0.23 ± 0.2 0.00 ± 0.00 0.00 ± 0.00 0.13 + 0.08 0.03 ± 0.06 0.00 ± 0.00 0.00 ± 0.00

PA-35 2.05 ± 0.4 1.75 ± 0.43 0.42 ± 0.10 0.36 ± 0.06 0.00 ± 0.00 0.00 ± 0.00 0.00 + 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00

IA-45 2.03 ± 0.65 2 ± 0.67 0.46 ± 0.18 0.53 ± 0.34 0.00 ± 0.00 0.00 ± 0.00 0.03 + 0.05 0.13 ± 0.13 0.00 ± 0.00 0.00 ± 0.00

PA-45 2.42 ± 0.24 1.78 ± 0.56 0.54 ± 0.07 0.37 ± 0.16 0.00 ± 0.00 0.00 ± 0.00 0.00 + 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00

IA-55 2.34 ± 0.37 1.36 ± 1.29 0.61 ± 0.13 0.34 ± 0.3 0.00 ± 0.00 0.00 ± 0.00 0.06 + 0.05 0.04 ± 0.06 0.00 ± 0.00 0.00 ± 0.00

PA-55 2.16 ± 0.76 1.41 ± 0.13 0.60 ± 0.33 1.56 ± 0.17 0.00 ± 0.00 0.00 ± 0.00 0.00 + 0.00 1.9 ± 0.21 0.00 ± 0.00 0.00 ± 0.00

IA-65 2.6 ± 0.07* 0.9 ± 1.56 0.79 ± 0.04 0.44 ± 0.76 0.00 ± 0.00 0.00 ± 0.00 0.05 + 0.09 0.05 ± 0.08 0.00 ± 0.00 0.00 ± 0.00

PA-65 2.77 ± 0.47* 1.6 ± 0.36 0.69 ± 0.12 0.67 ± 0.18 0.00 ± 0.00 0.00 ± 0.00 0.00 + 0.00 0.82 ± 0.29 0.00 ± 0.00 0.00 ± 0.00

IA-75 2.2 ± 0.38 2.03 ± 1.78 0.83 ± 0.12 0.61 ± 0.57 0.00 ± 0.00 0.00 ± 0.00 1.03 + 0.14 0.00 ± 0.00 0.10 ± 0.18 0.00 ± 0.00

PA-75 2.33 ± 0.66 0.00 ± 0.00 0.76 ± 0.19 1.6 ± 0.05 0.00 ± 0.00 0.30 ± 0.51 0.25 + 0.08 3.22 ± 0.83 0.09 ± 0.16 1.03 ± 0.72

IA-25 0.8 ± 0.27 0.26 ± 0.46 0.37 ± 0.07 0.14 ± 0.24 0.00 ± 0.00 0.00 ± 0.00 0.05 + 0.09 0.04 ± 0.07 0.00 ± 0.00 0.00 ± 0.00

PA-25 1.14 ± 0.45 0.99 ± 0.23 0.21 ± 0.19 0.25 ± 0.16 0.00 ± 0.00 0.00 ± 0.00 0.11 + 0.1 0.13 ± 0.04 0.00 ± 0.00 0.00 ± 0.00

IA-35 2.52 ± 0.38 1.94 ± 0.13 0.91 ± 0.37 0.63 ± 0.02 0.00 ± 0.00 0.00 ± 0.00 0.11 + 0.01 0.2 ± 0.02 0.00 ± 0.00 0.00 ± 0.00

PA-35 2.57 ± 0.26 1.68 ± 0.32 0.58 ± 0.13 0.77 ± 0.12 0.00 ± 0.00 0.00 ± 0.00 0.16 + 0.14 0.18 ± 0.03 0.00 ± 0.00 0.00 ± 0.00

IA-45 2.79 ± 0.44 2.9 ± 0.16 0.86 ± 0.15 0.75 ± 0.06 0.00 ± 0.00 0.00 ± 0.00 0.11 + 0.01 0.27 ± 0.01 0.00 ± 0.00 0.00 ± 0.00

PA-45 2.59 ± 0.09 1.31 ± 1.13 0.3 ± 0.13 0.17 ± 0.15 0.00 ± 0.00 0.00 ± 0.00 0.11 + 0.1 0.11 ± 0.1 0.00 ± 0.00 0.00 ± 0.00

IA-55 3.2 ± 0.47* 2.95 ± 0.4 1.35 ± 0.42 1.14 ± 0.19 0.00 ± 0.00 0.00 ± 0.00 0.08 + 0.07 0.34 ± 0.22 0.00 ± 0.00 0.00 ± 0.00

PA-55 3.17 ± 0.48* 1.36 ± 0.1 1.78 ± 0.24 2.2 ± 0.32 0.00 ± 0.00 0.00 ± 0.00 0.9 + 0.62 2.16 ± 0.4 0.00 ± 0.00 0.1 ± 0.17

IA-65 2.9 ± 0.57* 3.03 ± 1.76 1.02 ± 0.89 1.24 ± 0.21 0.00 ± 0.00 0.00 ± 0.00 0.73 + 0.1 0.16 ± 0.27 0.00 ± 0.00 0.00 ± 0.00

PA-65 2.25 ± 0.11 0.21 ± 0.37 1.45 ± 0.46 2.51 ± 1.45 0.00 ± 0.00 0.36 ± 0.09 1.46 + 0.48 1.21 ± 1.19 0.49 ± 0.13 1.01 ± 1.01

IA-75 2.9 ± 0.8* 0.00 ± 0.00 1.86 ± 0.2 2.74 ± 0.73 0.10 ± 0.18 0.00 ± 0.00 1.19 + 0.06 1.28 ± 1.06 0.00 ± 0.00 2.57 ± 0.58

PA-75 0.00 ± 0.00 0.00 ± 0.00 1.9 ± 0.07 2.73 ± 0.59 0.59 ± 1.02 0.34 ± 0.07 0.54 + 0.07 1.81 ± 0.02 3.50 ± 0.31 1.18 ± 0.35Across

samples<0.0001 <0.0001 <0.0001 <0.0001 0.499 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

By moisture

(%)<0.0001 0.001 <0.0001 <0.0001 0.261 0.002 <0.0001 <0.0001 <0.0001 <0.0001

By type 0.242 0.064 0.820 0.016 0.419 0.003 0.906 <0.0001 0.013 0.607Moisture x

type0.017 0.046 0.634 0.103 0.654 0.002 0.003 <0.0001 0.001 0.681

By duration 0.081 0.511 <0.0001 <0.0001 0.236 0.293 <0.0001 0.466 0.021 0.002Moisture x

duration0.017 0.305 <0.0001 0.001 0.225 0.500 <0.0001 0.994 0.003 <0.0001

⁰ P-values by acid names are values when comparing storage acids by temperature.

*These samples are not significantly differnt from each other even though the color coding may indicate otherwise.

Sample with same color within each column are not significantly different from each other. Color gradient: intensity proportional to amount of acid.

Within acid types, in cases where there are significant differences across temperature, the sample with the relatively high acid content is distinquished with red font.

P-values in bold imply not significantly different

21

220

P-values

Organic acids (% dry matter)

Duration

(Days)

Moisture

(%)

Lactic (P < 0.0001) Acetic (P < 0.0001) Propionic (P = 0.483) Isobutyric (P < 0.0001) Butyric (P < 0.0001)

Table 3.3: Differences in organic acids in anaerobic storage across stover types, moisture levels, durations and temperatures

84

3.3.5 Corn stover composition before and after storage

Compositional analysis was completed for the IA stover only, with results from before and after

anaerobic wet storage presented in Table 4. Eleven components were analyzed with a mass closure

of 95 - 101%. Mannan was absent in all samples, so is not shown in Table 4. Although some

variations exist across moisture at Day 0, the percentages of each structural and non-structural

component of the feedstock were not significantly different after the initial moisture adjustment. For

storage samples (day 21 and day 220), significant compositional changes across moisture were

observed, especially at 23oC and 220 days of storage (see Table 4). There were no significant

differences in the percentage compositions of glucan, lignin, acetyl and ethanol extractives across

moisture for each storage duration and at the two storage temperatures.

From Table 5, it can be observed generally that the amounts of structural sugars decreased

significantly during storage with the exception of glucan. This was as expected since hemicelluloses

(a structural heteropolymer composed mainly of xylan as backbone and arabinan, galactan, glucan

side chains among others) are more reactive to acids than cellulose (a glucan polymer).

Hemicelluloses’ high reactivity to acid is partially due to its branched and amorphous nature in

contrast to cellulose, which is unbranched and substantially crystalline. Hemicellulose can be

depolymerized or degraded by hydrolysis of glycosidic linkages and acetyl groups. As much as 70%

of xylan is acetylated in hardwood (Timell, 1967), with the acetyl group constituting from 3 to17%

of the the hemicelluloses (Sun et al., 2004). In IA stover, on average, acetyl constituted 3.8 to 5% of

the stover composition and between 12 -15% of hemicelluloses, if glucose (a minor component of

hemicellulose) is ignored. Acetyl degradation of up to ~55% was observed during wet storage, and

only 7 samples out of 48 showed an increase rather than a decrease in acetyl content. There was

significant evidence of a temperature effect on the degradation of acetyl (p < 0.0001) but no

evidence of the effect of storage duration (p = 0.062) nor moisture (p = 0.07). There was also no

interaction between temperature, duration and moisture. Generally, acetyl degradation was ~7% at

23oC and averaged 37% at 37

oC.

While storage temperature was important for acetyl degradation, the reverse was the case for xylan.

There was no significant evidence of a temperature effect on xylan degradation (p = 0.518) but there

85

was evidence that duration and moisture had influence on xylan degradation (p < 0.0001 in both

cases). Although both duration and moisture had significant impact on xylan degradation, there was

no interaction between these two factors, but there was a significant interaction between duration

and temperature (p < 0.0001) as well as moisture and temperature (p = 0.013). These statistically

significant interactions indicate that the effects of both duration and moisture vary across the two

temperatures.

In contrast to the lumped analysis described above, observations within specific storage groups

(Day 21 at 23oC, Day 21 at 37

oC, Day 220 at 23

oC and Day 220 at 37

oC) indicated that the effect of

moisture was inconsistent and was not always significant. For instance at Day 21, 23oC the change

in xylan was not significantly different at all moisture levels except for 25% moisture, which was

slightly higher than the change at 45% and 75% moisture. Similarly at Day 220, 23oC the change in

xylan at all moisture levels was not significantly different except at 25% moisture, where xylan

degradation was slightly higher than the change at the 35%, 65% and 75% moisture levels.. Storage

moisture content did not result in significant differences in xylan degradation at Day 21, 37oC but at

Day 220, 37oC, the change in xylan at 45% was lower than the change at 25% and 55%. At Day 21,

xylan degradation increased with temperature, whereas at Day 220 it decreased with temperature.

Across all moisture levels and temperatures, xylan degradation at Day 21 averaged ~18% but by

Day 220 only averaged about 5%.

If degradation of hemicellulose is considered as a whole (xylan + arabinan + galactan + acetyl),

temperature, moisture and duration were each observed to have a significant effect (p = 0.011, 0.001

and < 0.0001 respectively). Although there was not evidence of a 3-way interaction among

temperature, moisture and duration, there was a significant 2-way interaction between temperature

and duration. Hemicellulose degradation ranged from 6% to 30%, with higher values obtained at

37oC_21 days. At day 21, hemicellulose degradation increased with temperature (~16% at 23

oC and

~ 25% at 37oC) but showed no significant difference across temperature at day 220 (~ 10% loss at

both temperatures). A regression analysis showed that temperature accounted for less than 3% of

the variation in hemicelluloses, while duration accounted for 43% and both combined accounted for

47%. Although hemicellulose is the most thermally reactive structural component, wood

hemicelluloses have good physical, structural and chemical stability up to 100oC (Fengel and

Wegener, 1984) and are quite similar to herbaceous hemicelluloses. Other factors like moisture can

86

influence the thermal response of structural components, but observations are also usually at above 100oC

(Fengel and Wegener, 1984). Since wet storage is at a much lower temperature, it seems unlikely to expect a

singular impact of temperature in the sense described in this literature. The higher degradation at 37oC is

more likely due to the increased rate of reaction of acids produced during storage, which would result

from the temperature increase. Previous studies ((McDonald et al. 1991; Muck, 1996) have always

attributed the change in hemicelluloses content to low pH and acid hydrolysis. Moreover, the

behavior of hemicelluloses seems to be strongly influenced by its major component, xylan, for

which a temperature effect can only be observed with duration and moisture.

Hemicellulose degradation by acids, whether during storage or thermochemical pretreatment, can

result in shorter chain polymers, oligomers, corresponding mono and disaccharides, sugar acids,

acetic acid and phenolic acids. At low temperatures, the acetyl group is the most susceptible to

degradation, resulting in acetic acid formation. The non-polymeric sugars and acid components

contribute to the soluble fraction of the stover, as indicated by the increase in the water extractive

component after storage. Water extractive components are non-structural compounds that can

dissolve in or be extracted by water. Generally, there was a significant increase in water extractives,

as percentage of dry matter, from ~5% on day 0 to ~ 10% after storage (p < 0.0001). Water

extractives were significantly higher at day 21 compared to day 220, with a mean increase of

~105% and 62% respectively on mass basis when compared to the extractive amounts at Day 0 (p =

0.009). The decrease in water extractives at day 220 could be due to consumption of sugars by

surviving microbes or chemical degradation. There were no significant temperature effects on the

amount of water extractives. With respect to moisture, there were no significant differences in the

amounts of water extractives except for the 25% moisture samples, where the amounts were smaller

than at 55%. Overall, the change in hemicelluloses has a negative correlation with the change in

water extractives, although not strongly (r = - 0.476, p = 0.001). However, looking at the individual

constituents of hemicelluloses across all treatments, only the change in xylan showed some

correlation (again negative) with the change in water extractives (r = - 0.466, p = 0.001). This may

be due to the relatively higher mass of xylan compared to other components. Alternatively, it could

suggest that acetyl [acetic acid] removal from xylan is a more important contributer to the total

amount of extractives than degraded sugars from hemicellulose. The amount of acetyl as a

percentage dry matter was higher than the amounts of galactan and arabinan, both of which are also

87

sugar components of hemicellulose. In contrast to this lumped analysis, when xylan’s association

with water extractives was considered within individual storage groups (day 21 at 23oC, day 21 at

37oC, day 220 at 23

oC and day 220 at 37

oC), no correlation was found. This was because each

group had a narrow range of xylan degradation, with virtually no significant change across

moisture. However, across groups the means were significantly different (day 21 at 37oC > day 21

at 23oC > 220 at 23

oC >220 at 37

oC), and had a wide change in extractives.

The glucan composition of Day 0 samples was not significantly different from that of Day 220, but

was lower than the glucan composition at Day 21. The percentage of glucan increased up to 2% at

day 21 relative to the initial glucan content, translating into the percentage mass changes as shown

in Table 5. A mass increase in glucan content of corn stover after wet storage seems unusual, but

similar result was reported by Richard et al. (2001) while Ren (2006) observed there was no

degradation of cellulose in plain stover silage. Other forage silage studies showed both mass

increase (Danley and Vetter, 1973) and mass decrease (Yahaya et al., 2002) in cellulose content

after ensilage. This increase in glucan concentration may be an artifact resulting from substantial

losses in other components, so that the total dry matter basis from which the concentration is

calculated is reduced. This result may also be due to the heterogeneous nature of the feedstock,

which introduces variability in moisture measurements and thus dry mass calculations. If this result

is real, a decrease in glucan concentration on Day 220 relative to Day 21 could result from partial

degradation of hemicelluloses, in which the glucan component is degraded and removed with the

water extractives. This is, however contentious since hemicellulose degradation was apparently less

at Day 220 compared to Day 21. The change in glucan content was significantly affected by

duration (p <0.0001) and moisture (p =0.002) but not temperature (p =0.100). In this lumped

analysis, although effect of moisture is significant, the difference was only for samples at 25%

moisture on Day 21 (23oC), which had significantly higher glucan than samples at 45% moisture on

Day 220 (23oC). However, within each storage group there was no significant change in the mass of

glucan across moisture for all storage durations.

With respect to other structural sugars, both arabinan and galactan degradation were significant (p <

0.0001 for each) and ranged from ~9% to 38% and ~ 12% to 50% of the initial arabinan and

galactan content respectively. Temperature and moisture effects on arabinan composition were not

88

significant, but the effect of storage duration was significant. For galactan, the effect of moisture

was also not significant (p = 0.997) but temperature and duration were both significant (p < 0.0001)

with evidence of interaction among temperature and duration (p < 0.0001). Across all storage

moisture conditions, average degradation of arabinan at Day 21 at 23oC = Day 21 at 37

oC > Day

220 at 23oC = Day 220 at 37

oC. Degradation of galactan at Day 21 at 23

oC = Day 21 at 23

oC > Day

220 at 23oC > Day 220 at 37

oC. Lignin content, as in the case of glucan, showed significant changes

only at 21 days of storage, with a mean decrease of up to 4.1% relative to initial lignin

concentration. Across all storage conditions, temperature had no significant effect on lignin

degradation (p = 0.407) while the effect of moisture was only observed for the 25% moisture

samples, which were lower than for 65% moisture (p = 0.021). Non-structural inorganics, most

likely soil and/or soil minerals, appeared to decrease at 21 days while structural inorganics [ash]

showed no significant difference for all storage groups even though changes across groups were

varied.

Overall dry matter loss was lower in wet storage than in dry storage, indicating that wet storage is

usually a more effective storage option if biomass preservation is an important goal. For the aerobic

25% moisture condition used as an analogue for dry storage, dry matter loss was between 2.66%

and 3.56%, and this loss was much higher for higher moisture contents. Even this low level for

aerobic storage at 25% moisture is high compared to many anaerobic wet storage conditions, where

losses are generally well under 4% even at very high moisture levels. And there is always risk of

compromise in dry storage, especially in open or semi-sheltered systems, where infiltration of water

even to the 35% moisture level would dramatically increase the dry matter losses (Figure 1).

Anaerobic wet storage can also risk significant degradation if silage containment is compromised

and air infiltration occurs, but in properly compacted silage such infiltration will typically not

penetrate deeply, and dry matter losses and compositional changes are not as dramatic.

89

Table 3.4: Composition (% dry basis) of IA stover before and after anaerobic storage

Duration

0

21 36.19 ± 1.42 33.91 ± 1.01 35.72 ± 1.52 34.99 ± 0.50 34.20 ± 0.29 33.97 ± 1.42 34.33 ± 0.57 36.79 ± 0.21 35.78 ± 1.55 36.83 ± 1.36 34.50 ± 1.24 36.35 ± 1.97 0.469 0.151

220 35.67 ± 0.67 33.98 ± 0.89 32.66 ± 3.29 35.18 ± 1.26 33.34 ± 0.69 33.29 ± 1.65 32.73 ± 0.30 35.10 ± 0.42 32.96 ± 0.03 34.39 ± 0.37 31.92 ± 0.59 35.06 ± 1.39 0.282 0.518

0

21 19.90 ± 0.65 17.09 ± 0.54 19.62 ± 0.64 17.39 ± 1.1 18.40 ± 0.53 17.16 ± 0.57 18.93 ± 0.27 18.37 ± 0.05 19.96 ± 0.75 19.04 ± 0.64 18.39 ± 0.09 18.61 ± 1.60 0.075 0.259

220 23.10 ± 0.43 23.14 ± 0.15 19.64 ± 0.62 22.92 ± 0.88 21.66 ± 1.43 21.16 ± 0.36 20.31 ± 0.70 22.85 ± 0.76 20.98 ± 0.16 23.02 ± 0.63 19.89 ± 0.39 22.99 ± 1.17 0.024 0.198

0

21 1.00 ± 0.02 0.81 ± 0.18 0.88 ± 0.05 0.67 ± 0.02 0.82 ± 0.04 0.75 ± 0.04 0.87 ± 0.01 0.81 ± 0.01 0.86 ± 0.06 0.88 ± 0.07 0.76 ± 0.00 0.75 ± 0.04 0.012 0.288

220 1.19 ± 0.06 0.91 ± 0.04 0.97 ± 0.06 0.85 ± 0.00 1.07 ± 0.01 0.82 ± 0.06 1.07 ± 0.01 0.91 ± 0.02 1.13 ± 0.09 0.90 ± 0.04 1.02 ± 0.00 1.00 ± 0.02 0.052 0.059

0

21 2.91 ± 0.14 2.64 ± 0.36 2.65 ± 0.13 2.38 ± 0.19 2.54 ± 0.01 2.49 ± 0.04 2.68 ± 0.12 2.78 ± 0.25 2.60 ± 0.08 2.67 ± 0.2 2.52 ± 0.08 2.75 ± 0.11 0.071 0.480

220 3.38 ± 0.14 3.20 ± 0.27 2.80 ± 0.04 3.05 ± 0.13 2.97 ± 0.01 2.99 ± 0.18 3.02 ± 0.06 3.13 ± 0.12 3.09 ± 0.16 2.99 ± 0.14 2.87 ± 0.01 3.60 ± 0.02 0.008 0.028

0

21 14.93 ± 0.58 15.25 ± 0.23 14.80 ± 0.02 14.94 ± 0.07 14.62 ± 1.02 14.87 ± 0.23 15.20 ± 0.62 15.20 ± 0.36 15.55 ± 0.24 15.23 ± 0.37 15.47 ± 0.16 14.97 ± 0.47 0.530 0.733

220 15.31 ± 0.29 15.59 ± 0.06 15.29 ± 0.23 15.63 ± 0.13 16.76 ± 0.06 17.63 ± 0.3 15.01 ± 0.19 16.26 ± 1.67 16.31 ± 1.78 15.18 ± 0.17 15.76 ± 0.24 16.84 ± 0.19 0.287 0.084

0

21 3.49 ± 1.05 3.02 ± 0.06 4.42 ± 0.57 2.62 ± 0.57 3.82 ± 1.73 3.12 ± 0.09 4.18 ± 1.7 2.98 ± 0.11 3.39 ± 1.00 3.22 ± 0.09 5.36 ± 0.14 3.10 ± 0.10 0.604 0.338

220 4.37 ± 0.49 2.44 ± 0.03 4.33 ± 0.02 2.51 ± 0.16 4.16 ± 0.65 2.59 ± 0.07 3.72 ± 0.04 2.49 ± 0.06 3.66 ± 0.16 2.52 ± 0.17 3.68 ± 0.04 2.64 ± 0.24 0.218 0.781

0

21 9.40 ± 0.02 10.18 ± 1.2 12.18 ± 0.53 11.21 ± 0.16 11.86 ± 0.88 10.8 ± 0.32 12.23 ± 1.15 10.37 ± 0.15 10.97 ± 0.79 10.12 ± 0.71 9.13 ± 0.50 8.83 ± 0.12 0.016 0.070

220 5.47 ± 1.53 8.22 ± 0.22 9.85 ± 0.21 10.79 ± 0.39 10.27 ± 0.18 8.45 ± 2.43 8.80 ± 0.14 9.66 ± 1.70 6.99 ± 0.18 10.89 ± 0.14 7.09 ± 0.83 5.03 ± 1.75 0.003 0.043

0

21 2.67 ± 1.17 2.93 ± 0.08 2.55 ± 0.78 2.95 ± 0.09 2.84 ± 0.87 3.02 ± 0.05 2.66 ± 1.01 2.83 ± 0.01 2.46 ± 0.37 2.54 ± 0.43 2.56 ± 0.90 2.97 ± 0.18 0.998 0.302

220 3.92 ± 0.31 3.16 ± 0.12 3.75 ± 0.13 2.70 ± 0.06 2.17 ± 0.01 1.51 ± 0.82 3.71 ± 0.11 2.37 ± 0.73 3.65 ± 3.48 3.14 ± 0.04 3.74 ± 0.13 2.81 ± 0.18 0.074 0.074

0

21 2.21 ± 0.08 1.33 ± 0.95 1.34 ± 0.74 2.49 ± 0.14 2.11 ± 0.74 2.61 ± 0.08 1.44 ± 0.83 2.00 ± 0.17 2.15 ± 0.86 2.08 ± 0.71 2.36 ± 0.82 2.87 ± 0.13 0.639 0.154

220 2.17 ± 0.25 3.85 ± 0.02 2.73 ± 0.22 2.61 ± 0.23 2.24 ± 0.32 3.48 ± 0.37 2.57 ± 0.08 2.27 ± 0.24 3.40 ± 0.18 2.70 ± 0.28 3.12 ± 0.33 3.43 ± 1.16 0.013 0.127

0

21 4.54 ± 0.04 5.28 ± 0.44 4.07 ± 0.27 4.68 ± 0.03 4.62 ± 0.36 4.57 ± 0.17 4.94 ± 0.62 4.30 ± 0.16 4.97 ± 0.36 5.66 ± 1.05 5.53 ± 0.08 5.60 ± 0.11 0.059 0.115

220 4.20 ± 0.13 4.52 ± 0.10 4.43 ± 0.01 3.97 ± 0.31 4.71 ± 0.34 4.06 ± 0.24 4.69 ± 0.14 4.35 ± 0.06 5.12 ± 0.19 4.88 ± 0.14 6.06 ± 0.27 6.39 ± 0.96 0.001 0.010

3.82 ± 1.30 0.525

Values in red are for s torage at 37oC. P-va lues are for comparison across moisture

Structural inorganics

2.50 ± 0.59 2.23 ± 1.74 2.58 ± 0.65 2.62 ± 0.73 2.47 ± 0.95

2.20 ± 1.05 0.719

Non Structural

inorganics

4.51 ± 0.62 5.12 ± 2.04 5.10 ± 0.75 4.17 ± 0.75 5.22 ± 0.90 5.84 ± 0.85 0.531

Ethanol extractives

2.92 ± 0.22 2.58 ± 0.62 2.73 ± 0.92 2.90 ± 0.21 2.35 ± 0.83

4.20 ± 1.56 0.817

Water extractives

6.09 ± 2.09 4.96 ± 2.08 4.32 ± 4.12 7.32 ± 1.99 3.73 ± 2.84 4.40 ± 2.88 0.584

Acetyl

4.28 ± 1.18 5.00± 1.39 4.62 ± 1.18 3.81 ± 0.94 4.22 ± 0.61

3.81 ± 0.06 0.995

Lignin

15.02 ± 0.23 15.55 ± 0.57 15.96 ± 0.92 14.77 ± 0.20 15.90 ± 1.77 15.50 ± 1.25 0.574

Arabinan

3.81 ± 0.11 3.9 ± 0.27 3.80 ± 0.47 3.85 ± 0.08 3.84 ± 0.27

23.01 ± 2.73 0.921

Galactan

1.29 ± 0.08 1.35 ± 0.07 1.32 ± 0.11 1.24 ± 0.10 1.31 ± 0.11 1.37 ± 0.14 0.674

Xylan

22.05 ± 2.55 22.68 ± 1.84 22.28 ± 2.12 21.41 ± 1.28 22.92 ± 1.73

p-value

Glucan

33.02 ± 1.69 33.61 ± 1.43 33.43 ± 3.03 32.92 ± 1.04 33.64 ± 1.43 33.12 ± 1.71 0.991

Storage Moisture (%)

25 35 45 55 65 75

Significantly different samples

Xylan (23oC-220D): 25% moisture content different from 35%; 35% -75% not significantly different

Galactan (23oC-21D): All not significantly different except for 25%, which was different from 45% and 75%

Arabinan (23oC-220D): 25% and 65% not significantly different; 35% -75% not significantly different

Arabinan (37oC-220D): 25% not significantly different from all other moistures; 35%, 45% and 55% significantly different from 75 but not from each other

Water Extractable (23oC-220D): 25%, 65% and 75% not significantly different; 35% -55% not significantly different

Water Extractable (23oC-21D): All not significantly different except for 25% significantly different from 35% and 55%

Water Extractable (37oC-220D): All moisture not significantly different except for 75% MC, which is lower

Non-structural inorganic (23oC-220D): 25% - 55% not significantly different; 35% - 75% not significantly different; 45% different from 65%

Structural inorganic (23oC-220D): 25% -55% not significantly different; 35% - 65% not significantly different; all levels different from 75%

Structural inorganic (37oC-220D): All moisture not significantly different except for 75% moisture, which is higher

90

Table 3.5: Comparing average percentage change, on mass basis, in anaerobic storage samples by groups with reference to Day 0

23oC 4.9% 0.001 14.8% < 0.0001 34.9% < 0.0001 31.5% < 0.0001 6.0% 0.469 3.3% 0.031 5.5% 0.195 29.2% 0.008 113.1% < 0.0001 1.3% 0.888

37oC 5.1% 0.005 21.0% < 0.0001 41.8% < 0.0001 32.9% < 0.0001 31.8% < 0.0001 4.1% 0.009 1.6% 0.695 19.2% 0.048 97.7% < 0.0001 9.8% 0.081

23oC 2.3% 0.147 8.6% < 0.0001 20.1% < 0.0001 23.1% < 0.0001 10.3% 0.026 0.8% 0.686 5.2% 0.493 2.5% 0.771 54.8% 0.001 32.6% 0.011

37oC 1.3% 0.368 1.0% 0.488 33.3% < 0.0001 19.8% < 0.0001 43.1% < 0.0001 1.9% 0.379 9.0% 0.081 10.1% 0.303 68.9% < 0.0001 1.3% 0.885

Day 220

Lignin Structural Inorganics

Non-structural

Inorganics Water Extractives

Ethanol

Extractives

Day 21

Glucan Xylan Galactan Arabinan Acetyl

Note: a significant difference for any of the components does not imply differences at all moisture level rather that there is an increase or decrease in at least one moisture level

Values to the left = percentage change in component (lumped means) with reference to day 0; values to right = p-value; p-values in red imply not significantly different from day 0

Arrows

Pointing up = increase in composition above day 0 by the percentage indicated

Pointing down = decrease in composition by percentage indicated

Horizontal arrows: Pointing left = average value is less than Day zero’s but not significantly different; Right = greater than Day 0 but not significantly different from Day zero

Arrow thickness reflects the relative proportion of each component, on day 0, as a percent of total mass (comparison across row)

Arrow height reflects the relative gain or loss in mass. That is, shows the relative change among group for each particular component. The height does not reflect proportional

changes across components

91

3.4 Conclusions

Anaerobic storage was shown to work well at a wide range of moisture contents, providing

farmers with more flexibility in scheduling the harvest of their biomass feedstock. Although

some variability exists, in general the dry matter loss, organic acid profile and stover

composition were similar for most moisture levels for a given anaerobic storage duration. Wet

storage at 35% [low] moisture is therefore just as effective as storage at 65% [high] moisture,

with outcomes that are generally not significantly different. The relative responses of each stover

type to the varying effect of storage factors (temperature, moisture and duration) in both aerobic

and anaerobic conditions were similar, although in some cases the magnitudes differ

significantly with stover type. Under anaerobic conditions, significant differences in dry matter

loss were observed mainly at 37oC, and even then with no significant difference in 35- 65%

moisture samples. The results also indicated that moisture and temperature were not good

predictors of dry matter loss under anaerobic conditions, whereas under aerobic storage

conditions moisture was the major predictor and the effect of temperature was significant. For

the anaerobic storage samples with their generally lower dry matter losses, there was wide

variability in gravimetric dry matter loss determination for both total dry matter and several

individual constituents.

With respect to organic acids, differences across stover types were observed mainly at 37oC and

at high moistures. The relative proportion of each acid in the portfolio differed, but the total

amount of organic acids did not vary significantly. Although there was some evidence of

secondary butyric acid fermentations, the effect was small and impacts of acid inhibition in

downstream processes do not seem likely. This is important to a robust storage process, as

secondary fermentations can be a major problem for the livestock industry by decreasing silage

palatability. Although stover type had an effect on pH, generally, there was no significant

difference across storage duration for the 45% - 65% moisture samples.

Within the anaerobic storage treatments, the most influential storage factor with respect to stover

composition was storage duration. Storage duration impacted Arabinan, galactan, glucan, lignin,

water extractives and xylan compositions. On the other hand, temperature was influential only

for acetyl and galactan degradation and the storage moisture content was significant only for

92

lignin, water extractives and xylan. With respect to the various components of hemicelluloses,

the least degraded component as a percentage of its initial composition was xylan. For most

stover constituents, changes over the storage period were not linear and in the long run were

generally not significantly different from Day 0.

In general, once extreme moisture levels are avoided, quality in terms of feedstock composition

is not significantly different for biomass stored anaerobically. The determining factor, therefore,

for storage conditions would not be compositional issues but rather the economics of

transportation of high moisture biomass to a refinery, which is likely to draw from a large area so

transportation distances may be great. Compared to dry storage, wet storage could present an

additional advantage (besides issues with narrow harvest windows and reduced soil

contamination) if water soluble extractives can be conserved to avoid degradation of simple

sugars during subsequent processes. Furthermore, it can be assumed that freshly harvested

feedstock will give a more positive result than the rewetted dry corn stover feedstock used in this

study.

3.5 References

Atchison, J. E. and J. R. Hettenhaus. 2004. Innovative methods for corn stover collecting,

handling, storing and transporting. Available at

http://www.nrel.gov/docs/fy04osti/33893.pdf Accessed 27 August, 2008.

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Chapter 4

Corn stover reactivity to cellulolytic enzymes after wet storage

Abstract

Wet storage, synonymous to ensilage, has gained attention as an alternative biomass storage

method for biofuels since 2001, although ensilage been used to preserve forage for livestock feed

for millennia. In this natural process organic acids are produced by anaerobic microbial

degradation of a small fraction of the biomass, and these acids reduce the pH to levels that

minimize further microbial activity and can preserve the biomass for years if anaerobic

conditions are maintained. The interest in this storage method is reinforced by the potential

capability of the organic acids that make this an effective storage strategy to also serve as a mild

pretreatment to enhance downstream conversion processes. The degree and significance of this

natural pretreatment capability of wet storage on downstream processes has, however, not been

verified. Several studies have shown that hemicelluloses are degraded during wet storage, but

there remain questions about whether this degradation is adequate to serve as the sole

pretreatment or reduce the required severity of subsequent pretreatment. In this study, the degree

of pretreatment was investigated by measuring the reactivity of corn stover fiber to cellulolytic

enzymes. Although the results indicated significant improvement in hydrolytic outcomes after

wet storage, saccharification of cellulose to sugar monomers was still limited. The implication of

these results is that post storage pretreatment would still be necessary. The results also show that

dominance of lactic acid in the ensilage process is key to wet storage pretreatment effectiveness.

This is because of the lower pKa of lactic acid compared to pKa of other silage acids. Lactic acid

pka is lower than typical silage pH, giving lactic acid the advantage of having more dissociated

form, hence making protons relatively more available to facilitate the hydrolytic pretreatment

process.

Key words: Ensilage, pretreatment, organic acid, wet storage, corn stover, hydrolysis, glucose,

cluster analysis

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4.1 Introduction

Two main attractions of wet storage under anaerobic conditions are (1) minimizing dry matter

loss, and (2) enhancing downstream pretreatment and conversion processes. In the first case, it is

well established that the production of organic acids and associated reduction of pH have

bacteriostatic or bactericidal effects on most spoilage microbes, and the outcome is a well

preserved feedstock with reduced dry matter loss. For the second case, previous studies have

suggested the potential of the organic acids produced during storage to break some structural

bonds or alter cell wall structure, thereby serving as a pretreatment mechanism. The idea of

ensilage serving as an avenue for in situ pretreatment of the biomass to enhance the downstream

ethanol fermentation process was suggested by Linden et al. (1987) and Richard et al. (2001).

Several more recent studies have documented promising effects (Chen et al., 2007; Digman et

al., 2007; Ren et al., 2007; Thomsen et al., 2008; Oleskowicz-Popiel et al. 2010; Pakarinen et al.,

2011), although the range of silage conditions and their impacts have not been fully explored.

The pretreatment mechanism of organic acids during wet storage is hypothesized to be similar to

that of dilute hydrochloric, sulfuric, or phosphoric acid, as these inorganic acids have been

extensively explored for the pretreatment of lignocellulosic feedstocks. However, there are

important difference between wet storage and pretreatment in terms of both temperature and

time. In the case of inorganic acid pretreatment, the acid-amended biomass is exposed to high

pressures and temperatures (typically 120oC to 200

oC) for a period of minutes, whereas in wet

storage the temperatures would be much lower (20oC to 40

oC) but the duration much longer on

the order of months to years. The purpose of pretreatment is to allow more effective enzymatic

hydrolysis of sugar polymers into sugar monomers, as those sugar monomers can serve as a

reactive intermediate for fermentations that produce biochemicals and biofuels. The two main

polysaccharides of interest in lignocellulosic feedstocks are cellulose and hemicelluloses. The

reactivity of acids with biomass is random and targeted mainly at glycosidic bonds, which are

found between and within the polysaccharide chains. Dilute acid hydrolysis results in partial

hydrolysis of glycosidic bonds, and can also result in degradation of lignin if temperatures are

above 180oC (Chesson, 1993). At ambient temperatures, which would be typical of most wet

storage systems for biofuel production, acid hydrolysis is slow and limited to the amorphous

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regions where the hydrolytic reaction is initiated (Tímár-Balázsy and Eastop, 1998; Stoddart,

2007). Amorphous cellulose constitutes anything from 10% to 50% of total wood cellulose,

depending on the feedstock (Jacobson and Wyman, 2000). Cellulose is less susceptible to acid

pretreatment because of its highly crystalline nature. In contrast, hemicellulose has a

comparatively lower degree of polymerization and a highly branched, heterogeneous and fully

amorphous structure that makes it more susceptible to acid reactions. Another factor contributing

to hemicellulose’s susceptibility is the presence of α-glycosidic bonds in the side chains, which

are less stable than the β-bonds found in cellulose and the main chain of hemicellulose. The

pretreatment capability of organic acids, produced under wet storage conditions, is therefore

achieved mainly through hemicellulose hydrolysis.

A number of studies (McDonald et al. 1991; Muck, 1996; Richard et al., 2001; Ren et al. 2006)

have shown the preferential degradation of hemicelluloses over cellulose during ensilage. The

hydrolysis of hemicelluloses is an important pretreatment outcome of conventional acid or liquid

hot water pretreatment, and the effectiveness of these pretreatment processes is associated with

the amount of hemicelluloses removed (Wyman, 1999; Sun and Cheng, 2002; Mosier et al.

2005). Conventional dilute acid pretreatment can result in up to 90% removal of xylose (the

major constituent of hemicellulose), while liquid hot water pretreatment can remove up to 51%

(Elander et al., 2005; Zheng et al., 2009). Since the structural sugars are bound or linked

together throughout the plant cell wall, it can be expected that the degradation of hemicellulose

will increase the surface area and allow enzymes access to other structural components through

the openings created by their removal. Levels of hemicellulose degradation in silages can range

from 0.5% or less (Muck, 1996) to as high as 54% depending on the crop type, storage

conditions and storage additives (Ren, 2006). These variations in hemicellulose degradation not

only reflect the heterogeneity of biomass feedstocks, but also the sensitivity of wet storage to

different conditions, and thus pose a potential challenge to wet storage as a complement to other

pretreatment systems.

In addition to measures of hemicellulose degradation during wet storage, electron micrographs

show evidence that the structure of ensiled corn stover differs from that of unensiled stover

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(Donohoe et al. 2009; Oleskowicz-Popiel et al. 2010). Several of these images are reproduced for

comparison in Appendix C, Figures C1 and C2. However, the significance of these impacts has

not been verified. Investigations (Linden et al., 1987; Henk and Linden, 1994; Chen et al., 2007;

Digman et.al., 2007) into downstream processing of ensilage usually involve enhanced silage,

which is ensilage with some form of additive. The controls, silage without additives, were not

consistently better or similar to unensiled samples with respect to hydrolytic and fermentation

yields. These studies have implicitly attributed the effectiveness of silage as a pretreatment or

hydrolytic tool to these silage additives, and overlooked the potential of the plain, unamended

ensilage process.

The specific objective of this study was to determine the fiber reactivity of plain silage based on

storage conditions and relate this to the corresponding organic acid profile. For reporting

purposes, the organic acid profile relates to the types, amount and relative proportion of each

acid present during storage. The fiber reactivity concept was adapted from Henk and Linden

(1994) and refers to how easily the fiber can be converted to sugar through enzymatic reaction.

The essence of the fiber reactivity test is to determine if organic acids produced during ensilage

have any significant pretreatment effect on fiber structure. The response to enzymatic reaction

would be an indication of the degree of pretreatment that has occurred during storage, and can be

used in defining the pretreatment capability of wet storage.

4.2 Materials and Methodology


Corn stover from Iowa (IA stover) was obtained in 2008 from US Department of Energy’s Idaho

National Lab while stover from Pennsylvania (PA stover) was obtained from the Penn State

Dairy farm. The IA stover, planted from Pioneer brand 34A20 seed, was conventionally

harvested after the grain harvest in fall of 2007 from the Boyd plot near Boone, IA. After field-

drying for 3 to 5 days it was raked, baled, transported to Idaho and stored indoors with a tarp

cover to prevent dust accumulation. Particle size reduction to 1 inch minus (less or equal to 25.4

mm) was carried out in the early summer of 2008. The PA stover, planted from Dekalb DKC54-

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46 seed, was left standing in the field through the winter of 2007 and harvested and chopped in

spring of 2008.

The particle size distribution was analyzed using the Penn State Forage Particle Separator. The

PA stover had approximately 26 % of particles greater than 19 mm, 26 % were between 19 and

8 mm, 37 % were between 8 and 1.18 mm and 11 % were less than 1.18 mm. The IA stover had

a similar distribution; 26 % of particles greater than 19 mm, 26 % were between 19 and 8 mm,

31 % were between 8 and 1.18 mm and 17 % were less than 1.18 mm. A description of this

particle size separation method can be found in Heinrichs and Kononoff (2002).

Corn stovers from these two origins were presumed to have different compositional quality. The

initial moisture contents of the IA and PA stovers, approximately 7% and 30% respectively, were

adjusted to six various moisture levels (25, 35, 45, 55, 65, and 75% wet basis). Moisture

adjustment was as described in Chapter 3 and was within ±2 percentage units of the target

moisture. Corn stover was packed at a density of about 159 dry Kg/m3 in 1 pint (0.00047 m

3)

glass canning jars that were tightly sealed to create anaerobic conditions and stored for 220 days

at 37oC. Experiments were performed in triplicate. After storage, samples were stored in the

freezer at -20oC until further analysis. See Appendix C, Figures C3 and C4, for process chart and

experimental plan.

4.2.2 Corn stover compositional analysis

Glucan and xylan composition of the IA stover were determined using the methods described in

Chapter 3. The composition of the PA stover was only determined for day 0, again using the

methods described in Chapter 3. Glucan and Xylan degradation in PA stover, during storage, was

assumed to be similar to IA stover and deducted accordingly to obtain composition for day 220.

4.2.3 Organic acids and fiber reactivity test

Soluble extracts were collected before and after storage for organic acid measurements. Stover

samples were mixed with deionized water at a ratio of 1:10, wet stover weight: water. The

mixtures were shaken for 30 minutes at 200 rpm using a Barnstead SHKA 2000 open air

platform shaker (Barnstead International, Dubuque, IA) after which the extracts were filtered

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through Whatman No.1 paper. The collected extracts were filtered again using 0.2 m PTFE

filters, diluted 20-fold and analyzed using Dionex ICS 3000 ion exclusion chromatography

(Thermo Fisher Scientific Inc., Dionex ICS 3000, Sunnyvale, CA) for types and amount of

organic acids. Separation was performed at 30oC using IonPac ICE-AS1 guard (4 x 50 mm) and

analytical (4 x 250 mm) columns with 100 mM methanesulfonic acid at a flowrate of 0.16

mL/min. Organic acids were detected with a photodiode array detector (Dionex UVD 340U) at a

wavelength of 210 nm. Thirteen different potential acids (lactic, acetic, butyric, pyruvate,

isobutyric, valeric, isovaleric, propionic, tartaric, malic, formic, citric, succinic) were used as

standards.

Fiber reactivity across moisture levels

For the fiber reactivity assay, replicates of wet stored samples, “as is” were combined then

washed so that the organic acids and background sugar level were negligible and did not

interfere with the assay either by inhibiting enzyme activity or inflating hydrolytic yields. Also,

the removal of all extracts provided a better basis for directly testing the fibers. Samples “as is”

refer to silage without any post storage processing like drying or further size reduction. Washing

the “as is” silage was accomplished in multiple steps, starting with a Waring laboratory blender

with a 1: 10 dry stover: water ratio on low speed for 15 seconds followed by high speed for 10

seconds. The liquid was drained with a sieve, rinsed with water and transferred to plastic

centrifuge bottles. Water was added to obtain a 1:50 dry stover: to water ratio and shaken at 120

rpm for 30 minutes in a Barnstead Max Q5000 SHKE 5000-7 floor shaker (Barnstead

International, Dubuque, IA). The liquid was drained and the washing was repeated. After

washing, samples were drained with a sieve and compressed with a mechanical press to get rid of

excess free water. Water was added to the solids at a 1:00 dry stover: to water ratio and shaken at

120 rpm for 30 minutes, after which supernatants were collected to analyze for residual sugar.

The residual sugars in the washed samples were less than or about 0.01% on a dry matter basis.

The moisture content of washed samples was estimated using the microwave method (Jones et

al., 2004) as a guide to how much wet material was required to achieve the target dry weight for

the fiber reactivity test. Fiber reactivity was measured by how much glucose was released after

three days of enzymatic hydrolysis without pretreatment. For the PA stover samples, washed

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samples were hydrolyzed at 15% solid loading using a commercial cellulase (Spezyme CP,

Genencor, Rochester, NY) at 0, 2, 5 and 15 FPU/g initial glucan and a commercial β-glucosidase

(Novozyme 188, Novozymes A/S, Bagsvaerd, Denmark) at 0, 8, 20 and 60 CBU/g initial glucan.

The different enzyme application levels were used on the PA stover samples to explore the

sensitivity of ensiled and unensiled feedstock to these enzyme loading rates. Hydrolysis was

carried out in 50 ml centrifuge tubes with approximately 1 dry gram of stover and in triplicates.

Tetracycline was added at a final concentration of 40 μg/mL to prevent microbial growth during

hydrolysis, and citric acid buffer (pH 4.5) was added to obtain a final concentration of 0.05 M to

maintain the pH in the optimum range for enzyme activity. Samples were vortexed for

approximately 5 seconds before placing in a Barnstead Max Q5000 SHKE 5000-7 floor shaker/

incubator (Barnstead International, Dubuque, IA) at 50oC, 120 rpm for hydrolysis. A HOBO

®

U12-011H temperature data logger (Onset Computer Corp., Cape Cod, MA) was placed in

incubator to monitor temperature over the 3-day hydrolytic period. Control samples included

substrate blanks, enzyme blanks and unensiled feedstock as negative controls and Avicel (α-

cellulose, which is pure insoluble cellulose) as a positive control. Fiber reactivity across a range

of moisture contents was performed in two batches. The PA samples constituted the first batch of

the reactivity test, and were tested with the full range of enzyme loading rates described above.

After that PA stover batch was completed, a single cellulase enzyme loading rate of 15 FPU/ g

glucan with corresponding β-glucosidase loading rate of 60 CBU/g glucan was chosen for the IA

stover batch.

Fiber reactivity after pretreatment

The purpose of the pretreated samples was to determine if fiber reactivity of wet storage samples

would be more pronounced or effective after pretreatment. Pretreated washed PA samples were

also included in the first batch for hydrolysis. The pretreatment process employed was a liquid

hot water (LHW) method using the Dionex Accelerated Solvent Extraction 350 (ASE 350)

(Thermo Fisher Scientific Inc., Dionex ASE 350, Sunnyvale, CA) system at 190oC, with one

static cycle of 15 minutes and 0% flush. This temperature and time have been found to be the

optimum condition for controlled pH LHW pretreatment of corn stover (Mosier et al., 2005).

Hydrolysis was performed on the pretreated solids only (without the pretreatment liquid extract)

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in order to directly test the stover fiber without interference from extracted solubles, which may

contain sugars, acids and other inhibitors.

Fiber reactivity based on wet storage duration

Another variant to the fiber reactivity investigation was to explore reactivity based on storage

duration as opposed to reactivity across different moisture levels described earlier. Storage

durations investigated were 0, 21, 90 and 220 days at 23oC and 37

oC. Based on results obtained

from preceding investigations, two moisture levels (35% and 65%) with favorable outcome in

terms of fiber reactivity were chosen for Both IA and PA stover. However, unlike the previous

reactivity test, samples were dried and ground before testing. Drying was carried out in a

HotPack convection oven at 55oC to constant weight and grinding on a Wiley Mill (Model 4,

Thomas Scientific, Swedesboro, NJ) using a 2 mm screen. The dry ground samples were washed

using the ASE 350 system at 40oC, with three static cycles of 10 minutes each and 100% flush.

The same types of controls used in fiber reactivity across moisture were used for reactivity based

on duration. In addition, samples representing dry storage (i.e. at recommended moisture of

15%) and “as received” with a moisture content of approximately 7% were also tested. Also

included were corresponding aerobic samples. Correlating fiber reactivity with storage time

could be beneficial in determination of an effective minimum storage duration as well as the

predictability of downstream outcome for longer storage durations.

In all cases, after hydrolysis 20 ml of deionized water was added to each sample to facilitate

sampling due to the high solids loading. The samples were vortexed to mix and then centrifuged

to collect supernatant for sugar analysis. Supernatant from each sample was placed in a hot water

bath at 95oC for 10 minutes to prevent any further enzymatic reaction. The supernatants were

then filtered using a 0.2 μm nylon filters and stored at -20oC until analyzed for sugars.

4.2.4 Data analysis

Filtered samples were diluted 5-fold and the amount of glucose released during hydrolysis was

measured using a YSI 2700 SELECTTM

biochemical analyzer (YSI Inc., Yellow Springs, OH)

with 2% precision. Glucose yields from substrate and enzyme blanks, if any, were subtracted

from sample yield to get actual glucose resulting from hydrolysis. For pretreated samples, glucan

105

removed during pretreatment was subtracted from initial amount. Results were analyzed using

statistical tools such as principal component analysis (PCA), clustering analysis and analysis of

variance (ANOVA). To analyze the effect of organic acids on fiber reactivity, Principal

Component Analysis (PCA) was first performed on raw data to see if storage samples could

naturally be grouped into categories based on their organic acid profiles and to estimate the

number of categories to specify for the cluster analysis. Ward’s hierarchical clustering method

was then used to group the samples into the appropriate number of clusters. Two other cluster

analyses were performed using hydrolytic yields. One involved hydrolytic yields from the four

cellulase enzyme loadings (0, 2, 5 and 15 FPU/g glucan) for the PA samples as variables for

grouping and the other combined the IA and PA samples with two cellulase enzyme loadings (0

and 15 FPU /g glucan) for grouping. All correlation analyses were carried out using the Pearson

Method. This gives the Pearson correlation coefficient, r, which is a dimensionless index that

ranges from -1.0 to 1.0 and measures the degree of linear relationship between two data sets. The

initial assumption of linearity is rejected if “r” is 0 or the p-value is greater than 0.05. All

statistical tests were conducted at a significance level, α, of 0.05. Statistical software used for

these analyses are The Unscrambler® X 10.0 (CAMO Software Inc., Woodbridge, NJ) and

Minitab 14 (Minitab Inc., State College, PA).

4.3 Results and Discussion

4.3.1 Organic acid profile and cluster analysis

Organic acid profile

The organic acids identified in the wet stored stover samples included lactic, acetic, butyric,

isobutyric and propionic acids. Low levels of tartaric and malic acids were also identified in a

number of samples but were difficult to quantify due to their short retention times near the

elution of the system void volume and hence were not used in the current analysis. The results

indicated that IA control (day 0) samples had essentially no organic acids (see Figure 4.1). All

acids are reported on a dry matter basis. PA control (Day 0) samples had only lactic and acetic

acids, both of which constituted ≤ 0.6% dry mass of stover. These two acids were also present in

all wet stored IA and PA samples, except for samples stored at 75% moisture, which had no

lactic acid likely due to clostridia secondary fermentations (Jones et al., 2004). Lactic acid

106

concentration was up to 4.9% and 2.2% in IA and PA stover respectively while acetic acid was

up to 3.5% and 4.2 % respectively. Isobutyric acid was also present in all storage samples.

Propionic and butyric acids were present only in high moisture storage samples. Figure 1 shows

the complete organic acid profile of IA and PA stover at the two storage durations and different

moisture levels. The lower acid content of 45% PA stover could be due to a compromise in

anaerobic condition as reflected in high mean pH (5.12) compared to other storage samples (4.00

– 4.93) in Table C4, Appendix C. In general total acid content increased linearly with moisture

content. The variation in total acids and a regression plot is provided in Appendix C, Figure C13.

The correlation between total acids and moisture content was 0.813 and 0.819 for IA and PA

samples respectively, both with p-value of < 0.0001. In terms of individual acids, all acids were

significantly correlated with moisture except lactic acid (lactic (r = -0.145, p = 0.398), acetic (r =

0.807, p < 0.001), propionic (r = 0.508, p = 0.002), isobutyric (r = 0.586, p < 0.0001), and

butyric (r = 0.651, p < 0.0001)). Analysis of variance showed the mean value of the various

organic acids at the different moisture levels are all not equal (lactic (p = 0.003), acetic (p <

0.001), propionic (p = 0.010), isobutyric (p = 0.001), and butyric (p < 0.001)). The ANOVA

results comparing the means at the various moisture levels can be found at Appendix C,

Statistical analysis (A). Table 4.1 shows the differences in organic acid across moisture levels

and stover type.

107

Figure 4.1: Organic acid profile showing means of IA and PA stover at different moisture levels. Lighter bars are IA samples and

darker bars to the right are PA samples. Error bars are standard deviations of mean

25 25 25 25 35 35 35 35 45 45 45 45 55 55 55 55 65 65 65 65 75 75 75 75

0 0 220 220 0 0 220 220 0 0 220 220 0 0 220 220 0 0 220 220 0 0 220 220

Butyric ,% 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.00 0.00 0.00 1.01 0.00 0.00 2.57 1.18

Isobutyric ,% 0.00 0.00 0.04 0.13 0.00 0.00 0.20 0.18 0.00 0.00 0.27 0.11 0.00 0.00 0.34 2.16 0.00 0.00 0.16 1.21 0.00 0.00 1.28 1.81

Propionic ,% 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.36 0.00 0.00 0.00 0.34

Acetic ,% 0.00 0.00 0.14 0.25 0.00 0.08 0.63 0.77 0.05 0.09 0.74 0.17 0.00 0.22 1.14 2.20 0.00 0.24 1.24 2.51 0.00 0.11 2.74 2.73

Lactic ,% 0.00 0.00 0.26 0.99 0.00 0.24 1.94 1.68 0.00 0.21 2.85 1.31 0.00 0.16 2.95 1.36 0.00 0.26 3.03 0.21 0.00 0.18 0.00 0.00

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

Org

anic

aci

d (%

dry

mat

ter)

Duration (days)

Moisture (%)

IA

IA

IA

IA

IA

IA

IA IA IA IA IA IAPA

PA

PA

PA

PA

PA

PA

PAPA

PAPA

PA

108

Table 4.1: Differences in organic acids levels across moisture levels and stover type

Cluster analysis

For PA organic acids, the first two principal components accounted for ~82% of total variability.

Approximately 41% of the samples were significantly associated with the first component, and

about 53% were associated with the second component. Acetic, propionic, isobutyric and butyric

acids, i.e. volatile fatty acids, were the major contributors to component 1, while lactic acid was

the main contributor, ~ 86%, to component 2. This is also true for PCA of the combined PA and

IA samples. These PCA results were supported by the strong correlation acetic acid has with

isobutyric acid (0.912, p < 0.0001), propionic acid (0.643, p = 0.001) and butyric acid (0.770, p <

0.0001), all of which were strongly correlated with each other. No significant correlations exist

between lactic acid and the other acids. However, when considering only 220-day samples, there

were some negative correlations between lactic and isobutyric (-0.446, p = 0.006), propionic (-

0.360. p = 0.031) and butyric acids (-0.552, p < 0.0001) but still no correlation with acetic (-

109

0.297, p = 0.079). Based on the PCA result, three categories were chosen for Ward’s clustering

analysis. The three categories resulting from the clustering analysis of PA samples were: (1) a

high amount of acids (5 to <10%) associated with high moisture wet stored samples (55-75%

moisture), (2) a moderate amount of acids (2 to <5%) associated with low moisture wet stored

samples (35 and 45% moisture) and (3) a low amount of acids (<2%) associated with day zero

and 25% moisture anaerobic (wet stored) samples. These groupings were maintained when

combined with the IA samples, which had a slightly different pattern. The IA groupings were:

(1) High amount of acids: wet stored samples with 75% moisture, (2) moderate amount of acids:

wet stored sample with 35 – 65% moisture and (3) low amount of acids: day zero and 25%

moisture anaerobic (wet stored) samples.

4.3.2 Fiber reactivity of pretreated washed PA stover

To evaluate the impact of pretreatment, xylan and glucan removal as well as hydrolytic sugar

yields were measured by enzymatic hydrolysis. Xylan removal was used as an indicator of liquid

hot water (LHW) effectiveness. When taking into account the amount of xylan degraded during

storage, xylan removals in the Day 220 samples were significantly higher (55.0% ± 7.2) than

Day 0 samples (47.2% ± 6.1) (p < 0.0001) as shown in Figure 4.2. Xylan degradation during

storage was not determined for PA samples, so to calculate the actual amount of xylan removed

during enzymatic hydrolysis, the average value for IA stover (5% - see Chapter 3) was assumed.

Disregarding amount of xylan degraded during storage, xylan removal by enzymatic hydrolysis

at Day 0 and Day 220 were not significantly different. See Table C1 in Appendix C. This

suggests that the percentage xylan removal had approached the limit of removal under LHW

conditions or could be close to xylan removal under optimum pretreatment conditions. The idea

of a limit to xylan removal in liquid hot water pretreatment is also based on results from Elander

et al. (2005) and Zheng et al. (2009), in which xylan removal was less than 55%. Xylan removals

for Day 220 samples were more variable than Day 0 and not significantly different across

moisture (p = 0.915). For Day 0, there were no significant differences among the different

moisture treatments except for the 35% moisture condition, which was lower than 65% moisture

(p = 0.031). See Figure 4.2.

110

Also, as shown in Figure 4.2, the glucan removed during enzymatic hydrolysis was generally

less than 4% for both day 0 and day 220. Less glucan was present in day 220 samples as a result

of a net 1% loss in glucan during storage (See Chapter 3), and Day 0 samples had significantly

higher glucan removal during pretreatment (P < 0.0001). The relatively low glucan removed

from wet storage samples could be a result of some glucan degradation during storage, hence a

lesser amount of readily degradable glucan was available for removal. For glucan removal there

was no significant difference across moisture for Day 0 samples (p = 0.545). Glucan removal at

Day 220 was also not significantly different across moisture levels except for 25% moisture (p =

0.030), which was similar only to 35% moisture samples. See Figure 4.2.

Figure 4.2: Xylan and glucan removal during LHW pretreatment of ensiled (day 220) and

unensiled (day 0) PA stover. Error bars are standard deviations of mean

Glucose yields, as percent theoretical, were calculated with reference to the mean glucan

composition for each moisture level. The glucan content of the initial biomass prior to the

storage trials ranged from 33.50% to 35.63% for both IA stover and PA stover. Grams glucan

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

18.0

20.0

0

10

20

30

40

50

60

70

80

15 25 35 45 55 65 75 85

Xyl

an r

emo

ved

, %

Storage moisture, %

Xylan - day 220 Xylan - day 0 Glucan - day 220 Glucan - day 0

Glu

can

rem

ova

l, %

111

were converted to glucose using a factor of 1.111. Glucan removed during pretreatment was

subtracted from this original amount to determine the appropriate amount of enzymes to add and

for calculation of theoretical yields. As expected, glucose yields increased significantly with

enzyme loading (p < 0.001) (see Figure 4.3). For the PA stover, these values were 28.79% ±

4.33, 47.18% ± 3.45 and 84.18% ± 6.65 for 2, 5 and 15 FPU/g glucan respectively. The results

indicated that the glucose yields of Day 220 and Day 0 samples were not significantly different

at the various enzyme loadings, p = 0.586 (Figure 4.3). Furthermore, glucose yields at various

moisture levels for each enzyme loading were not significantly different for both Day 0 (p =

0.994) and Day 220 (p = 0.990). The implication is that xylan removal did not serve as a good

indicator for downstream glucan hydrolysis. This is understandable since conditions used for

pretreatment were the optimum time and temperature for raw feedstock. Given the differences in

xylan removal, it is possible that at a lower pretreatment severity (less time and/or a lower

temperature), there might be significant differences in glucose yield between wet storage

samples and Day 0 samples.

Enzyme Loading (FPU/g glucan)

Glu

co

se

yie

ld (

% t

he

ore

tica

l)

1510520

90

80

70

60

50

40

30

20

10

0

1510520

Day 0 Day 220

Moisture

65

75

25

35

45

55

Figure 4.3: Mean glucose yield ( % theoretical) of pretreated washed PA corn stover fiber at

different enzyme loadings

112

4.3.3 Fiber reactivity of corn stover without pretreatment

Fiber reactivity results showed the glucose hydrolysis yields for both stover types to be generally

poor, less than 30% of theoretical. See Figure 4.4. The glucose yields after hydrolysis were

calculated using mean glucan composition for each moisture level as was done for pretreated

samples. As expected, sugar yield increased with increased enzyme loading. At 15 FPU cellulase

enzyme loading, the glucose yields of Day 220 samples of both IA and PA stover were

significantly higher (p < 0.0001) than corresponding Day 0 samples. Average yields for Day 220

were ~23.61% ± 3.16 and 15.54% ± 5.71 respectively in contrast to 14.30% ± 4.83 and 11.05% ±

3.51 for Day 0. At 5 FPU cellulase enzyme loading, glucose yields of PA stover at Day 220

(12.56% ± 3.41) were significantly higher than yields at Day 0 (9.60% ± 3.85) (p = 0.022).

However, at 2 FPU there were no significant differences as a result of wet storage, 8.83% ± 2.80

vs 6.93% ± 3.20 for Day 220 and day 0 respectively (p = 0.065). Compared to yields obtained

from Avicel hydrolysis (~ 58% for 2 FPU, 65% for 5 FPU and 83% for 15 FPU), enzymatic

access to stover cellulose was still limited after wet storage. This could be due to the low level of

hemicellulose degradation during storage as compared to conventional pretreatment. On average

10% of hemicelluloses in IA stover was degraded during storage, in contrast to ~55% removal

during LHW pretreatment [of PA stover]. In theory, the pretreatment capability of wet storage

derives from hemicelluloses degradation as a result of organic acid interaction.

In this study there was no significant difference in storage (Day 220) hemicellulose degradation

across the tested range of storage moisture contents (p = 0.083) and no correlation between

hemicellulose degradation and glucose yields (p = 0.90).. However, if day 0 samples are also

considered in addition to the Day 220 samples, in the correlation analysis, then the correlation

between hemicellulose degradation and glucose yield is 0.62 (p = 0.001). See Appendix C,

Figure C7 for graphical relationship between glucose yield and hemicellulose degradation during

storage. For Day 220, the IA samples had statistically significant groups of ((25-45%) 65%), (55,

65%) and (55, 75%) in decreasing glucose yields (p <0.0001). The overlaps defined using the

Tukey test suggest glucose yields at all moisture levels were not that different except for 75%

moisture, which is similar to 55% percent moisture but significantly differs from the remaining

moisture contents. For the PA samples at an enzyme loading of 15 FPU, the groups were (25%,

113

75%), (35%, 65%) and (45% -65%) (p <0.0001). See Appendix C, statistical analysis (B) for

Tukey results used for glucose groupings. Yields from the 25% and 75% moisture samples were

the lowest among this set. Thus when these extreme moisture levels are avoided, a similar

glucose yield could be expected from wet storage across a wide range of moisture contents. On

the other hand Day 0 samples did not show any significant difference in glucose yields across

moisture for both stover types (p = 0.346 for IA stover and p = 0.073 for PA stover). See Figure

4.5 for glucose yields across moisture.

Figure 4.4: Variation in glucose yields for different enzyme loadings and means values for the

different moisture levels. Big black circles are lumped means for each enzyme loading.

114

Figure 4.5: Glucose yield of fiber reactivity test, with 15 FPU/g glucan cellulose enzyme

loading, without pretreatment.

Cluster analysis was performed on glucose yields of the fiber reactivity test, initially assuming

the same number of groups (three) as was determined for the organic acid cluster analysis (see

Appendix C, Tables C3 and C5). PA samples were clustered based on yields from the four

cellulase enzyme loadings: 0, 2, 5 and 15 FPU /g glucan (see Appendix C, Table C8 and Figures

C8 and C9). The result was slightly different from the combined IA-PA set, which was based on

6.90

23.25

16.07 17.38

18.40

11.22

7.38

13.88 13.62

8.31

10.93 12.21

0

5

10

15

20

25

30

25 35 45 55 65 75

Glu

cose

yie

ld (

% t

he

ore

tica

l)

Storage moisture (%) PA

27.53

24.70 25.56

20.98

23.88

18.99

18.09

11.76 12.60 11.16

17.72

14.48

0

5

10

15

20

25

30

25 35 45 55 65 75

Glu

cose

yie

ld (

% t

he

ore

tica

l)


Wet stored (Day 220) Control (Day 0)IA

115

two enzyme loadings (0 and 15 FPU/g glucan) (see Appendix C, Table C6). The three PA stover

glucose clusters consisted of (1) high glucose yield: wet storage (Day 220) 35 -65% moisture

samples, (2) moderate glucose yield: all Day 0 samples excluding 25% moisture, as well as 25%

and 75% wet stored (Day 220) samples; and (3) low glucose yield: Day 0 25% moisture. The

combined IA-PA analysis yielded: (1) high glucose yield: wet storage with 25 -65% moisture for

IA; 35% for PA, (2) moderate glucose yield: wet storage moisture of 75% for IA; 45 - 65% for

PA and (3) low glucose yield: Day 0 samples including PA wet stored samples of 25 and 75%

moisture. The cluster outcome also showed that for each stover type, two groups could be

sufficient, defined as wet stored samples and day zero samples. In general, IA samples had

higher glucose yields than PA samples (p > 0.0001). Wet stored IA samples were clustered as

relatively high yielding while wet stored PA samples fall mainly under moderate glucose yield.

This lower outcome for PA stover compared to IA stover could be due to the lower pH of the

former and the presence of more volatile acids which have higher pKa. The implication is that

the PA stover had comparatively fewer protons available during storage for the catalysis of

hydrolytic pretreatment.

4.3.4 Fiber reactivity of dry stover without pretreatment as affected by storage duration

The glucose yield after various storage durations was evaluated for dry ground samples without

pretreatment. Glucose yields were also generally poor, less than 30% of theoretical yield (see

Table 4.2) as in the case of fiber reactivity across moisture and without pretreatment. There was

no significant difference in yields across duration except for Day 0 samples, which were

generally lower (p = 0.001). Glucose yields from “as received” samples, 1-inch minus dry stover,

were similar to samples stored anaerobically at 35% and 65% moisture, and were higher than

samples stored anaerobically at 15% moisture (p = 0.001). Although there was no significant

difference in total organic acids present in samples stored at 23oC and 37

oC (p: PA = 0.917; IA

=0.454), mean glucose yields were generally higher at 37oC (22.45% of theoretical) than 23

oC

(19.8%) (p = 0.004). In the previous discussion on organic acids, lactic and acetic acid were not

correlated hence qualifying the two acids as potential predictors, if multicollinearity is to be

avoided. These two acids are therefore the ones considered here in defining relationship between

storage acid and glucose yield. Lactic acid levels in PA samples were generally higher for 23oC

116

compared to 37oC (p = 0.001). Acetic acid on the other hand was not different at these

temperature levels (p = 0.212). Aerobic samples had no organic acids. However, for the IA

stover samples neither of these two acids were significantly different across these two

temperature levels, (p =0.080 and 0.943 for lactic and acetic acid respectively.) Aerobic samples

had low glucose yield, which may be an indication that much of the readily hydrolyzed glucose

was already degraded during storage, since cellulase and other hydrolytic enzymes are produced

by aerobic microbes. Microbial contamination during hydrolysis evident through molds present

in these samples, may also explain these low glucose yields after aerobic storage. The same

amounts of antibiotics were added to these aerobic samples, but this was not effective in

presenting these microbial growths. Although samples were dried after storage and well

preserved, the low drying temperature (55oC) may only have inactivated and not killed the

spoilage microbes present. Spores and other microbial structures can tolerate dessication and

may have been carried over from storage, and some may have been resistant to the effect of

antimicrobial agents.

Table 4.2: Fiber reactivity results (glucose yield as percentage of theoretical) of dry stover for

other storage conditions

Moisture

(%)

Temperature

(oC) Day 0 Day 2 1 Day 9 0 Day 22 0 Day 2 1 Day 9 0

As received

(7.5%)25.2 ± 0.57

15 37 22.43 ± 1.87 15.18 ± 4.42 16.51 ± 8.53

22 20.37 ± 1.55 21.57 ± 0.31 22.77 ± 1.06 15.12 ± 1.83 13.18 ± 0.38

37 21.81 ± 2.04 24.54 ± 0.23 26.62 ± 1.52 10.65 ± 2.21 15.52 ± 1.44

22 13.62 ± 3.33 14.57 ± 0.37 20.59 ± 0.27 0.09 ± 0.08 0.05 ± 0.09

37 22.33 ± 0.27 26.27 ± 1.12 24.31 ± 0.45 0.01 ± 0.02 0.17 ± 0.29

22 18.02 ± 2.69 22.1 ± 0.77 22.69 ± 0.24 14.59 ± 1.05 8.51 ± 4.37

37 23.19 ± 0.18 24.73 ± 0.34 14.56 ± 2.17 10.38 ± 4.4

22 5.86 ± 1.48 19.47 ± 0.37 18.21 ± 0.48 1.11 ± 1.16 0

37 19.63 ± 1.01 22.55 ± 2.01 0.91 ± 0.09 0

PA

stover

35

65

Anaerobic Aerobic

IA stover 35

65

117

4.3.5 Relating organic acid cluster with fiber reactivity cluster

The relationship between the clusters of similar organic acid portfolios with clusters of similar

glucose yields was examined using results from the fiber reactivity assay across different

moisture contents for non-pretreated, anaerobically stored samples. For the PA stover clusters,

high glucose yields were associated with high acid levels (excluding the 75% moisture sample)

and moderate acid levels. Moderate glucose yields were associated with Day 0 samples

(excluding the 25% moisture sample) as well as Day 220 25% and 75% moisture wet stored

samples. Low glucose was associated with 25% moisture Day 0 samples. The relationships

between the organic acid and fiber reactivity clusters of the combined IA and PA samples are

shown in Figure 4.6. The pattern in Figure 4.6 suggests some reasonably distinct range of

glucose yields and associations with organic acid levels and also corroborates the advantage of

anaerobic storage at moisture levels of 35 -65%. Glucose yields across this full population of

stored and unstored samples were correlated with the total organic acids (r = 0.349, p = 0.002)

with significant differences in the glucose yields of the various group (p < 0.0001). In general,

high glucose yields were associated with moderate organic acid levels; moderate glucose yields

were associated with high organic acid levels; and low glucose yield with little or no organic

acids. Two exceptions were the PA and IA stover samples stored anaerobically at 25% moisture.

Interestingly, although both clusters in the low range for organic acid concentrations, the IA

stover produced a high glucose yield while the PA stover had a low glucose yield. This suggests

an important effect of biomass harvest conditions and structural composition on subsequent

hydrolysis. Across this full population of initial (Day 0) and wet storage (Day 220) samples, only

lactic acid showed a significant correlation with glucose yield (r = 0.518, p < 0.0001).

118

Figure 4.6: Relationship between organic acids grouping and fiber reactivity grouping of PA and

IA corn stover (without pretreatment) and grouped using mean glucose yields from cellulase

enzyme loading of 15 FPU /g glucan.

Chart plotted using ranks of glucose and total acids. Diamonds represent day 0 samples and circles represent day 220 samples.

Numbers following IA and PA represent storage moisture content on a percent wet basis. See Appendix C, Figure C11 for

alternate graph using actual glucose and acid amounts.

When considering only stored samples (Day 220), glucose was not correlated to total acids (-

0.142, p = 0.409). The contrary effects associated with low acid concentrations in the 25%

moisture IA and PA samples were partially responsible, as were the moderate and low glucose

yields for the highest acid concentrations. Moderate acid concentrations were associated with

high glucose yields, suggesting an optimum level of total acids. Glucose yields showed some

significant correlations with some individual acids; positively with lactic acid (0.366, p = 0.028)

and negatively with propionic (-0.345, p = 0.039) and isobutyric (-0.340, p = 0.043). There was

no correlation with neither acetic nor butyric acids (each: 0.299, p =0.180). In Table 4.3, it can

119

be observed that this overall relationship does not necessary hold within the cluster groups.

Organic acids, specifically lactic and acetic acids were significantly and positively correlated to

glucose yields in Group 2 (samples with moderate acid levels), even though lactic acid

dominates. In Group 1 (samples with high acid levels), all acids were present, however none of

them was correlated with the glucose yields. Acetic acid is dominant in this group, and even

assuming the correlation p-values gave a false negative (e.g., that relationships with p-values

>0.05 were in fact correlated), acetic acid showed a very weak correlation compared to other

acids. Glucose yields from Group 3 (samples with low acid levels) were also not affected by

individual organic acids, but this was understandable since acids levels were negligible.

Although glucose yields from Group 1 and 3 had no correlations with individual organic acids,

Group 1 had significantly higher glucose yields than Group 3 when excluding the sole storage

condition with high glucose yield in Group 3 (IA stover, 25% moisture at day 220) ( p = 0.006).

Otherwise there was no significant difference in the glucose yields of the two groups (p = 0.090).

120

Table 4.3: Differences in the three organic acid groupings and correlation of individual acids

with glucose

Group 1

(Relatively high acid level)

Group 2

(Moderate acid level)

Group 3

(Low acid level)

Storage duration Day 220 Day 220 Mainly Day 0*

Moisture 55 -75% 35 - 45%⁰ 25 -75%

Total organic acids (OA) 5 < OA < 10% 2 < OA < 5% < 2%

pH 4.07 - 4.78 4.00 - 5.12 4.31 - 6.83

Lactic0 -1.42 %

(0.218, P = 0.497)

1.33 - 4.95%

(0.534, P = 0.013)

0 - 1.16%

(-0.142 P= 0.409)

Acetic1.49 - 4.17 %

(-0.066, P = 0.839)

0.2- 1.37 %

(0.612, P = 0.003)

0 - 0.42%

( -0.243, p = 0.154)

Propionic0 - 0.46%

(-0.431, P = 0.162)0 (*) 0 (*)

Isobutyric0 - 2.41%

(0.239, P = 0.455)

0 - 0.59%

(0.226, P = 0.324)0 (*)

Butyric0 - 3.14%

(0.090, P = 078) 0 (*) 0 (*)

Lactic acid as % of total acids < 30% 57 - 84% 0 - 100%

Lactic: acetic ratio 0 - 0.7 1.5 - 9.8 0 - 9.0

Distinguished byHigh acetic acid levels and

all acids presentHigh lactic acid levels Little or no acid

Glucose yield

(% theoretical)

Moderate

55%: 17.38 ± 4.33

65%: 18.40 ± 0.87

75%: 15.11 ± 4.60

High

35%: 23.97 ± 1.05

45%: 21.88 ± 5.28

55%: 20.98 ± 2.19

65%:23.88 ± 0.13

Low

25%: 14.97 ± 9.25

35%: 12.82 ± 2.96

45%: 13.45 ± 4.33

55%: 9.73 ± 3.36

65%: 14.33 ± 5.86

75%: 13.35 ± 1.6

For glucose yield, percent before values is storage moisture

Highlighted cells indicate main acid under group

⁰ For IA stover, 55% and 65% moisture fall under group 2

*IA and PA Day 220 samples with 25% moisture fall under this group

(*) Independent variable (organic acid) is zero hence no basis for correlation

Values in bracket represent correlation coefficient with glucose yield from fiber reactivity test and corresponding p-values)

Lactic: acetic ratio used because no correlation between the two; in addition, acetic correlated to all the other acids

121

Although Group 1 has a high total acid concentration, the relatively low yields compared to

Group 2 cannot be attributed to the potential inhibitory nature of the acids since the samples were

thoroughly washed before hydrolysis. The pH values during storage (4.07 – 4.78), and the acid

profile of the high acid samples (in which volatile acids dominate with pKa of 4.76 – 4.88, see

Appendix C, Table C9) suggest that the effect of the Group 1 acids on fiber structure could be

negligible. This is because the level of dissociated acids, and hence protons to facilitate cleavage

of bonds, were lower in Group 1 than in Group 2. On the other hand, for the cluster of samples

with moderate total acid concentrations (Group 2), lactic acid with a pKa of 3.86 is dominant. In

this cluster the high pH relative to this pKa leads to a relatively high dissociation of lactic acid,

resulting in more hydrogen ions being available for the hydrolytic reaction that alters the

feedstock structure. This also may explain why lactic acid was the only acid correlated to glucose

yields. In addition, this explains why the IA stover, which produced more lactic acid during wet

storage, had higher glucose yields than the PA stover. The overall affect of these pKa – pH

relationships increases the natural pretreatment effect of the lactic acid dominated treatments in

Group 2, while a much lower effect is observed with the higher pKa acids that dominate in

Group 1. Although high concentrations of these volatile acids are present in Group 1, their

impact on glucose yield is minimal, with results similar to Group 3 which had little or no acid.

4.4 Conclusions

There is evidence from the fiber reactivity test conducted in this study that wet storage, without

any form of biological or chemical additives, does indeed have pretreatment capability. Although

cellulose accessibility was limited, due to the low hemicellulose degradation during storage, the

feedstock structure was altered enough to improve glucose yields over day 0 samples by a factor

of ~1.5 to 2.4. However, relative to conventional pretreatment and theoretical glucose yields,

these yields were low, indicating that wet storage of corn stover biomass would still require post

storage pretreatment to achieve desirable hydrolytic yields. The glucose yield after enzymatic

hydrolysis of wet stored samples was comparable across moisture except for the extreme

moisture levels tested (25% and 75%). Generally, storage at 75% moisture had the lowest

glucose yield. In the moderate moisture range from 35% to 65%, this similarity in yields also

minimizes the expected complexity and questions about quality given the varied moisture

conditions under which different farmers may harvest and store their biomass feedstock.

122

Cluster analysis comparisons indicated that moderate [total] acid levels corresponded with high

glucose yields and vice-versa, with significant but not very strong correlations. For wet storage

pretreatment to proceed, organic acids must dissociate to provide protons that will catalyze the

cleavage of bonds. Generally, samples with high total acid levels are high moisture samples (55 -

75%) with comparatively lower lactic acid amount, while samples with moderate acid levels had

comparatively higher lactic acid concentration. Lactic acid was found to be the most influential

acid because of its low pKa, which allows more dissociation at the relatively high pH typical of

corn stover silages.

With respect to post storage pretreatment, there was no significant difference between day 220

and day 0 samples likely due to the pretreatment conditions employed. It is probable that less

severe conditions would favor wet storage samples over day 0 samples. Also using fresh stover

instead of rewetted dry stover could present different outcome in favor of the former. The lack of

difference at lower enzyme loadings could be more of low reaction rate rather than maximum

yield that could be obtained from the two durations. For reactivity across duration without

pretreatment, yield after 21 days of storage were comparable. Aerobic samples at similar

moisture levels were more prone to microbial contamination that resulted in lower yields.

4.5 References

Chen, Y., R. R. Sharma-Shivappa and C. Chen. 2007. Ensiling agricultural residues for

bioethanol production. Appl Biochem Biotechnol 143:80–92.

Chesson, A. 1993. Chapter 14: Mechanistic models of forage cell wall degradation. In Forage






Mich.: ASABE.





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Elander, R. T., C. E. Wyman, B. E. Dale, M. T. Holtzapple, M. R. Ladisch, Y. Y. Lee, and T.

Eggeman,. 2005. Initial comparative process economics of leading pretreatment

technologies. Available at http://www.eri.ucr.edu/ISAFXVCD/ISAFXVPP/InCPELB.pdf





Heinrichs, J. and P. Kononoff. 2002. Evaluating particle size of forages and TMRs using the new

Penn State Forage Particle Separator. Pennsylvania State University, College of

Agricultural Sciences, Cooperative Extension DAS 02-42.

Jacobsen, S.E. and C.E., Wyman. 2000. Hemicellulose and cellulose hydrolysis models for

application to current and novel pretreatment processes. Applied Biochemistry and

Biotechnology (84–86):81–96.


Czako. 1987. Preservation of potential fermentables in sweet sorghum by Ensiling.




Mosier, N., C. Wyman, B. Dale, R. Elander, Y.Y. Lee, M. Holtzapple, and M. Ladisch. 2004.

Features of promising technologies for pretreatment of lignocellulosic biomass.

Bioresource Technology 96: 673–686. Available at http://i-

farmtools.org/ref/Mosier_et_al_2005.pdf Accessed 13 August 2009.

Mosier, N., R. Hendrickson, N. Ho, M. Sedlak, M.R. Ladisch. 2005. Optimization of pH

controlled liquid hot water pretreatment of corn stover. Bioresource Technology

(96):1986-1993



2009.

Oleskowicz-Popiel P., T. M. Lehtinen, J. E. Schmidt, A. B. Thomsen. 2010. Silage pretreatment

of corn stover to produce bioethanol. Journal of Industrial Microbiology &

Biotechnology: 1-16. Manuscript Number: JIMB-D-10-00268.

http://www.eri.ucr.edu/ISAFXVCD/ISAFXVPP/InCPELB.pdf





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Pakarinen, A., P. Maijala, S. Jaakkola, F. L. Stoddard, M. Kymäläinen and L. Viikari. 2011.

Evaluation of preservation methods for improving biogas production and enzymatic

conversion yields of annual crops. Biotechnology for Biofuels 4(20):1-13.





Ren, H., T. L. Richard and K. J. Moore. 2007. The impact of enzyme characteristics on corn

stover fiber degradation and acid production during ensiled storage. Applied Biochemistry

and Biotechnology 136-140: 221-238.



Stoddart, B. 2007. The structures of plant tissues and the effects of drying and fluid preservation

upon them. NatSCA News 11: 38-43. Available at

http://www.nhm.ac.uk/hosted_sites/natSCA/training/seminars/BS1.pdf Accessed on 23

July 2009.

Sun, Y., and J. Cheng. 2002. Hydrolysis of lignocellulosic materials for ethanol production: a

review. Bioresource Technology 83: 1-11.



biochemistry and biotechnology 148(1-3): 23-33.

Tímár-Balázsy, Á., and D. Eastop. 1998. Chemical principles of textile conservation.

Butterworth-Heinemann series in conservation and museology. Wosbon, MA. Elsevier

Science Ltd.



http://www.wilsoncenter.org/news/docs/Biomass%20ethanol.pdf Accessed 02

February 2009.


production. Int J Agric & Biol Eng 2(3): 51- 68.

http://www.nhm.ac.uk/hosted_sites/natSCA/training/seminars/BS1.pdf


125

Chapter 5

Impact of the organic acids produced during wet biomass storage on

pretreatment and bioconversion of corn stover to ethanol

Abstract

Organic acids produced during wet storage are recognized to be potentially beneficial at the

storage level, both for biomass preservation and to aid pretreatment. There are major concerns,

however, as to their impact on downstream processes especially microbial fermentation. This is

because organic acids have the capability to inhibit microbial metabolism or growth, which in

turn can affect biofuel productivity or yield. This study investigated the interaction of organic

acids produced during wet storage with subsequent pretreatment, as well as the potential for

interference with ethanol fermentation process. Interaction with pretreatment was observed by

measuring xylan and glucan removal and the formation of inhibitors. The results indicated that

organic acids generally do not impede downstream processes and in fact can be beneficial.

Levels of organic acids were generally below inhibitory levels, with a clear change in the acid

profile after pretreatment that showed inverse relationships in amounts of individual storage and

pretreatment acids. Whereas unensiled corn stover required 15 minutes of pretreatment to

optimize sugar release, ensiled corn stover could be treated equally effectively at either 15

minutes or at a lower pretreatment duration of 10 minutes. Furthermore, the different organic

acid profiles that accumulate at various storage moisture levels generally do not differ

significantly in their impact on downstream ethanol fermentation. Biorefineries, therefore, do not

need to be concerned with process adjustments to accommodate any supposed changes that

might result from different storage moisture conditions.

Key words: Acetic acid, biofuel, corn stover, ethanol, fermentation, hydrothermal treatment,

inhibitors, Liquid hot water pretreatment, wet storage, ensilage

5.1 Introduction

Wet storage, synonymous with ensilage, is the storage of biomass materials under anaerobic

conditions at moisture levels that permit acidogenic microorganisms to grow and produce

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sufficient quantities of organic acids to reduce pH to levels that inhibit microbial biodegradation

and allow long term biomass preservation. Wet storage thus contrasts with dry storage. In dry

storage moisture levels must be kept low enough to slow down and inhibit active microbial

activities/reactions. Traditional dry storage of biomass feedstocks in bales and other formats is

low cost and can be effective if the materials are kept dry, but carries the risk of spontaneous fire

outbreaks, narrows harvest windows (especially in humid climates), and can result in extensive

soil contamination during field drying operations. Wet storage systems can reduce these

concerns, and as indicated in the previous chapter can serve as an avenue for in situ pretreatment

of the biomass to enhance downstream biofuel fermentation processes (Linden et al., 1987;

Richard et al., 2001). With a mechanism similar to dilute acid pretreatment, organic acids

produced during wet storage could serve as long-term, mildly acidic, low temperature

pretreatment. Pretreatment of cellulosic feedstock is a major cost component of biofuel

production, so any reduction in this requirement is likely to have commercial value.

Previous studies on wet storage have either shown unfavorable impact, no impact, or had a

favorable impact on downstream processing with reference to the controls (Linden et al., 1987;

Henk and Linden, 1994; Chen et al., 2007; Digman et.al., 2007). However, in most prior studies

the biomass samples were microbially, enzymatically or chemically treated to enhance storage,

and few have looked at the natural ensilage process. In one study without additives, Thomsen et

al. (2008) investigated wet storage of whole-crop maize silage for ethanol production. Although

their results failed to demonstrate a pretreatment effect from ensilage, they did show that

subsequent pretreatment sugar yields as well as ethanol yield were remarkably improved as a

result of the ensilage process. However, the widespread applicability of this result could be

confounded by the high starch content of the whole crop (grain and stover) maize feedstock. The

differences among wet storage outcomes may be dependent on the feedstock type, and very few

studies have involved corn stover, which is the most abundant agricultural residue in the US.

Most importantly, none of these previous studies explicitly targeted the impact of storage organic

acids, or any modifications of these storage acids during pretreatment and fermentation. In a

number of these studies, the feedstocks were washed before subsequent processing, perhaps to

prevent interference of the acids with the downstream process. The cost of such washing and the

associated wastewater treatment would be hard to justify at a large commercial scale.

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Although wet storage of biomass has potential benefits for downstream processing to biofuels, it

is also known that most storage acids like lactic acid and acetic acid can inhibit microbial

activities (Lund and Eklund, 2000; Branen, 2002; Deublien and Steinhauser, 2008) and hence

negatively affect biofuel fermentations. Although no prior investigations were found on the

impacts of silage organic acids on biofuel production, their function in ensilage is to inhibit

microbial degradation, and there are ample examples in the food industry of organic acids

preserving food through organic acid inhibition or other antimicrobial effects (Lund and Eklund,

2000). Natural acid fermentations are used to preserve sauerkraut, pickles, yogurt and silage, but

unlike foods which will be digested in a mammalian gut, when silage is used as a biofuel

feedstock this acidic condition could serve as a potential impediment to downstream

fermentations to ethanol and other biofuels and biochemicals. There are a number of reports on

negative effects of organic acids with specific reference to ethanol-producing microbes

(Palmqvist et al., 1996; Koegel et al., 1997; Zaldivar and Ingram, 1999; Palmqvist and Hahn-

Hägerdal, 2000; Klinke at al., 2004; Knauf and Kraus, 2006). Several of these studies focus on

the yeast Saccharomyces cerevisiae, which is the most common microbe used in ethanol

fermentation. These studies showed the inhibitory effect of organic acid on ethanol-producing

microbes is dependent on the fermentation conditions, especially initial pH, extracellular-

intracellular pH gradient, temperature, the presence of other chemicals, and the type and amount

of organic acid present in both dissociated and undissociated forms. Importantly, Taherzadeh et

al. (1997), Thomas et al. (2002) and Torija et al. (2003) observed that the effect of organic acids

at low levels can sometimes be positive, stimulating growth of fermentative microbes and

ethanol production, and may be necessary for fermentation to proceed. For instance Taherzadeh

et al. (1997) observed that acetic acid could stimulate ethanol production during fermentation if

concentrations were lower than 0.05% (w/v) at pH 4.5. Torija et al. (2003) also observed that

organic acids commonly present in grapes were responsible for the completion of the

fermentation process as well as enhanced ethanol yields; none of the controls (i.e. without

organic acid of any sort) were able to ferment all the sugars within 21 days.

The main aim of this study was to directly and indirectly investigate the counteractive effects of

the organic acids produced during wet storage of corn stover; to understand how these acids

interact with the pretreatment process and the potential for reduced severity pretreatment as well

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as their effect on ethanol yields. Any observed potential for reduced severity pretreatment after

wet storage would be an indirect measure of the upstream pretreatment capability of ensilage

(interaction of organic acid produced during storage with structural bonds in feedstock in

storage). The potential for reduced severity could also result from the interaction of these organic

acids, acting as catalyst, with structural bonds in subsequent pretreatment process. Wet storage

could thus enhance downstream pretreatment. Acetic acid and other organic acids generated

during hydrothermal pretreatment are recognized as catalyzing agents in enhancing water

ionization and cleavage of acetyl/hydrolysis of hemicellulose (Mosier et al., 2005a; Mohammad,

2008; Zheng et al., 2009). Organic acids, when compared to dilute inorganic acids (as used in

“dilute acid pretreatment”), can minimize degradation of hydrolyzed sugars to inhibitors that can

impact on subsequent ethanol yield. This has motivated investigations into the use of organic

acids (e.g. acetic, lactic, formic, and maleic) as catalysts in hydrothermal pretreatment (Kootstra

et al., 2009; Xu et al., 2009; Marzialetti et al., 2011). Organic acid interactions with post-storage

pretreatment could therefore be positive.

However, as noted earlier in this introduction, these acids can interfere with the fermentation

process. The downstream effects of these acids will also be directly determined by examining the

inhibitory nature of organic acids on ethanol fermentation. The use of unensiled, washed and

unwashed silage with and without liquid hot water (LHW) pretreatment extracts will provide

evidence of the transformation dynamics of these organic acids during common biofuel unit

operations, as well as the direct effect of the acids on ethanol fermentation.

5.2 Materials and methods


Corn stover, Pioneer brand 34A20, was obtained in 2008 from Idaho national Lab. The stover

was harvested in 2007 from the Boyd plot near Boone, IA and field-dried for about 3 -5 days,

raked, baled, transported to Idaho and stored indoors with a tarp cover to prevent dust

accumulation. Particular size was reduced to 1” minus (less or equal to 25.4 mm) in the early

summer of 2008. The particle size distribution was analyzed using the Penn State Forage Particle

Separator, as described in Heinrichs and Kononoff (2002). About 26% of particles were greater

than 19 mm, 26% were between 8 and 19 mm, 31% were between 1.18 and 8 mm and 17% were

129

less than 1.18 mm. This biomass feedstock was referred to in previous chapters of this

dissertation as the IA stover.

The corn stover had an initial moisture content of about 7% and was adjusted to six different

moisture levels, 25% to 75%, to initiate these wet storage experiments. Moisture adjustment was

accomplished by spraying with appropriate amount of water and leaving the samples overnight

to be thoroughly absorbed into the fibers. The moisture adjustment was within ±2 percentage

units of the target moisture level. Corn stover was stored at a bulk density of about 159 dry

Kg/m3 in 1 pint (0.00047 m

3) glass canning jars that were tightly sealed to create anaerobic

conditions. Storage duration was 220 days at 37oC and ambient temperature, which was

approximately 23 ± 1oC. Experiments were performed in triplicates. After storage, samples were

dried in a HotPack convection oven at 55oC, ground using a 2 mm screen on a Wiley Mill

(Model 4, Thomas Scientific, Swedesboro, NJ) and stored at room temperature in sterile airtight

Whirl-Pak bags (Nasco, Fort Atkinson, Wisc.) prior to pretreatment. Composition of the

feedstock before and after storage was in accordance with the NREL standard protocols and as

determined in Chapter 3.

5.2.2 Organic acid measurements and pretreatment

Collection of soluble extracts and measurements of pH and organic acids in the feedstock after

storage were performed in accordance with the methods described in Chapter 3. The pH of

pretreatment extracts was also determined using SevenEasy S20 pH meter (Mettler-Toledo

International Inc, Columbus, OH).

The impact of organic acids on liquid hot water (LHW) pretreatment requirements was

investigated using washed and unwashed samples of dry ground ensiled (Day 220) and unensiled

(Day 0) corn stover. Ensiled samples are the moisture adjusted stover stored under anaerobic

conditions, in this case for 220 days, while the unensiled samples are the corresponding day zero

samples. Washed samples were washed with deionized water using the Dionex Accelerated

Solvent Extraction 350 (ASE 350) (Thermo Fisher Scientific Inc., Dionex ASE 350, Sunnyvale,

CA) system set at 40oC with three static cycles of 10 minutes, 100% flush, and a purge time of

200 seconds for 66 ml cells. The purpose of washing was to remove all organic acids produced

130

during storage in order to prevent any involvement or interaction with the pretreatment

procedure. In this way, the washed samples served as control against which unwashed samples

were compared to assess the impact of organic acids on the pretreatment process. Washed

samples would also indicate whether any change in pretreatment outcome is as a result of acid

interaction during storage or acid interaction during pretreatment when compared to unwashed

samples of unensiled and ensiled stover respectively. Only 37oC samples were washed for

comparison. See Appendix D, Figure D1 and Table D1, for a flowchart of sample processing and

analysis and an overview of the experimental design.

Liquid hot water (LHW) pretreatment of samples was also accomplished using the ASE 350

equipment, with each sample replicated four times. LHW pretreatment is a well established and

effective strategy that involves heating water-saturated or moist feedstock at high temperatures

(160 -220oC) under high pressure to maintain the liquid state for a couple of minutes without any

chemical additives. Optimum conditions reported by Mosier et al. (2005b) for controlled pH

LHW pretreatment were 190oC for 15 minutes, and these conditions were used as the benchmark

for reduced severity comparisons. This standard pretreatment condition with the ASE was

defined as 190oC, 1 static cycle of 15 minutes, 0% flush volume and a purge time of 120 seconds

for 10 ml cells, using deionized water. Each 10 ml ASE cell was filled with 1.5 dry gram of

sample. Solid loading was 14 – 20% and 13 – 15% for unwashed and washed samples

respectively. The solid loading is the percentage of dry solids to total liquids after pretreatment.

The variability in solid loading is subject to the amount of water added by the ASE 350 during

the filling and heating stage. The potential for reduced severity pretreatment was investigated by

comparing shorter retention times (5 and 10 minutes) with the standard 15 minutes at 190oC.

Pretreatment extracts, 500 μl each, were diluted 30-fold and filtered through 0.2 μm PTFE

syringe filters prior to analyzing for inhibitors (5 -Hydroxymethyl furfural (HMF) and furfural)

and organic acids using Ion Exclusion Chromatography (ICE) (Thermo Fisher Scientific Inc.,

Dionex ICS 3000, Sunnyvale, CA). Separation was performed at 30oC using IonPac ICE-AS1

guard (4 x 50 mm) and analytical (4 x 250 mm) columns with 100 mM methanesulfonic acid

eluent at a flowrate of 0.16 ml/min. Inhibitors and organic acids were detected with a photodiode

131

array detector (Thermo Fisher Scientific Inc., Dionex UVD 340U, Sunnyvale, CA) at

wavelengths of 270 nm and 210 nm respectively.

In addition to inhibitors and organic acids, xylan and glucan removal during pretreatment was

also determined. Xylan removal, a proxy for hemicellulose hydrolysis, was used as a

comparative indicator of the relative effectiveness of the different pretreatment conditions. To

measure the removal of these sugar polymers, the pH of the extracts was first measured using a

pH meter (Mettler-Toledo International Inc, SevenEasy S20, Columbus, OH).

Based on the pH range, 52.3 μl of 72% w/w sulfuric acid (Sigma–Aldrich, St. Louis, MO) was

added to 1.5 ml of each extract to obtain a final concentration of 4% sulfuric acid in 10-ml

autoclave safe bottles. The pretreatment extracts had pH levels greater than 3.5 but less than 5.

For this pH range, the hydrogen ion concentrations had significant digits at the 4th or 5th

decimal place. As a result, volume of acid was practically the same. See Appendix D, Equation

D1 for formula used in calculating amount of acid to add. Bottles were tightly covered using

rubber stoppers with crimped aluminum seals and placed in autoclave, together with sugar

recovery standards, at 121oC liquid setting for 1 hour. The acid-hydrolyzed extracts were filtered

through 0.2 μm PTFE filters and diluted 400-fold. Monosaccharide composition was determined

using Dionex ICS 3000 Ion Exclusion Chromatography. Separation was by high pH anion

exchange at 30oC using CarboPac PA20 guard (3 x 30 mm) and analytical (3 x 150 mm)

columns with 2 mM sodium hydroxide (NaOH) eluent at a flow rate of 0.5 ml/min. Detection of

the monosaccharides was by pulsed amperometric [electrochemical] detection at gold working

electrodes, using a quadruple waveform. Xylan and glucan removal were calculated from xylose

and glucose concentrations, using conversion factors of 0.88 and 0.90 respectively. Equation

5.1 was used in calculating xylan removal. For glucan removal, the various xylan components

were replaced by glucan.

( )

5.1

Where

= Mass of xylose in pretreatment extract (g)

132

= Xylose degraded during storage (% dry matter)

XDM = Original xylan content of corn stover before storage (% dry matter)

DM = Dry mass of corn stover that was pretreated (g)

5.2.3 Simultaneous fermentation and saccharification

After pretreatment the solids content of each pretreatment cell was directly transferred to a 50-ml

centrifuge tube for fermentation. Pretreatment extracts were collected separately during the

extraction process. For each storage condition investigated, two replicates were fermented with

pretreatment extract and two without extract. The pretreatment extract contains most of the

inhibitory compounds, which are soluble. Thus there were two steps in the overall process when

inhibitors could be separated, first when washing samples after wet storage but before

pretreatment, and second when extracting liquids after pretreatment. These two separations (or

their absence, for the “unwashed” storage samples and “with extract” fermentation treatments)

make it possible to determine the impact of organic acids and other inhibitors formed during

storage and/or pretreatment separately with respect their contribution to fermentation inhibition.

Although the wet storage organic acid profile may transform during pretreatment, the new post-

pretreatment organic acid profile is assumed to be influenced by (or a product of) the acids

produced during storage.

Simultaneous fermentation and saccharification (SSF) was carried out in tightly sealed 50-ml

centrifuge tubes. Samples fermented with pretreatment extract had a solids loading of 8.4% ±

0.1%, while washed and unwashed samples fermented without extract had a solids loading of

5.2% ± 0.1%. The solid loading was calculated as the ratio of dry mass of feedstock used in

fermentation to the mass of total fermentation liquids. For samples without extract, some solids

were lost in the pretreatment extract resulting in the lower solid loading. The fermentation broth

contained the following components prepared in a cocktail before addition: Penicillin-

Streptomycin at a final concentration of 30 μg/mL (0.1% v/v) to prevent microbial growth; citric

acid buffer (pH 4.5) at 0.05M to maintain a pH of 4.8, which is in the optimum range for enzyme

activity; Yeast peptone (YP) as a microbial nutrient at 1% broth volume; commercial cellulase

(Spezyme CP, Genencor, Rochester, NY) at 15 filter paper units (FPU)/g glucan complemented

133

with a commercial β-glucosidase (Novozyme 188, Novozymes A/S, Bagsvaerd, Denmark) at 60

cellobiase units (CBU)/g glucan. The microorganism used for fermentation, Saccharomyces

cerevisiae NRRL Y-2034, was obtained from the USDA ARS culture [NRRL] collection.

Saccharomyces cerevisiae Y-2034 is a wild type 6-carbon sugar fermenting yeast. The yeast was

grown in YPD media (10 g/L yeast extract, 20 g/L peptone, and 50 g/L dextrose) for about 24

hours after which the cells were centrifuged at 4200 rpm for 5 minutes. The supernatant was

discarded and the cells were washed in 1× PBS (Phosphate Buffer Solution: 138 mM sodium

chloride, 2.7 mM potassium chloride, 12 mM sodium and potassium phosphates, pH 7.4). After

washing, cells were resuspended in PBS and used as fermentation inoculant. Each tube was

inoculated with appropriate volume of inoculant to obtain an initial OD600 of 0.5. Fermentation

tubes were vortexed for ~ 5 seconds to mix contents before incubation for 72 hours. The

fermentation temperature and agitation, 35oC and 110 rpm, were achieved using a lateral motion

hot water bath. However, the vertical placement of tubes in the bath did not provide the complete

mixing intended by the agitation. Tubes were therefore removed twice (every 24 hrs) within the

fermenting period and inverted couple of times to mix contents. Control samples included

enzyme-yeast blanks and Avicel (α-cellulose). At the end of the fermentation period, samples

were centrifuged and the supernatant collected in appropriately labeled micro-centrifuge tubes.

The supernatant from each fermentation broth was diluted 9-fold and analyzed for ethanol using

the YSI 2700 SELECTTM

biochemical analyzer (YSI Inc., Yellow Springs, OH) with 2%

precision.

5.2.4 Data analysis

Results were analyzed using statistical tools such as analysis of variance (ANOVA) and

regression analysis. All statistical tests were conducted using Minitab 14 (Minitab Inc., State

College, PA) at a significance level, α, of 0.05.

5.3 Results and discussion

5.3.1 Pretreatment pH

After pretreatment the pH of the biomass feedstock generally decreased relative to the pH before

pretreatment (Appendix D, Table D9). This result was expected, and could be attributed to

134

deacetylation of xylan at high temperatures leading to the formation of acetic acid. This acid in

turn interacts with the pretreatment process by serving as hydrolytic catalyst, providing free

protons. This change in pH can thus indirectly indicate LHW pretreatment activity. Compared to

unensiled stover, smaller differences were observed between pH after wet storage and

subsequent pretreatment pH of unwashed ensiled feedstock. The average difference for unensiled

feedstock was 2.2 pH units while feedstock ensiled at 23oC and 37

oC had average differences of

0.08 and 0.18 pH units respectively. See Appendix D, Table D9.Two factors – hemicellulose

degradation during storage and the buffering capacity of organic acids – are likely responsible

for the smaller differences in ensiled feedstock. From Chapter 3, it was observed that on

average, 10% of hemicelluloses were degraded after 220 days of storage. The acetyl groups

constitute 3.8 -5% of the stover, and are the most susceptible components to the low temperature

acid hydrolysis that occurs during ensilage. This was evident from the large amount degraded, up

to 55% reported in Chapter 3. This implies that fewer acetyl groups would be available for

conversion to acetic acid during pretreatment. Alternatively, organic acids present in ensiled

samples, up to 9.1% compared to less than 0.5% for unensiled samples, could serve as buffering

agents in resisting pH change. This second factor is marginally supported by the relatively larger

pH change in washed ensiled samples compared to unwashed samples. Without storage organic

acid interfering with pretreatment, the decrease in storage pH of 0.34 mean pH units during

pretreatment of washed samples was still smaller than that of unensiled feedstock indicating a pH

limit in the vicinity of pH 4. The pH of washed samples were generally lower (~4.08) than

unwashed samples (~4.24) (p = 0.001).

Although the pH of unensiled samples decreased more dramatically during pretreatment, mean

resultant pH values were still generally higher (4.44 ± 0.17) than for the ensiled samples (4.26 ±

0.16) (p < 0.0001). From Table 5.1, it can be inferred that this drastic decrease in pH was a result

of more acetyl in the unensiled feedstock, which was then available for hydrolysis to acetic acid

during pretreatment. Generally, pH decreased with increased pretreatment time. For unensiled

samples the pH at all three time levels (5, 10, 15 minutes) were significantly different from each

other (4.64 ± 0.09, 4.42 ± 0.08, 4.27 ± 0.06 respectively; p < 0.0001) and feedstock moisture had

no significant impact. For ensiled samples, there was no significant difference between 10 and

15 minutes (4.23 ± 0.10 and 4.18 ± 0.16), both of which were lower than 5 minutes (4.37 ±0.14)

135

(p < 0.0001). With respect to storage moisture, there was no significant difference in pH at all

moisture levels except for 25% moisture, which was higher from 45% and 55% moisture (p <

0.0001). There was also no significant impact of storage temperature on pretreatment pH.

Generally, the pH values were moderate and conducive for both enzymatic hydrolysis and

ethanol fermentation.

Table 5.1 Relating hydrogen ion concentration to acetyl hydrolysis during pretreatment

Before Pretreatment After Pretreatment Hydrogen ions from pretreatment

([H+]after - [H+]before)

pH [H+] pH [H+]

Ensile 4.38 4.16869E-05 4.26 5.49541E-05 1.32671E-05

Unensile 6.67 2.13796E-07 4.44 3.63078E-05 3.6094E-05

Ratio ([H+]Unensiled /[H+]Ensiled)

2.72

Acetyl content* Unensiled has 122% more acetyl than ensiled stover

100% would implies twice the hydrogen ions compared to ensiled

stover, if all acetyl is removed during pretreatment of unensiled stover

22% would imply 0.44

Expected ratio ** ([H+]Unensiled /[H+]Ensiled)

2.44

The actual ratio of 2.72 is greater than the expected 2.44

This is reasonable since acetyl is not the only component in hemicellulose that is hydrolyzed to acids

Hemicellulose degradation during storage implies less of the other acid types too

* Up to 55% acetyl was degraded during storage (Chapter 3). This upper limit is used in calculation ** Expected ratio assuming all of hydrogen ions produced during pretreatment were from acetyl (from hemicellulose) hydrolysis to acetic acid

5.3.2 Glucan and xylan removal

Glucan removal during pretreatment in unensiled stover was ~58% higher than ensiled (4.49% ±

0.65 vs 2.84% ± 0.56; p <0.0001) and storage temperature had no significant impact on amount

removed (p = 0.157). See Tables D2 and D3 in Appendix D for data on glucan and xylan

136

removal at 23oC and 37

oC. These removal values are based on percentage of the total glucan (or

xylan) polymer in the feedstock and were calculated using Equation 5.1. Pretreatment time also

had no significant impact on glucan removal (p = 0.742 ensiled, 0.525 unensiled). Similarly,

xylan removal from ensiled stover was not significantly different for the various pretreatment

times (p = 0.210), with the results indicating 5 minutes (27.67% ± 3.29) was just as effective as

15 minutes (28.68% ± 2.08). This result may have important commercial implication if the

assumption that xylan removal reflects the extent of pretreatment is valid. In contrast, xylan

removal from unensiled stover was significantly lower after 5 minutes of pretreatment (22.23% ±

2.06) compared to 10 (26.62% ± 2.31) and 15 minutes (27.30% ± 1.12) of pretreatment (p <

0.001). At the longest retention time of 15 minutes, xylan removals for ensiled and unensiled

stovers were not significantly different from each other. This result suggests as retention time or

pretreatment severity increases wet storage effects on pretreatment are masked, as previously

observed in Chapter 4. Ensiled stover had significantly higher xylan removal, about 28% on

average, compared to 25% for unensiled samples (p < 0.0001). The minimum xylan removed

was 17% and maximum was 34%.

When considering the effect of storage moisture content on glucan removal, the results indicated

samples ensiled at 45 - 75% moisture and subsequently pretreated were not significantly

different from each other but lower than samples ensiled at 25% and 35% moisture. For

unensiled samples, pretreatment of samples in the range of 25% - 55% moisture did not

experience significantly different glucan removal. With respect to xylan removal, the effect of

moisture was only observed in the ensiled samples. At 23oC, xylan removal was highest at 35%

moisture (30.84% ± 2.48) although this difference was only significant relative to the 25% and

65% moisture samples, and both of these treatment conditions were not significantly different

from other moisture levels (p = 0.007). At 37oC, xylan removal at 45% and 55% moisture was

only significantly higher than the 35% moisture treatment. These results showed storage

temperature had some effect on xylan removal and revealed a significant interaction with storage

moisture (p < 0.0001). Generally, samples stored at 23oC experienced more xylan removal

during pretreatment than samples stored at 37oC (27.58% ± 3.17 vs 25.05% ± 3.21; p = 0.001).

The lower average xylan removal rate after ensilage at 37oC could be biased by a few samples at

137

the extremes of the storage moisture range, which had no lactic acid or lower lactic acid at 37oC

compared to 23oC.

Washing ensiled samples appeared to increase xylan removal. For example, during pretreatment

for 15 minutes the washed ensiled samples experienced more xylan removal than the unwashed

ensiled samples, 36.36% ± 4.56 and 23.89% ± 2.73 respectively (p < 0.0001). Since washed

samples do not contain organic acids, the implication is that contribution of organic acids to

pretreatment is primarily during the storage process, rather than during the subsequent

conventional pretreatment process. Although organic acids accelerate xylan removal, they may

also interfere with and limit xylan removal during conventional pretreatment. This acceleration

and limitation was observed in data on the amount of xylan removed in 5 minutes compared to

15 minutes. Under these circumstances xylan removal did not increase with time, although

removal from the washed samples indicated more xylan was potentially available. Despite this

apparent interference, xylan removal in wet storage samples was still better [at shorter retention

time] or comparable [at longer retention time] to unensiled samples as previously discussed.

5.3.3 Organic acids and inhibitors from pretreatment

Organic acids

The main acids identified in the pretreatment extracts of unwashed corn stover feedstock were

lactic (≤ 4.2% DM), acetic (≤ 2.2% DM), and isobutyric (≤ 3.9% DM) acids (see Figures 5.1 and

5.2). Individually, none of these acids exceeded 0.6% mass per pretreated volume. Low levels of

tartaric, malic, formic, pyruvic were also detected. Wet storage and pretreatment conditions

affected which acids were dominant, and these were different for different conditions. Lactic

acid was the dominant acid in wet stored and pretreated feedstock (2.94% ± 0.81 [Day 220] vs

0.14% ± 0.31 [Day 0]) while acetic acid was dominant in Day 0 samples (1.06 ± 0.31 [Day 0] vs

0.64 ± 0.33 [Day 220]). Isobutyric was equally dominant after pretreatment in before and after

storage samples (1.61 ± 0.91 [Day 220] vs 1.42 ± 0.75 [Day 0]).

138

Figure 5.1: Main pretreatment acids in the extracts of unwashed stover at the various

pretreatment retention times. Solid circle = Mean; open circle = Individual values; ˟ = Outliers;

Rectangle = Range box

As shown in Figure 5.3, the amount of lactic acid generally increased during pretreatment while

acetic acid amount decreased. Samples with lower lactic acid (0.0 to < 1.50%) prior to

pretreatment generated more lactic acid (up to 4.22%) and samples with higher lactic acid (1.94

to 3.20%) generated less (< 1.40%). Lactic acid could have been produced through hydrothermal

deamination and hydroxylation of amino acids, or in small amounts via hydrothermal

degradation of polysaccharides (Quitain et al., 2002; He at al., 2007). Another potential source of

lactic acid generation may be a reaction of acetic acid formed during storage with formaldehyde

produced during pretreatment (see Equation 5.2).

5.2

Org

an

ic a

cid

(%

dry

ma

tte

r)

Storage

Prt time

Isobutyric Acetic Lactic

UnensiledEnsiledUnensiledEnsiledUnensiledEnsiled

151051510515105151051510515105

4

3

2

1

0

Acetic acid + Formaldehyde → Lactic acid

139

At standard conditions, this reaction is spontaneous. This pathway is conjectured based on the

disappearance during pretreatment of some of the acetic acid present after wet storage after

pretreatment. Also, compared to washed samples with no storage acids, hence no acetic acid, the

amount of lactic acid generated during pretreatment was less than the amount produced during

storage, which was washed out before pretreatment. In effect, there was a decrease in lactic acid

during pretreatment, not an increase as in unwashed samples. Lactic acid in the pretreatment

extract of washed samples was in general less than 0.5%. Formaldehyde, assumed in Equation

5.2 as reacting with acetic acid, can be produced from thermohydrolytic degradation of xylose

(Schafer and Roffael, 2000; Roffael and Huster, 2012).

About half of the unwashed wet storage samples with acetic acid lower than 1% (dry basis, d.b.)

[generally in samples from 25 – 45% moisture] showed a percentage point decrease (up to 0.9%

d.b.) in the original amount after pretreatment and the other half showed an increase (up to 0.8%

d.b.) above the wet storage amount. In contrast, wet storage samples with acetic acid

concentrations greater than 1% to 2.8% [55 – 75% moisture] decreased up to 2.3% (d.b.).

Isobutyric acid in pretreatment extracts was high, and was not significantly different for both

Day 0 and Day 220 samples. Generally there was an increase above the initial wet storage

amount (up to 3.1% d.b.) except for the retention time of 5 minutes, where 65% and 75%

moisture samples showed a decrease in isobutyric acid.

In the pretreatment extracts from washed samples, malic acid was dominant (2.11% ± 1.73 DM),

far more than acetic and lactic acids which averaged ≤1% and ≤0.51% respectively. Formic acid

amounts were comparable to lactic acid. Traces of succinic acid were observed in the 75%

moisture samples. No isobutyric acid was present in pretreatment extracts from any washed

samples. This lack of detection of isobutyric acid in washed samples contrasts with the relatively

high levels of isobutyric acid in the unwashed samples after pretreatment. The increase in the

isobutyric acid concentration of unwashed samples could thus be due to interactions of organic

acids or other extractives washed out of the feedstock. Unlike acetic and lactic acids, isobutyric

acid is usually not reported in studies of hydrothermal processing of lignocellulosic materials,

and a specific abiotic reaction that could occur during pretreatment has not been elucidated.

140

Figure 5.2: The dominant three acids in the pretreatment extract from various storage moisture levels, unwashed, and subjected to

three pretreatment retention times (See Appendix D, Tables D4 and D5, for other acids present in pretreatment extract)

141

Figure 5.3: Change in amounts of organic acids from unwashed wet stored samples and total amount of acids in pretreatment extracts

Changes in organic acid profile are percentage point difference represented by ‘acids present in pretreatment extract (%) - storage acids (%)’, using mean values,

all on a dry matter basis. Negative values indicate the amount of acid after pretreatment was less than the amount in the wet storage sample prior to pretreatment.

Total acids are also read from the same axis and include other minor acids present in the pretreatment extract.

-4

-2

0

2

4

6

8

25 35 45 55 65 75 25 35 45 55 65 75 25 35 45 55 65 75 25 35 45 55 65 75 25 35 45 55 65 75 25 35 45 55 65 75 25 35 45 55 65 75 25 35 45 55 65 75 25 35 45 55 65 75

Chan

ge in

org

anic

acid

pro

file

afte

r pre

trea

tmen

t (%

)


ΔLactic ΔAcetic Δisobutyric Total pretreatment acid

5 minutes 10 minutes 15 minutes

Day 220 (Ensiled)Day 0 (Unensiled)

5 minutes 10 minutes 15 minutes 5 minutes 10 minutes 15 minutes

23C 37C

142

Total organic acid in pretreatment extracts were not significantly different at the various

pretreatment retention times (p = 0.353) and various storage moisture levels (p = 0.306).

However, wet storage did have an impact. As observed in Chapter 3, organic acids were

virtually absent in control (Day 0) samples. These unwashed Day 0 samples behaved similarly

to the washed wet stored samples, generating more acids during pretreatment than unwashed wet

stored samples. On average, however, total acids from pretreated unwashed wet stored feedstock

were still higher (~ 6.54% DM) than for unwashed Day 0 samples (~ 4.46% DM) (p < 0.0001).

The unwashed samples also showed the apparent changes in the organic acid profile after

pretreatment. Butyric acid, present in a number of unwashed high moisture samples in amounts

greater than 1% DM, disappeared completely. Individually, amount of lactic acid and acetic acid

in ensiled samples were not significantly different at the various pretreatment times (p = 0.405

and 0.118 respectively). For Day 0 samples, the lactic acid concentration was not different for

the various pretreatment times (p = 0.642). However, the acetic acid concentration at 15 minutes

was significantly higher than at 5 and 10 minutes (p < 0.0001). With respect to temperature,

samples stored at 23oC generally had lower lactic acid (p = 0.001) but more acetic (p = 0.002)

than 37oC samples after pretreatment. Based on the amount of storage acetic and lactic acids at

23oC and 37

oC (Chapter 3), a similar inverse relation as describes in earlier paragraph was

observed, in which samples with relatively lower initial [storage] acids had higher amounts

generated during pretreatment than samples with higher initial [storage] acids and vice-versa.

Inhibitors

In addition to organic acids, two sugar degradation products 5-(Hydroxymethyl) furfural (HMF)

and furfural were measured in pretreatment extracts. The former derives from hexoses and the

later from pentoses, and both are known to inhibit many ethanologens including yeast.

Generally, both of these inhibitors were higher in ensiled samples compared to unensiled

samples and both were not affected by storage temperature (p > 0.5). On average, HMF

concentrations were less than 0.05% (0.039% ± 0.018) on a dry matter basis (d.b.) and furfural

concentrations were less than 0.5% (d.b.) (0.47% ± 0.27) in unwashed ensiled samples. These

HMF and furfural concentrations were about 30% and 75% more respectively than the amounts

in unensiled samples. The ratio of xylan removed to glucan removed was approximately the

143

same as the ratio of furfural to HMF produced, and this ratio was on the order of ~10. On a per

[pretreated] volume basis, concentration of HMF and furfural were 0.03 ± 0.05 g/L extract and

0.48 ± 0.32 g/L extract respectively. Tables D6 and D7 in Appendix D show the concentrations

at various moisture and pretreatment times.

In contrast to unwashed samples, washed samples had no HMF in the pretreatment extracts, but

far higher furfural concentrations than were observed in the unwashed samples, averaging 1.12%

± 0.26 (d.b.) or 1.5 g/L extract at 15 minutes retention time. See Appendix D, Table D2. Glucan

removal during pretreatment from washed samples was higher than that from unwashed samples

(3.28% DM ± 0.38% vs 2.81% DM ± 0.43%, p = 0.017). This higher glucan removal was

expected to result in larger amounts of HMF in the pretreatment extracts from the washed

samples, but this was not the case. This suggests that HMF was produced from degradation of

preexisting glucose in the water soluble components of corn stover or glucose hydrolyzed during

wet storage rather than from the glucose produced from structural degradation during the

pretreatment process. This result thus supports the hypothesis that decomposing valuable

feedstock components to simpler, more bioavailable forms during wet storage increases their risk

of being degraded in subsequent processing to less valuable forms. Alternatively, it is possible

that other water soluble compounds in the stover or produced during ensilage may catalyze

glucose degradation or serve as reaction partners in the formation of HMF in unwashed samples.

Their absence in the washed samples would therefore hinder the formation of HMF. The higher

furfural could be attributed to the higher xylan removal from washed samples (p < 0.0001). At 15

minutes pretreatment retention time xylan removal was ~36% and ~24% of theoretical for

washed and unwashed samples respectively. Furfural generated from the pretreated washed

samples previously stored under the extreme moisture conditions (25% and 75%) were lower [<

1%] than amounts produced during pretreatment of washed samples from the mid-range 35% -

65% moisture wet storage samples, which were not significantly different from each other.

The amounts of both the HMF and furfural inhibitors increased with pretreatment time as

expected. For unensiled feedstock, amounts of HMF and furfural generated at each pretreatment

retention time were significantly different from each other (p < 0.05) (See Table 5.2 and Figure

5.4). In wet stored stover, HMF generated during 5 and 10 minutes of pretreatment were not

144

significantly different and were lower than the amount generated in 15 minutes. Furfural,

however, was different for all pretreatment times. For the unensiled versus wet stored stover,

there was more than a 100% and a 60% increase in furfural respectively, for every 5 minutes

increase in pretreatment time. Furfural produced during pretreatment was not significantly

different for the various storage moistures. HMF was also not significantly different except for

45% moisture ensiled stover samples (which had higher amounts than observed for the 65%

samples) and 35% moisture (which had higher amounts than observed for the 75% moisture

samples of unensiled stover).

Table 5.2: Furfural and HMF generated during liquid hot water pretreatment of unwashed corn

stover

Pretreatment

time (minutes)

Furfural (% dry matter) HMF (% dry matter)

Ensiled Unensiled Ensiled Unensiled

0 0† 0

† 0

† 0

†

5 0.229 ± 0.124 0.079 ± 0.019 0.027 ± 0.016 0.015 ± 0.006

10 0.414 ± 0.080 0.236 ± 0.027 0.036 ± 0.010 0.028 ± 0.007

15 0.670 ± 0.287 0.490 ± 0.062 0.052 ± 0.019 0.046 ± 0.013

p-value < 0.0001 < 0.001 0.001 < 0.001

Regression *

R2 ~ 0.84 ~ 0.86 ~0.63 ~0.72

* All intercepts set to zero. R2 was similar in zeroed and actual intercept except for unensiled furfural in which the

actual equation [(0.0411 x pretreatment time) - 0.1426] had an R2 of ~95%.

† Analysis of water soluble extracts collected before pretreatment showed there was no furfural or HMF present in

the feedstock before pretreatment.

145

Figure 5.4: Variation in Furfural and HMF with moisture and pretreatment time

5.3.4 Fermentation

Ensiled versus Unensiled

Generally, there was no significant difference in fermentation yields of unensiled (Day 0) and

ensiled (Day 220) unwashed stover. This was true whether the stover samples were fermented

with or without their pretreatment extracts (p: with extract = 0.745, without extract = 0.235) (see

Table 5.3). However, the pretreatment response of samples with or without wet storage was

different in terms of the dominate acids and inhibitors produced and xylan removed at shorter

retention times. Organic acids, furfural and HMF were higher in ensiled samples, and so was

xylan removal. The first group can adversely affect fermentation yields depending on their

concentrations and the pH of the fermentation broth, while xylan removal can favor ethanol

yields, for typical hexose fermenting yeast as were used in this assay. Ranges of the dominant

acids in the fermentation broths, on mass per fermentation volume basis, were 0.00 – 2.17 g/L,

0

0.2

0.4

0.6

0.8

1

1.2

25 35 45 55 65 75 25 35 45 55 65 75 25 35 45 55 65 75

5 5 5 5 5 5 10 10 10 10 10 10 15 15 15 15 15 15

Inh

ibit

or

gen

era

ted

(%

dry

mat

ter)

Ensiled HMF Ensiled Furfural Unensiled Furfural Unensiled HMF

146

0.00 – 1.07 g/L and 0.00 - 2.69 g/L for isobutyric, acetic and lactic acids respectively. Acetic

acid was dominant in unensiled feedstock while lactic was dominant in ensiled feedstock.

Acetic acid levels above 0.50 g/L lead to intracellular accumulation that could affect cell growth,

ethanol production or both, while lactic acid concentrations greater than 8.0 g/L could lead to

cell death depending on type of yeast and intracellular pH (Narendranath et al., 2001; Ingledew ,

2003). Although lactic acid concentrations in the present study were much lower than this 8 g/L

level, 92% of unensiled samples in this study had acetic acid concentrations greater than 0.50

g/L. The fermentation pH of 4.8 in this study is, however, higher than the pH, 3.0 to 4.0, in

Narendranath et al. (2001) and Ingledew (2003). Thus the effect of these acids on fermentation

microbes in the present study is expected to be less than in these prior studies, due to the lower

amount of undissociated acids. A more recent study conducted by Xu et al. (2010) found that

acetic acid, which is more inhibitory than lactic acid, only inhibited ethanol yields when the

concentration used in LHW pretreatment reached or exceeded 10% g acetic acid/g feedstock. In

addition, 0.51g/L furfural and 0.07 g/L HMF present in fermentation broth when 6% g acetic

acid/g corn stover was used during pretreatment, did not have any inhibitory effect on

fermentation yields. Ethanol yield was similar to that of control samples without acetic acid.

When acetic acid was less than 6%, concentrations of ethanol were higher than the control (up to

8.63 g/L compare to 7.63 g/L in the control) with maximum ethanol yield obtained at 1.0% -

3.0% g acetic acid/g corn stover, equivalent to 1.5 g - 2.5 g acetic acid/L fermentation broth. Xu

et al. (2010) observed that the amount of acetic acid increased above the amount used in

catalyzing pretreatment and the concentration in pretreatment liquor, when a maximum of 40% g

acetic acid/g feedstock was used, was about 23 g/L. Under these severe conditions, maximum

furfural and HMF concentrations were 1.74 g/L and 0.23 g/L respectively. Pretreatment liquors

were fermented directly without dilution and ethanol yield was about 0.50 g/L, about 93% less

than amount in the control. This level of inhibition was attributed mainly to the high acetic acid

concentration. Palmqvist et al. (1999) observed that acetic acid and furfural individually, at pH

of 5.5, stimulated ethanol yield when concentration was 0 -10 g/L and 0 -2 g/L respectively.

When both are present in the fermentation broth, there was an interactive effect that could be

negative depending on the concentration of each compound. For instance, when acetic acid was 8

g/L and furfural was 0.6 g/L, volumetric productivity and ethanol yield both increased by about

147

20% above the controls without acid and furfural. When acetic acid was 2 g/L and furfural was

2.4 g/L, volumetric productivity decreased by 70% but final ethanol yield was still 20% more

than the control. However, at acetic acid concentrations of 8 g/L and furfural at 2.4 g/L,

volumetric productivity decreased by 80% and final ethanol yield also decreased by 50%.

Larsson et al. (1999) also observed that at pH of 5.5, acetic acid up to 6 g/L increased ethanol

yield even in presence of more inhibitory acids like formic and levulinic acids. At a

concentration of 4.8 g/L, varying furfural concentration from 1.5 g/L to 2.5 g/L did not affect

final ethanol yield, although volumetric productivity was reduced with increasing concentrations.

In the present study, acetic acid, after pretreatment of unwashed samples, was generally less than

2% of the dry mass of corn stover. The maximum concentration in g/L fermentation volume was

1.1, which was observed for 75% moisture unensiled stover pretreated for 15 minutes. Only 8%

of the unensiled samples, which had higher acetic acid concentration than ensiled samples, had

acetic acid concentrations exceeding 1 g/L (at 65% and 75% moisture). Furfural and HMF

concentrations in unwashed samples were 0.346 g/L ± 0.03 and 0.028 g/L ± 0.002 respectively.

Although about 10% of the unwashed samples in this study had furfural levels exceeding 0.51

g/L, the maximum HMF was below 0.06 g/L. Maximum furfural concentration in unwashed

samples was 0.73 g/L for 55% moisture ensiled stover pretreated for 15 minutes. The implication

is that none of the potential inhibitors produced during pretreatment affected fermentation yield.

If a 30% solid loading (e.g. 30 g dry stover in 100 g liquid) is assumed during pretreatment and

is fermented directly without dilution, the concentrations of lactic, acetic and furfural would be

expected to increase by a factor of 1.5 to 2.3, since solid loading in this study was 13 to 20%.

Applying a 2.3 factor to maximum values in Table D8, in Appendix D, would imply maximum

concentrations of 12.03 g/L (at 55% moisture, ensiled, pretreated 5 mins) 6.03 g/L (at 75%

moisture, unensiled, pretreated 10 mins) and 2.90 g/L (at 55% moisture, ensiled, pretreated 15

mins) for lactic, acetic and furfural respectively.

Even at this higher solid loading, these concentrations in themselves may not contribute to

significant inhibition. Graves et al. (2006) observed that with 25% solids in corn ethanol

fermentation, Saccharomyces cerevisiae could tolerate 30 g/L of lactic acid. At 30% solids,

ethanol yield was still not affected when pH was 5 but dropped by about 13% at pH 4. Acetic

148

acid at 2 g/L increased ethanol production even with 30% solids and this was not affected by pH.

However, with 20% solids and pH of 4, a 4 g/L acetic acid concentration reduced ethanol

production by 50%. In contrast, when pH was increased to 5.5, ethanol production did not seem

to be affected by 8 g/L acetic acid. There are several factors that complicate the predictability of

the effects of these organic acids or furfural on ethanol yield under high solid loading. At high

solid loading, there are several other factors that could lead to lower ethanol yields, especially

when solid concentrations exceed 20%. These include osmotic stress due to higher sugar

concentrations (Darku and Richard, 2011); lower enzyme adsorption rates (Kristensen et al.,

2009); mass transfer limitations (Varga et al., 2004; Kristensen et al., 2009; Zhang et al., 2009);

and ethanol inhibition from higher ethanol concentration (Mohagheghi et al., 1992).

Disregarding these high solids limitations and applying a factor of 2.3 to values in Table D6

(Appendix D), similar yields can be expected from both ensiled and unensiled stover based on

the previously discussed observations by Palmqvist et al. (1999) and Larsson et al. (1999). This

is because although ensiled samples have high furfural, only 14% of these samples at 30% solid

loading would contain furfural greater than 2.4 g/L (39% would be less than 1 g/L) and

maximum acetic acid would be less than 4 g/L (51% were ≤ 2 g/L). At these concentrations, a

stimulating effect on ethanol yield would be expected. For unensiled samples, acetic acid levels

were higher than in ensiled samples. Yet even then, at a hypothetical 30% solids loading, while

89% of the samples would have concentrations greater than 2 g/L, only 4% would exceed 4.8

g/L. In contrast, furfural concentrations were lower than in ensiled samples; at 30% solids the

maximum concentration would be less than 2 g/L and 33% of the samples would have

concentrations less than 1 g/L. With limited understanding of the complex interactions of

inhibitors in fermentation broth coupled with the pH effect, it is possible that with higher solids,

ensiled and unensiled feedstock could respond differently, with one having better outcomes over

the other in terms of ethanol yield. While ethanol productivity will most certainly be affected by

15 minutes of pretreatment at higher solid loading, final yields may not be affected. Based on the

current analysis, the ethanol fermentation outcomes of ensiled and unensiled samples at high

solids would be balanced by lower acetic in the former and lower furfural in the later, likely

resulting in similar yields.

149

Since wide type Saccharomyces cerevisiae is exclusively a 6-carbon fermenting yeast, the

similarity in glucan composition of ensiled and unensiled samples also accounted for the

similarity in ethanol yields. The assumption here is that there was no glucose degradation after

structural decomposition during pretreatment. This assumption is based on the negligible

amounts of HMF generated during pretreatment. At high temperature, under acidic conditions,

glucans are degraded to HMF. In addition, washed samples did not have any HMF suggesting

that HMF was likely from free water extractable glucose, which in the case of washed samples

were washed out. The similarities in yields also suggest that although, statistically, xylan

removal was significantly higher in ensiled feedstock, it was not of practical significance.

Alternatively, it may be possible that glucan was more accessible in ensiled samples, but its

utilization was hindered by inhibitors leading to coincidental similarities in yields with the

unensiled samples. The following section, discussing fermentation yields with and without the

pretreatment liquids, should help evaluate this question.

150

Table 5.3*: Fermentation yields, on percent theoretical basis, at the different pretreatment retention times

Day 0 Day 220

Pretreatment time (min)

5 10 15 p-value 5 10 15 p-value

Unwashed

With Extract (23oC)

46.65 ± 1.91a 50.46 ± 2.10b 55.27 ± 1.60c <0.0001 44.00 ± 2.91a 49.43 ± 3.65b 57.59 ± 6.41c <0.0001

No Extract (23oC)

40.07 ± 1.82a 45.45 ± 3.59b 49.11 ± 3.71c <0.0001 41.29 ± 8.84a 48.11 ± 1.86b 50.31 ± 4.89b 0.002

With Extract (37oC)

38.58 ± 2.28a 41.91 ± 3.16ab 46.86 ± 11.29bc 0.021

Washed

With Extract (37oC)

33.25 ± 2.75a 43.13 ± 5.37b 41.56 ± 6.21b <0.0001

No Extract (37oC)

45.65 ± 6.62

P-value (down) <0.0001 0.001 <0.0001 <0.0001 <0.0001 <0.0001

*Same letter across indicate not significantly different; same color down indicates not significantly different

151

Fermented with pretreatment extract versus without extract

Ethanol yields for unwashed samples fermented with pretreatment extracts were significantly

higher than samples fermented without extract (p = 0.041) (see Table 5.3). For samples without

pretreatment extract, glucan removed during pretreatment was accounted for. If glucan removal

was unaccounted for, the yields from samples without extract would have been much lower. See

Appendix D, Table D12 for the amount of potential ethanol lost if extracts are discarded. This

may seem negligible from a mass perspective. Glucan removal was low during pretreatment and

on average and could theoretically have yielded 0.0073 g ± 0.0002 of ethanol or ~0.59 g/L ± 0.04

g ethanol/ L fermentation volume. In terms of theoretical ethanol yield, this could potentially be

up to 5% of the total ethanol that could be derived from the fermentation, if the extracts were

fermented too (see Appendix D, Table D12 and Equation D3). Yields of samples fermented with

and without extract were 50.57% ± 5.79 and 45.72% ± 6.01 or on a mass ethanol per

fermentation volume basis, 6.68 g/L ± 0.09 and 5.78 g/L ± 0.11 respectively. The lower yields of

samples without extract could be due to the absence of organic acids, which have been reported

by Taherzadeh et al. (1997), Larsson et al. (1999), Palmqvist et al. (1999), Thomas et al. (2002)

and Torija et al. (2003) to have a stimulating effect at low concentrations on ethanol fermentation

thereby increasing yields. Correlation and regression analysis (Appendix D: Tables D13 and

D14, Figures D2 and D3) show that acetic acid has a positive correlation and significant

regression relationship with ethanol yield in unensiled samples, but not with ensiled samples.

This lack of correlation may be a result of the lower acetic acid levels in the ensiled samples,

suggesting that there may be a lower limit below which acetic acid has no stimulating effect, just

as there is an upper limit as observed in other studies. Tables D13 and D14 also show that

furfural and HMF, at the concentrations found in unwashed stover in this study, were positively

and strongly correlated with ethanol yield. The regression equations for unensiled stover are

reasonable as they show that without furfural or HMF, ethanol yield was similar to the mean

yield in samples fermented without pretreatment extract, hence had no furfural or HMF. This

result supports the observation by Palmqvist et al. (1999) that furfural serves as an ethanol

stimulant when concentrations are ≤ 2 g/L. As noted in the previous paragraph, the organic acids,

HMF and furfural in the pretreatment extract were not likely to exert any inhibition to

152

fermentation and were responsible for the higher yield in samples fermented with pretreatment

extracts.

Storage at 23oC versus Storage at 37

oC

This section compares the ethanol yields of unwashed ensiled samples fermented with

pretreatment extract at different storage temperatures. Samples stored at 23oC had better yields

(as a percentage of theoretical ethanol yield) than samples stored at 37oC (50.34% ± 7.20 vs.

42.45% ± 7.53; p < 0.001). The difference, however, was due mainly to differences in yields at

the extreme moisture levels, 25% and 75% (see appendix D, Tables D10 and D11). At these

respective storage moistures, yields were 47.99% ± 5.72 vs 41.33% ± 3.76 and 49.12% ± 5.46 vs

32.88% ± 6.56 for 23oC versus 37

oC respectively. Although glucan composition before

pretreatment and glucan removal during pretreatment were similar for samples ensiled at these

two temperatures, xylan removal was significantly higher in samples stored at 23oC. In addition,

samples stored at 23oC had higher acetic acid and lower lactic acid content in the pretreatment

extracts than samples stored at 37oC. Thomas et al. (2002) observed that while both acetic acid

and lactic acid at low concentrations could enhance ethanol yields, given appropriate pH, acetic

acid was a better stimulant and resulted in more ethanol production while lactic acid resulted in

more cell growth than ethanol. At pH 4.5, increasing acetic acid concentration to 16 g/L did not

have much inhibitory effect on yields (Thomas et al., 2002). Acetic acid levels in this study were

lower and the pH of the fermentation media was approximately 4.8, so it is possible that this

stimulating effect of acetic acid was responsible for the difference in yields.

Washed versus unwashed samples

Samples stored at 37oC were analyzed for the effect of washing versus not washing before

pretreatment. Although unwashed samples fermented with the pretreatment extract had higher

percentage theoretical ethanol yields (42.45% ± 7.53) than washed samples (39.31% ± 6.55),

there was no significant difference between the two. This was in spite of the higher xylan

removed in the washed samples (~36%) compared to unwashed samples (~24%). The organic

acid profiles of these two groups were also very different, with the washed samples dominated

by malic acid which is less inhibitory than the isobutyric, lactic and acetic acids that were

dominant in the unwashed samples. However, washed samples contained more than twice the

153

amount of furfural found in unwashed samples. On a furfural mass per fermentation volume

basis, washed samples had 0.82g/L ± 0.19 furfural while unwashed had 0.35g/L ± 0.18 at a

pretreatment retention time of 15 minutes. The furfural concentrations in the washed samples

were not expected to inhibit ethanol production from discussion under fermentation of ensiled

and unensiled stover. It is, however, possible that higher levels beyond 0.51 g/L (Xu et al., 2010)

or 0.6 g/L (Palmqvist et al., 1999) could, in the presence of other compounds present in the

pretreatment extract. In one study, the growth rate of S. cerevisiae was not affected at furfural

concentrations ≤ 1 g/L and specific ethanol productivity (gram ethanol per gram feedstock per

hour, g/g/h) was not affected at 1.5 g/L. Even at a furfural concentration of 2g/L, while specific

ethanol productivity was significantly reduced the final ethanol yield was not (Boyer et al.,

1992). In the present study, fermentation was not allowed to proceed to completion, and that

could have partially masked any inhibitory effect of borderline inhibitory concentrations of

furfural, which are assumed to be responsible for the lower trending (but not significantly lower)

yields of washed samples. The inhibitory effect of these compounds is dependent on several

factors including pH, inoculation rate, substrate concentration, and the presence of other

compounds.

Comparing washed samples fermented with extract to those fermented without extract, it was

observed that although the yields of the latter were on average higher (45.65 % ± 6.62 vs 41.56%

± 6.21 at 15 minutes pretreatment time), the difference was also not significant (p = 0.132). For

the same pretreatment duration, washed samples without extract were also not significantly

different from unwashed samples fermented with extract (46.86% ± 11.29). The implication is

that although furfural may have some effect on fermentation at a concentration of 0.82g/L, this

effect is not pronounced.

Effect of storage moisture and pretreatment retention time

With respect to the main processing alternatives investigated in this study, storage moisture had

no significant influence on ethanol yield among all the samples of unwashed stover, both

unensiled and ensiled at 23oC, whether fermented with or without their pretreatment extracts.

Ethanol yields for washed stover, fermented with and without pretreatment extracts, were also

not significantly different for unensiled samples or ensiled storage at either temperature across

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all storage moisture conditions (see Figure 5.5). However, for samples ensiled at 37oC and not

washed before pretreatment, there was a storage moisture effect. Although yields for 75%

moisture samples [unwashed with extract] was not significantly different from 25% moisture,

they were lower than yields from all other moisture, averaging 37% of theoretical ethanol yield

compared to yields >43% of theoretical for samples in the intermediate moisture range (p =

0.008). At 37oC, samples at 75% moisture had high butyric acid, which disappeared during

pretreatment and could not have been responsible for the low yields. These samples were also

high in acetic acid and had no lactic acid prior to pretreatment, but the acetic acid was reduced

and the lactic acid generated during pretreatment. The only marked differences between the 75%

moisture samples and other samples were the presence of high storage butyric acid and the lack

of lactic acid. This suggests that lactic acid produced during storage, which was in a more

dissociated form than the other organic acids due to storage pH, could have favorably impacted

feedstock structure at other moisture levels. Although lactic acid at low concentrations can

stimulate fermentation, these results as well as observations from previous paragraph suggest

that lactic acid generated during storage is of more value than lactic acid generated by

pretreatment. As was also observed in Chapter 4, if extreme storage moistures are avoided, the

impacts of moisture on subsequent process outcomes are not significant.

In contrast to storage moisture, pretreatment retention time frequently had a significant impact on

ethanol yields. Generally, ethanol yields increased with pretreatment time. For unensiled

samples, with and without their pretreatment extracts, yields at the various pretreatment times

were significantly different from each other. Importantly, for ensiled samples, yields from the 10

minute pretreatment duration was not significantly different from 15 minutes, except for the

23oC unwashed samples fermented with extract. This suggests that ensilage may permit a

reduction of pretreatment severity without sacrificing yield, potentially reducing conversion

costs. These pretreatment time comparisons are detailed in Table 5.3.

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Figure 5.5: Comparing means ethanol yields of dry ground stover

156

Top two charts represent samples stored at 23oC; bottom chart represent samples stored at 37

oC and fermented with

pretreatment extracts. Time = pretreatment retention times

5.4 Conclusions

The results presented in this chapter indicate that the organic acids produced during wet storage

and/or pretreatment generally do not inhibit ethanol fermentation and in fact can enhance the

fermentation yield. These effects of organic acid can be observed at two levels: (1) at the storage

level, they potentially alter feedstock structure, resulting in more xylan removal or weaker

linkages between components of the plant cell wall matrix, and (2) during subsequent

pretreatment, when organic acids can accelerate as well as limit xylan removal depending on the

acids involved. Generally, these contributions of organic acids to the bioconversion of corn

stover feedstock to ethanol are greater at storage level, and the partial pretreatment benefits are

more pronounced during storage than in subsequent thermochemical pretreatment processing. It

is not likely that the organic acids produced during wet storage will exceed the minimum

inhibitory concentration for S. cerevisiae. However, an important observation of this study is that

the acid profile was changed during pretreatment. Lactic acid, which is less inhibitory than some

other organic acids, was dominant in ensiled samples and its low pKa means more of it was in

the dissociated form. Higher levels of dissociated acids mean more hydrogen ions that can

interact favorably with structural bonds. When these effects occur at the storage level the

disassociated acids cannot be easily assimilated into microbes even if retained in subsequent

processes, and are thus less likely to inhibit microbial growth or ethanol production. In addition,

when lactic acid is dominant during storage there is the potential for a shift in reaction towards

production of acetic acid during LHW pretreatment. This results in relatively more acetic acid

production during pretreatment, which is a better ethanol stimulant than lactic acid. As with the

livestock industry, lactic acid dominated silage has the best outcomes during both storage and in

subsequent bioconversion. From an engineering design perspective, it would be useful to develop

coupled ensilage/pretreatment systems that encourage more lactic acid production during storage

and more acetic acid in subsequent pretreatment processing, as long as those acid concentrations

are less than 5% DM.

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Using both xylan removal and ethanol yield as proxies for pretreatment effectiveness, these

results also provide evidence that pretreatment of ensiled stover could be carried out at a shorter

pretreatment time and thus lower severity than unensiled stover, and still be as effective. There

was evidence from the xylan removal results that wet storage resulted in changes that rendered

the feedstock more responsive to subsequent pretreatment process. Fermentation results also

indicated ensiled stover could achieve similar ethanol yields with shortened pretreatment times.

However, as pretreatment severity increases, the benefits derived from ensilage decrease. Xylan

removal rates by themselves were not always predictive, providing an indication of pretreatment

severity and perhaps pretreatment effectiveness, but not necessarily fermentation outcomes,

perhaps due to the presence of other compounds generated during storage or pretreatment.

In Chapter 3, it was observed that storage moisture content had minimal or no effect on

feedstock composition except at extreme levels. A similar observation was made in this chapter

with respect to ethanol yields. This is good news for biorefineries, which thus do not have to be

overly concerned with process adjustments to accommodate changes that might result from

different storage conditions, as these changes are not significant. Finally, these results confirm

that washing ensiled biomass to get rid of silage organic acids before subsequent processing does

not serve any benefit.

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hydrolysis using low concentrations of formic acid. Journal of Chemical Technology and

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controlled liquid hot water pretreatment of corn stover. Bioresource Technology

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acid on the growth of Saccharomyces cerevisiae in a minimal medium. Journal of

Industrial Microbiology and Biotechnology 26:171-177.

Palmqvist, E., H. Grage, N. Q. Meinander and B. Hahn-Hägerdal. 1999. Main and interaction

effects of acetic acid, furfural and p-hydroxybenzoic acid on growth and ethanol

productivity of yeasts. Biotechnology and Bioengineering 63(1):46–55.

Roffael, E. and H. G. Hüster. 2012. Complex chemical interactions on thermo hydrolytic

degradation of urea formaldehyde resins (UF-resins) in recycling UF-bonded boards.

European Journal of Wood and Wood Products 70(4): 401-405

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58:259–264.

Taherzadeh, M. J., C. Niklasson and G. Liden. 1997. Acetic acid friend or foe in anaerobic batch

conversion of glucose to ethanol by Saccharomyces cerevisiae? Chemical Engineering

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Thomas, K. C., S. H. Hynes, and W. M. Ingledew. 2002. Influence of medium buffering

capacity on inhibition of Saccharomyces cerevisiae growth by acetic and lactic acids.

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carboxylic acids produced from hydrothermal treatment of organic wastes. Journal of

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saccharification and fermentation of wet oxidized corn stover to ethanol. Biotechnology

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corn stover pretreated by lactic acid and/or acetic acid. Journal of biotechnology 139(4):

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Chapter 6

Post storage handling and processing of wet stored stover: effects of drying

Abstract

Biorefineries are projected to handle a wide variety of feedstocks to guarantee supply and sustain

continuous operation as well as optimize production of a wide spectrum of bio-based end-

products in addition to fuels. The goal is to improve environmental performance, accommodate

diversity in the agricultural industry, maximize economic benefits, and ensure the long term

sustainability of the bioenergy industry. One question that arises, when considering the

transportation aspects of biomass logistics, is whether or not wet storage (ensiled) feedstock

should be transported wet to the biorefinery. A uniform feedstock format has been suggested as a

reliable and perhaps necessary option for biorefineries utilizing multiple feedstocks to minimize

equipment redundancy and down time and improve overall performance. Most uniform

feedstock strategies would require drying of feedstocks that had been in wet storage. This

chapter examines the impact of drying ensiled feedstock by evaluating pretreatment outcomes

and ethanol fermentation. The results indicate that indicators of effective LHW pretreatment,

including higher xylan removal and minimum levels of inhibitors, favored “as is” moist silage

over dried silage. Ethanol yields were also higher for “as is” samples by factors of 1.3 and 1.6 for

unwashed and washed samples respectively. In addition, the results suggest that the impacts of

organic acids on feedstock during storage had a significant pretreatment effect. This effect is

enough to weaken the pore structure in plant cell walls, which collapsed during drying and

resulted in the low ethanol yields for dried silage.

Key words: corn stover, wet storage, ensiled, drying, logistics, pretreatment, organic acids,

ethanol, furfural, fermentation, xylan removal

6.1 Introduction

Wet storage (ensilage) of biomass for biofuel could address several logistical issues related to

harvest and storage. Harvest can be accomplished in a single pass, reducing the potential for soil

compaction due to traffic and the operational costs associated with the multi-pass system

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required by conventional harvesting methods. Wet storage can also eliminate the challenge of

reducing high moisture levels of feedstocks at the time of harvest, as well as contamination

concerns of field drying. However, wet storage may not necessary contribute positively to

transportation logistics. Dry storage is expected to have an advantage over wet storage with

respect to the ease and cost of transportation, eliminating the additional economic burden of

moving water over long distances to biorefineries. However, the low bulk density of dry

feedstock presents its own challenges, with volume rather than weight limits a constraint to truck

hauling capacity, and the resulting impact on transportation economics. One strategy for

handling wet storage feedstock is transportation through pipelines to a refinery, but the economic

viability of pipeline slurry systems is only achieved at a very large scale (Kumar et al., 2005).

Another possibility is to reduce the feedstock moisture content before transporting to a

biorefinery. Three practical options are available to reduce the moisture of wet storage biomass:

(1) reduce the moisture content prior to ensilage, initiating wet storage at low moisture levels,

probably near 35%; (2) reduce the water mass and/or increase dry bulk density by compression

(use a press to squeeze out the water and/or make pellets or briquettes); and (3) dry the feedstock

before transporting. This list is not exhaustive; for example, Grozdits (1997) describes a

biologically treated, self-ventilating system, using a proprietary mixture of thermophilic

microbes. But pre-storage drying, dewatering by densification, and post-storage drying, are

moisture reduction approaches that have been demonstrated for other food and biological

materials. The first option, drying biomass before storage, is well understood for hay production

systems, and would produce materials comparable to the 25% and 35% moisture wet storage

treatments described in previous chapters. The second option, densification or biomass into

pellets or briquettes works best for most materials at feedstock moisture of 15% - 20% (Kaliya

and Morey, 2009). The silage juice or effluent produced by densifying wetter materials

constitutes a disposal problem as well as potential loss of fermentable constituents. In theory this

effluent could be collected and fermented, but the relatively low sugar content may not make it

cost-effective, especially if densification is decentralized and away from the main biorefinery.

The third option, drying of silage after storage, is uncommon and there is currently no evidence

of such in the literature.

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Either densification or drying of post-storage silage would involve additional costs, and may not

have any economic advantage over transporting water depending on the methods or tools used.

However, some form of moisture reduction may be required to achieve the vision of a uniform-

format feedstock supply system designed to address the non-uniformity of the wide variety of

herbaceous/lignocellulosic feedstocks (Hess et al., 2009). At the advanced level, the uniform-

format supply chain concept does not provide for separate wet and dry delivery lines to the

refining site. Instead, all feedstock are formatted into high density dry products. Understanding

the effects of post-storage drying on biomass characteristics is therefore necessary to evaluate

whether wet storage is compatible with a uniform format supply chain, and how such drying

would affect biomass quality characteristics and potential biofuel yield.

If drying is to be accomplished post-storage, air drying seems unlikely due to the large surface

area required, the potential for contamination if surfaces are not clean, and because the silage is

likely to be compromised by aerobic degradation during a slow drying process. Aerobic stability

is the ability of the silage to maintain its integrity and resist spoilage or deterioration when

exposed to aerobic conditions, and lasts only a few days before the same organic acids that

provided preservation benefits in anaerobic conditions become high energy substrates for aerobic

microbes. Aerobic instability is a major concern in the livestock industry, and during feed out

from large silage piles can account for dry matter losses of up to 15% to 25% (Holmes and

Muck, 2000; Dow, 2008). Although aerobic stability can be enhanced using silage additives,

plain silage can maintain stability for up to 65 hours (Danner et al., 2003). This may allow for air

drying in arid regions, but is a short window of time in more humid climates, especially for a

large scale industrial process that would require high throughput throughout the year.

Understanding the impacts of high throughput densification or rapid drying methods are

therefore required if wet storage materials are going to enter a uniform format process or another

type of dry materials handling supply chain. This study will focus on rapid drying and its impacts

on biomass characteristics and subsequent processing. Although rapid drying times can be

accomplished by vacuum or freeze drying, high temperature drying is by far the most common

strategy for high volume bulk organic materials. Drying temperatures of up to 100oC have been

shown to have no significant impact on the sugar content of corn stover and wheat (Houghton et

164

al., 2009). However, this result may not apply to pretreated materials. There are currently no

known studies investigating the interaction of silage acids with heated drying processes, and

whether or not this interaction contributes to pretreatment of feedstock during drying or

subsequent hydrothermal pretreatment.

In this chapter, the effect of drying of wet storage (ensiled) feedstock on subsequent processes

was investigated to see if it is an option worth pursuing. Even with structurally intact fresh

biomass, drying can reduce the available fiber surface area by collapsing pore spaces in plant cell

walls (Esteghlalian et al., 2001). Pretreating biomass before drying, even air drying, will result in

extensive and permanent collapse of the pores (Dowe and McMillan, 2001; Selig et al., 2008).

The collapse of pore spaces may negatively impact the amount of sugars that can be hydrolyzed

enzymatically, and hence reduce the biofuel yield that can be obtained from the feedstock.

Previous chapters in this dissertation have shown that the organic acids produced during ensilage

can result in partial pretreatment during storage. This biological pretreatment process parallels

some of the mechanisms effective in conventional inorganic acid pretreatment, but is not as

severe. This study investigates the significance of this pretreatment effect and the impacts of

subsequent drying on biomass characteristics and ethanol yield.

6.2 Materials and methodology

6.2.1 Corn stover description and storage

Field dried corn stover, Pioneer brand 34A20, with particle size distribution of less or equal to

25.4 mm was rewetted to adjust the moisture level form an initial level of ~7% to six different

levels (25%, 35%, 45%, 55%, 65%, 75%). The moisture adjusted materials were stored under

anaerobic conditions at bulk densities of about 159 dry Kg/m3 in glass canning jars at 37

oC for

220 days. Storage was in triplicates.

After storage, triplicate samples were thoroughly mixed and divided in twos. The half portions

were dried in a HotPack convection oven at 55oC until constant weight, then ground using a 2

mm screen on a Wiley Mill (Model 4, Thomas Scientific, Swedesboro, NJ) and stored at room

temperature in sterile airtight Whirl-Pak bags (Nasco, Fort Atkinson, Wisc.) prior to

pretreatment. The other halves were stored “as is” – that is not dried nor ground after storage – in

165

Ziploc®

bags at -20oC prior to pretreatment. Some “as received” stover was also stored at room

temperature for downstream analysis. “As received” stover is dry stover without moisture

adjustment.

6.2.2 Organic acids, pretreatment, inhibitors and sugar removal

Organic acid measurements before and after storage were as described in Chapter 3. Prior to

liquid hot water pretreatment using ASE 350 (accelerated solvent extraction), part of the “as is”

samples and part of the dried samples were washed to prevent interference of organic acid with

the pretreatment process. Washing of “as is” samples was as described in Chapter 4 and washing

of dried samples and pretreatment conditions was as described in Chapter 5. Based on the

maximum amount of sample that can fill the ASE cell without impeding smooth flow of fluid

through the cell, cells were loaded with 1.5 dry grams of dried silage samples and approximately

1 dry gram of “as is” samples. Solid loading for pretreatment was~15.9% and ~9.5% for

unwashed dried and “as is” samples respectively and ~13.7% and ~8.4% for respective washed

samples. Pretreatment retention times were 5, 10 and 15 minutes.

Pretreatment extracts were analyzed for organic acids, HMF and furfural, xylan and glucan

removal and pH following methods describes in Chapter 5.

6.2.3 Fermentation

Pretreated solids and separately collected pretreatment extracts were combined in a 50-ml

centrifuge tube for simultaneous saccharification and fermentation. The fermentation broth

contained the following components prepared in a cocktail before addition: Penicillin-

Streptomycin at a final concentration of 30 μg/mL (1% v/v) to prevent microbial growth; citric

acid buffer (pH 4.5) at a final concentration of 0.05M in the fermentation broth to maintain the

pH at 4.8, in the optimum range for enzyme activity; Yeast peptone (YP) as microbial nutrient at

1% broth volume; commercial cellulase (Spezyme CP, Genencor, Rochester, NY) at 15 filter

paper units (FPU)/g initial complemented with a commercial β-glucosidase (Novozyme 188,

Novozymes A/S, Bagsvaerd, Denmark) at 60 cellobiase units (CBU)/g glucan. The ethanol

fermenting microbe, Saccharomyces cerevisiae NRRL Y-2034, was obtained from the USDA

ARS culture [NRRL] collection. The centrifuge tubes were tightly sealed and placed in a 110

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rpm lateral motion hot water bath at 35oC for 72 hours. Supernatants, collected after

fermentation, were analyzed for ethanol using YSI 2700 SELECTTM

biochemical analyzer (YSI

Inc., Yellow Springs, OH). See Chapter 5 for more details on fermentation.

6.2.4 Data Analysis

Results were analyzed using statistical tools such as analysis of variance (ANOVA) and

covariates (ANCOVA) as well as regression. All statistical tests were conducted using Minitab

14 (Minitab Inc., State College, PA) or XLSTAT (Addinsoft, New York, NY) at a significance

level, α, of 0.05.

6.3 Results and discussion

6.3.1 Pretreatment pH

Generally, pH of pretreatment extracts was lower than storage pH and decreased with

pretreatment time. On average, Day 0 samples decreased by more than 2 pH units while Day 220

samples decreased by 0.18 – 0.34 units. See Appendix E, Table E1. Although pH at Day 0

decreased drastically during pretreatment, it was still higher than pH at Day 220 (p < 0.0001).

Figure 6.1 shows pH across moisture at the different pretreatments times. Unwashed dried silage

had relatively higher pH (4.25 ± 0.21) than “as is” silage (4.14 ± 0.34) (p = 0.001) but there was

no significant difference in pH of dried and “as is” when samples were washed before

pretreatment (Dried = 4.08 ± 0.18, “as is” = 4.08 ± 0.16; p = 0.989). Figure 6.1 also shows a

second order polynomial best describes the relationship between pH and storage moisture

content. The coefficient of determination shows that pH can be reasonably predicted by

moisture. Using 5-minute pretreatment as a reference, graphs of “as is” samples are roughly

translated vertically down (lower pH) and horizontally to the right (higher moisture) with

increase in pretreatment time. This suggests the lowest pH, when “as is” samples were pretreated

for 10 and 15 minutes, was generally realized in samples stored at relatively higher moisture and

is more comparable at these two pretreatments times than at 5 minutes. In contrast, the unwashed

dried samples seemed to have shifted horizontally to the left, suggesting that the lowest pH was

achieved in samples stored at relatively lower moisture levels. Those lowest pH values for these

unwashed dried samples were higher than those of “as is” samples. Appendix E, Table E2 shows

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the vertices of the various graphs and in general indicating lowest pretreatment pH values

attained and the corresponding moisture level at which these were or can be achieved. The

exceptions are unwashed unensiled “as is” samples with convex parabolas, whose vertices depict

maximum pH. The results suggest that for the wide range of moisture in this study, covering all

possible practical storage moistures levels, pH is not likely to drop to 3. The lower the pH, the

higher the potential for inhibition further downstream. Washed samples generally had lower pH

than unwashed samples. Although pH generally decreased with pretreatment time, for “as

received” stover (not shown in Figure 6.1), there was no significant difference across

pretreatment time (p = 0.318). Average pH for “as received” stover was 4.13 ± 0.16.

The pH of pretreated samples is important for at least three reasons: (1) it may be an indication

of how much deacetylation or degradation takes place during pretreatment leading to release of

acetic acid or other organic acids. Organic acids interact with the LHW pretreatment process, in

a kind of mild acid hydrolysis of linkages in hemicelluloses, in a manner similar to dilute acid

pretreatment. Using pH as a tool for measuring pretreatment activity is not very reliable when

comparing different feedstock since the buffering capacities could prevent changes in pH that

could result from more acid release. Also, the presence of other acids prior to pretreatment may

interfere with this pretreatment indicator (2) pH is a crucial parameter that determines enzymatic

and microbial activities. For some micro-organisms, the pH determines what products can be

formed. Saccharomyces cerevisiae, which is the most common microbe used for ethanol

fermentation, is very robust and can survive wide pH range, 2.5 – 8.5 or even 10.0 depending on

the medium composition. Optimum pH for growth is however between 4.0 and 6.0 and optimum

pH for ethanol production is approximately 4.0 to 4.5 (Buzas et al., 1989; Joosten and Peeters,

2010). The ethanol metabolic pathway from sugars is not greatly affected by the pH, although

production rate and product amounts can be affected. However, when growth or metabolite

inhibitors are present in the fermentation broth, pH becomes critical as it determines the

microbial tolerance or sensitivity to the inhibitors. (3) pH can affect process cost. This is

particularly true for acid and alkali pretreatment, in which feedstock must be neutralized or

adjusted before fermentation. The pH values of pretreatment extracts in this study were mainly

within the range conducive for subsequent enzymatic and fermentation processes.

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Figure 6.1: pH of pretreatment extracts of “as is” and dried stover at different retention times

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Table 6.1: pH of “as is” and dried stover at different pretreatment retention times

6.3.2 Organic acids, HMF and Furfural

Organic acids

In Figure 6.2, it can be observed that the organic acid profile of corn stover changed drastically

as a result of drying the feedstock prior to pretreatment. Drying resulted in more acids in Day 0

samples and the disappearance of lactic and acetic acids in Day 220. Lactic and acetic acids,

which are common and dominant in corn stover silages, were replaced by more inhibitory

isobutyric acid. Isobutyric acid was almost the sole acid in high moisture samples. Tartaric,

pyruvic and malic acids can be difficult to quantify due to their short retention times near the

elution of the system void volume, however, the high amounts detected in the samples facilitated

their quantifications.

A number of studies (Porter and Murray, 2001; Weißbach and Strubelt, 2008; Kreuger et al.,

2011) have shown that 55% - 89% of volatile organic acids (acetic, butyric, isobutyric,

propionic) and 9% - 53% of lactic acid in ensiled feedstock were lost [volatilized] during drying

at temperatures of 60oC – 105

oC. However, there were no indications of any increase or

transformation in acid content. For unensiled stover there were essentially no acids before

drying. It is possible that other volatile compounds were degraded and interacted with each other

5 10 15 p-value

Wash 4.22 ± 0.15a 4.07 ± 0.10b 3.94 ± 0.08c <0.0001

Unwash 4.23 ± 0.15a

4.12 ± 0.09ab

4.05 ± 0.08b 0.001

Wash 4.28 ± 0.11a

4.04 ± 0.07b

3.92 ± 0.06c <0.0001

Unwash 4.40 ± 0.18a

4.25 ± 0.16bab

4.09 ± 0.17b <0.0001

"As is" Unwash 4.60 ± 0.14a

4.40 ± 0.13b

4.28 ± 0.13b <0.0001

Dry Unwash 4.64 ± 0.04a

4.42 ± 0.06b

4.27 ± 0.03c <0.0001

Day 0

Pretreatment retention time (min)

Day 220

"As is"

Dry

170

and together with the “grinding effect” produced the transformations observed in acid content

after drying.

Figure 6.2: Organic acid profile of “as is” (top) and dried (below) Day 0 and Day 220 samples

before pretreatment

0

1

2

3

4

5

6

7

8

15 25 35 45 55 65 75 15 25 35 45 55 65 75

0 0 0 0 0 0 0 220 220 220 220 220 220 220

Org

anic

aci

d (%

dry

mat

ter)

Butyric (% DM)

Isobutyric (% DM)

Acetic (% DM)

Lactic (% DM)

Moisture,(%)

Duration (days)

0

1

2

3

4

5

6

7

8

15 25 35 45 55 65 75 15 25 35 45 55 65 75

0 0 0 0 0 0 0 220 220 220 220 220 220 220

Org

anic

aci

d (%

dry

mat

ter)

Pyruvic (% DM)

isobutyric (% DM)

acetic (% DM)

malic (% DM)

tartaric (% DM)

Duration (days)

Moisture (%)

“AS IS”

DRY

171

After pretreatment, organic acid types in unwashed “as is” and unwashed dried Day 220 stover

were similar although amounts in “as is” samples were generally higher than amounts in dried

samples. The same acid types were also present in the corresponding Day 0 samples. Eight acid

types were identified in unwashed samples (see Table 6.2). Dried unensiled stover generated

formic and lactic acids in addition to the four acids initially present before pretreatment and dried

ensiled stover generated four to six more acids, whereas “as is” samples generated formic,

pyruvic and malic acids in addition to acid types initially present before pretreatment. The

difference between “as is” and dried acid types, however, was the presence of tartaric acid in the

dried samples versus butyric and propionic acids in the “as is” samples, each of which was

absent in the other type of sample. Butyric and propionic acids were present only in the 75%

moisture samples. The main acids (> 0.5% of the dry matter, DM) in the unwashed “as is”

samples were acetic (2.6% ± 1.73), isobutyric (2.17% ± 1.3), lactic (0.82% ± 1.44) and formic

(0.50% ± 0.38). In the unwashed dried samples, the main acids were isobutyric (1.47% ± 0.82),

lactic (1.6% ± 1.46), acetic (0.82% ± 0.44), tartaric (0.54% ± 0.85). Generally, there was a

decrease in lactic acid from storage amount and an increase in acetic and isobutyric acids after

pretreatment in “as is” samples, while dried samples showed an increase in lactic and acetic but

a drastic decrease in isobutyric acid.

In washed samples, both “as is” and dried, fewer acids were generated during pretreatment and

these few acids were each present in lower concentration compared to unwashed samples. The

lower concentration of organic acid generated were observed in both ensiled and unensiled

washed samples. The former may result from degradation occurring during storage resulting in

organic acids that were washed out and lesser LHW hydrolysable components. The unensiled “as

is” samples had little or no acid and both dried and “as is” had no structural degradation during

“storage” to account for the lower concentration of organic acid generated during pretreatment.

An alternative explanation of the lower organic acid concentration will be that the water

extractable components of the stover, up to 7% (see Chapter 3), could also be major contributors

to organic acid generated during pretreatment or could aid in catalyzing the reaction. Water

extractable components may include non-structural sugars, some alcohols, inorganic ions and

organic acids.

172

Only four acid types were identified in washed “as is” stover and of these, only acetic and formic

were common to unwashed “as is” samples. Acetic acid was the dominant acid in washed “as is”

samples. Formic acid amounts were not significantly different for washed and unwashed

samples. Isobutyric, butyric and lactic acids, washed out before pretreatment, were not

regenerated during pretreatment of washed samples; malic, pyruvic and formic acids were

generated instead. These acids together were less than 1% DM (0.07% ± 0.35).

For washed dried stover, lactic acid was present in addition to the four acids present in washed

“as is” samples but malic acid dominated, in contrast to acetic acid dominance in washed “as is”

samples and in contrast to unwashed dries samples in which lactic acid dominated. Acetic acid

amounts were higher in washed dried silage (0.87% ± 0.06) compared to unwashed dried stover

(0.64% ± 0.33) but lower than amounts in washed “as is” stover.

173

Table 6.2: Organic acid profile of Day 220 stover after pretreatment with acids listed by decreasing inhibitory potential.

ₒ Showing mean values

Dried samples were dried at 55oC before pretreatment; Washed samples were washed before pretreatment. See text for detailed protocol

* Lactic acid can be metabolized by most fungi

? Pyruvic provides energy for most living cells and is a key intermediate for most metabolic processes. Like most things, excess pyruvic could have negative impact.

Pyruvic inhibitors are usually derivatives compounds

√ √ Higher antimicrobial activity relative to other microbial group

x not measured

See Appendix E, Table E7, for data on Day 0 (unensiled controls)

Generally same types of acids were present in corresponding Day 0 samples but at relatively lower amounts

High (15.4% DM) Low (1.1% DM)High (8.2% DM) Low (0.1% DM)

25 35 45 55 65 75 25 35 45 55 65 75 25 35 45 55 65 75 25 35 45 55 65 75

√ √√ 5 0.00 0.00 0.00 0.00 0.00 0.15 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ₓ ₓ ₓ ₓ ₓ ₓ

10 0.00 0.00 0.00 0.00 0.00 0.18 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ₓ ₓ ₓ ₓ ₓ ₓ

15 0.00 0.00 0.00 0.00 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

√√ √ 5 0.32 0.33 0.31 0.23 0.24 0.31 0.18 0.22 0.14 0.25 0.35 0.33 0.21 0.12 0.21 0.14 0.11 0.15 ₓ ₓ ₓ ₓ ₓ ₓ

10 0.50 0.35 0.52 0.38 0.40 0.47 0.40 0.29 0.31 0.33 0.28 0.30 0.21 0.25 0.27 0.18 0.10 0.29 ₓ ₓ ₓ ₓ ₓ ₓ

15 0.54 0.62 1.33 0.65 0.42 0.56 0.65 0.59 0.47 0.55 0.61 0.56 0.28 0.21 0.24 0.20 0.22 0.31 0.4 0.3 0.4 0.3 0.3 0.3

√ √ 5 0.00 0.00 0.00 0.00 0.00 4.55 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ₓ ₓ ₓ ₓ ₓ ₓ

10 0.00 0.00 0.00 0.00 0.00 4.22 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ₓ ₓ ₓ ₓ ₓ ₓ

15 0.00 0.00 0.00 0.00 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

√ √ 5 1.39 2.44 2.69 2.05 1.87 0.00 0.0 0.0 0.0 0.0 0.0 0.0 1.22 0.80 1.60 1.13 0.00 0.00 ₓ ₓ ₓ ₓ ₓ ₓ

10 2.45 2.58 4.10 2.90 2.76 0.00 0.0 0.0 0.0 0.0 0.0 0.0 1.29 1.65 1.89 1.42 1.46 2.15 ₓ ₓ ₓ ₓ ₓ ₓ

15 2.91 4.99 4.24 5.02 2.80 2.58 0.0 0.0 0.0 0.0 0.0 0.0 1.88 1.31 1.96 1.66 0.86 1.32 0.0 0.0 0.0 0.0 0.0 0.0

√√ √ 5 1.50 1.92 2.38 2.98 2.93 7.11 0.69 0.79 0.77 0.64 0.66 0.76 0.71 0.44 0.65 0.23 0.40 0.53 ₓ ₓ ₓ ₓ ₓ ₓ

10 1.84 1.53 3.75 3.55 3.35 8.13 0.99 1.06 0.93 1.04 1.24 1.10 0.40 0.74 0.53 0.37 0.55 0.78 ₓ ₓ ₓ ₓ ₓ ₓ

15 1.40 2.84 3.19 4.16 2.82 2.52 1.41 1.55 1.28 1.43 1.62 1.63 0.87 0.40 0.66 0.43 0.59 0.87 0.8 0.9 0.9 0.9 0.8 0.9

√ * 5 1.36 0.96 2.68 1.61 1.79 0.00 0.0 0.0 0.0 0.0 0.0 0.0 1.73 2.27 3.08 3.80 2.89 3.45 ₓ ₓ ₓ ₓ ₓ ₓ

10 1.49 0.34 5.61 3.25 2.03 0.00 0.0 0.0 0.0 0.0 0.0 0.0 1.91 2.83 3.52 3.77 2.96 3.61 ₓ ₓ ₓ ₓ ₓ ₓ

15 0.88 0.86 0.00 0.00 2.91 0.49 0.0 0.0 0.0 0.0 0.0 0.0 1.63 2.16 2.72 3.60 3.05 3.50 0.3 0.4 0.4 0.3 0.4 0.2

√ √√ 5 0.1 0.3 0.2 0.1 0.1 0.0 0.13 0.12 0.15 0.12 0.17 0.12 0.15 0.16 1.79 1.18 0.04 0.09 ₓ ₓ ₓ ₓ ₓ ₓ

10 0.22 0.24 0.28 0.19 0.15 0.15 0.18 0.15 0.14 0.16 0.19 0.18 0.14 0.19 0.39 1.33 1.07 0.62 ₓ ₓ ₓ ₓ ₓ ₓ

15 0.23 0.31 2.38 0.33 0.21 0.17 0.45 0.43 0.43 0.42 1.07 0.31 0.21 0.29 0.68 0.75 0.29 0.69 1.8 1.9 2.4 2.9 3.4 0.4

√ 5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.85 0.26 0.16 0.09 0.01 0.04 ₓ ₓ ₓ ₓ ₓ ₓ

10 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.27 0.27 0.20 0.00 0.01 0.01 ₓ ₓ ₓ ₓ ₓ ₓ

15 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.22 0.13 0.30 0.10 0.01 0.00 0.0 0.0 0.0 0.0 0.0 0.0

? ? 5 0.12 0.23 0.22 0.19 0.23 0.05 0.09 0.18 0.08 0.09 0.11 0.12 0.23 0.20 0.33 0.30 0.12 0.14 ₓ ₓ ₓ ₓ ₓ ₓ

10 0.19 0.18 1.11 0.25 0.28 0.67 0.13 0.11 0.10 0.11 0.23 0.10 0.22 0.22 0.24 0.35 0.26 0.15 ₓ ₓ ₓ ₓ ₓ ₓ

15 0.22 0.28 0.69 0.29 0.25 0.34 0.20 0.14 0.13 0.14 0.19 0.14 0.23 0.19 0.17 0.18 0.16 0.18 0.2 0.2 0.1 0.2 0.3 0.1

5 4.79 6.15 8.49 7.16 7.14 12.18 1.09 1.31 1.14 1.10 1.29 1.33 5.10 4.25 7.82 6.87 3.57 4.40 ₓ ₓ ₓ ₓ ₓ ₓ

10 6.69 5.22 15.37 10.52 8.97 13.82 1.70 1.61 1.48 1.64 1.94 1.68 4.44 6.15 7.04 7.42 6.41 7.61 ₓ ₓ ₓ ₓ ₓ ₓ

15 6.18 9.90 11.83 10.45 9.41 6.66 2.71 2.71 2.31 2.54 3.49 2.64 5.32 4.69 6.73 6.92 5.18 6.87 3.51 3.74 4.13 4.67 5.14 1.90

Pretreatment

organic acids Bac

teri

a

Yeas

t &

mo

uld Pretreatment

time (min)

Inh

ibit

ory

po

ten

tial

Total acids

Average total acids

Lactic (%)

Malic (%)

"As is", washed Dried, unwashed Dried, washed"As is", unwashed

Propionic (%)

Formic (%)

Butyric (%)

Isobutyric (%)

Acetic (%)

Tartaric

Pyruvic (%)

5.93 3.858.94 1.87

174

The dominant pretreatment acid in unwashed dried stover was lactic acid. The dominant acid in

“as is” stover – both washed and unwashed – was acetic acid although amounts in unwashed

ensiled stover (3.22% DM ± 1.81) was more than twice the amount in the washed ensiled (1.09%

± 0.36) and unensiled samples (1.52% DM ± 1.11, washed and unwashed lumped). Acetic acid

generated in washed ensiled (1.09% DM ± 0.36) and washed unensiled (1.07% DM ± 0.35)

samples were not significantly different (p = 0.80) and did not vary with storage moisture but

were both lower than amounts in unwashed unensiled samples (1.97% DM ± 1.4). For unwashed

samples – ensiled and unensiled – there were no differences in amount of acetic acid generated

across moisture except for 75% moisture samples, which had higher amounts, up to 8%.

Dominant acids as well as the total acids in unwashed samples, of both ensiled and unensiled

samples of “as is” and dried stover, were not significantly different at the various pretreatment

times except for isobutyric, for which amounts in 15 minutes were higher than amounts in 10 and

5 minutes. Although there was an increase in amount of total acids in unwashed samples during

pretreatment over storage amounts, the presence of initial acid [regardless of the type] in

unwashed samples seems to dampen the effect of pretreatment time on acid yields. For washed

samples, dominant acids and total acids increased with pretreatment time and were different for

all time levels. The total acids after pretreatment of ensiled stover were on average

approximately 9% and 6% for unwashed “as is” and unwashed dried samples respectively and

2% and 4% for corresponding washed samples (see Table 6.2). Appendix E, Tables E4 to E7, has

details of organic acids generated at the various pretreatment times.

In “as received” stover (dry stover without moisture adjustment), six acid types were identified

after pretreatment and the dominant acids were acetic, isobutyric and tartaric acids. Similar to

washed “as is” samples, no lactic acid was detected in both washed and unwashed “as received”

stover. Compared to the dominant acids in unwashed and washed dried samples, there was no

significant difference in isobutyric amounts but acetic acid was higher in “as received” stover.

There was, however, no significant difference in amounts of acetic and isobutyric when

compared to unwashed “as is” samples.

175

Inhibitors

Amounts of furfural generated in unwashed dried silage samples (Day 220) were generally more

than twice the amounts generated in “as is” silage samples (0.376% DM ± 0.215 vs 0.156% DM

± 0.109) while HMF amounts were about seven times more (0.034% DM ± 0.018 vs 0.005%

DM ± 0.003). See Figure 6.3. At 15 minutes pretreatment retention time, where more inhibitors

were expected, washed dried silage had no HMF present, however, furfural was more than ten

times as much in washed dried silage as it was in washed “as is” silage (1.125% DM ± 0.258 vs

0.105% DM ± 0.048). At this retention time, amount of furfural in washed dried samples was

about twice the amount in unwashed dried samples. The opposite was the case for “as is”

samples, in which washed samples had about half the amounts generated in unwashed samples.

As with dried Day 220 samples, dried Day 0 samples had higher amounts of both furfural and

HMF compared to “as is” samples. HMF although negligible, was about ten times as much when

compared to “as is” samples, while furfural was about thrice as much.

Furfural and HMF amounts in pretreated ensiled (Day 220) samples were generally higher than

amounts in unensiled (Day 0) samples. Furfural in dried silage and “as is” silage were more than

amounts in corresponding unensiled sample by a factor of 1.5 and 2.0 respectively (p < 0.05).

Amounts in dried Day 0 and “as is” Day 0 samples were 0.255% DM ± 0.166 and 0.078% DM ±

0.051 respectively. HMF amounts in dried silage (0.034% DM ± 0.018) and dried unensiled

sample (0.028% DM ± 0.015) were not significantly different. Although HMF in “as is” silage

was about 1.9 times more than amount in unensiled “as is” samples (p < 0.0001), these amounts

were negligible (0.005% DM ± 0.003 vs 0.003% DM ± 0.002). For “as received” samples,

furfural generated was similar to amounts generated in dried samples (0.350 ± 0.255) but HMF

levels were higher (0.075 ± 0.045). Furfural amounts at 5 minutes were lower than amounts at 10

and 15 minutes in dried stover (p = 0.002). In contrast, furfural amounts in “as is” samples were

different at all time levels (p < 0.0001).

176

Figure 6.3: Furfural and HMF generated during LHW pretreatment. Filled rectangles = Day 220;

Empty rectangles = Day 0; Brown =HMF

177

6.3.3 Sugar removal

Generally, glucan removal was up to 7.7% in “as is” samples and up to 5.9% in dried samples.

Xylan removal was up to 57.7% in “as is” samples and up to 43.4% in dried samples. See Figure

6.4. On average, amount of glucan removed in “as is” silage [i.e. Day 220] was about 0.5%

points higher than amount removed in dried silage (p = 0.011) while xylan removed was about

6.1% points higher (p < 0.0001). Glucan removal in “as is” and dried Day 0 samples on the other

hand was not significantly different (p = 0.505). However, xylan removal in Day 0 samples was

higher in “as is” samples, about 4.6% points above dried samples (p = 0.001). Washing “as is”

silage before pretreatment resulted in approximately 14.0% point increase in xylan removal over

unwashed samples (p < 0.001) while washed Day 0 samples had only about 5.0% point increase

over corresponding unwashed samples. Glucan removal, however, was not significantly different

in washed and unwashed samples. For washed dried silage although xylan removal was about

11.5% points higher than corresponding unwashed sample, it was about 6.4% point lower than

washed “as is” silage.

Figure 6.4 shows glucan and xylan removal across moisture. Generally, there were no significant

differences in amount of glucan removed in feedstock at the various moisture levels except for

the washed samples, where the 25% moisture samples had more glucan removed. Xylan removal

was also not affected very much by storage moisture. There were no significant differences

across moisture in all Day 0 samples. For Day 220, dried samples at 35% moisture levels had

less xylan removed than dried 45% and 55% moisture samples (p = 0.006). Also, for “as is”

samples, xylan removal in 45% moisture samples was more than removal in 65% and 75%

moisture samples (p = 0.005).

178

Figure 6.4: Glucan and Xylan removal, as % of initial amount present, across moisture during

LHW pretreatment.

It can be observed in Table 6.3 that glucan removals were generally not different at the various

pretreatment retention times. In addition, glucan removals in unwashed stover were quite similar

0

1

2

3

4

5

6

7

8

25 35 45 55 65 75 25 35 45 55 65 75

Glu

can

rem

ove

d (%

)

Nominal moisture content (%)

Unwashed, Dry

Unwashed, "as is"

Washed, "as is"

0

10

20

30

40

50

60

25 35 45 55 65 75 25 35 45 55 65 75

Xyl

an r

em

ove

d (%

)

Nominal moisture content (%)

Unwashed, Dry

Unwashed, "as is"

Washed, "as is"

XYLAN

Day 220 Day 0

GLUCAN

Day 220 Day 0

179

for dried and “as is” samples. In contrast, Xylan removals, showed significant difference with

pretreatment time although there was generally no difference between amounts removed in 5 and

15 minutes for Day 220 and amounts removed in 10 and 15 minutes for Day 0. At 5 minutes

retention time, there was no significant difference in xylan removal in unwashed dried and “as

is” samples. However, at higher retention times, xylan removals in “as is” samples were

significantly higher than removals in dried samples.

180

Table 6.3: Xylan and glucan removal (% of amounts initially present) at different pretreatment retention time

Same letter (across) implies not significantly different

Same font color (down) implies not significantly different

(See Appendix E, Tables E9 and E10, for full data on glucan and xylan removal by moisture at the various pretreatment times)

5 10 15 p-value 5 10 15 p-value

Unwashed, Dry 24.61 26.64 23.89 0.091 2.96 3.03 2.81 0.517

Unwashed, "as is" 27.95 33.51 31.88 0.040 3.25 3.25 3.70 0.425

Washed, "as is" 44.22 49.19 41.86 < 0.0001 3.33 3.98 3.74 0.110

<0.0001 <0.0001 <0.0001 0.427 0.005 0.016

Unwashed, Dry 21.94 27.23 27.10 < 0.0001 4.26 4.61 4.59 0.318

Unwashed, "as is" 22.60 33.65 33.95 < 0.0001 3.96 4.95 5.05 0.094

Washed, "as is" 29.05 37.44 38.73 < 0.0001 3.25 3.78 4.36 0.009

0.003 <0.0001 <0.0001 0.077 0.002 0.256p-value down

p-value down

Day 220

Day 0

Xylan removed (%) Glucan removed (%)

Pretreatment time (min) Pretreatment time (min)

a a a

abba

aba

a b b

a b b

a b b

a a a

a a a

a a a

a a a

a a a

a ab b

181

6.3.4 Ethanol yield

Generally, ethanol yields, on percentage theoretical basis, from wet storage “as is” samples were

higher than yields from corresponding dried silage samples (see Figure 6.5). Mean yields for

washed and unwashed samples were 61.8% ± 10.5 vs 37.6% ± 6.3 and 54.4% ± 12.2 vs 40.6% ±

7.2 respectively for “as is” vs dried silage (p < 0.0001). For “as is” samples, yields from washed

samples were significantly higher than yields from unwashed samples (p = 0.008), whereas for

dried samples, there was no significant difference between the two (p = 0.064). Compared to Day

0 samples (unensiled, controls), Day 220 “as is” samples that were washed had higher yields

61.8% ± 10.5 vs 53.19% ± 6.53 (p < 0.0001), however there was no significant difference in

yields for unwashed samples 54.4% ± 12.2 vs 54.6% ± 10.1 (p = 0.944). In contrast, unwashed

dried samples showed higher yields at Day 0 (48.2% ± 3.8) compared to Day 220 (40.6% ± 7.2)

(p < 0.0001). The higher yields of “as is” Day 0 samples over dried Day 0 samples supports the

theory that drying could collapse pore spaces making hydrolysis less effective. Also the higher

yield of dried Day 0 samples over dried Day 220 samples further suggest that the partial

pretreatment capability of silage was effective enough to weaken the pore structure in plant cell

walls, compounding pore collapse during drying and reducing enzyme access to structural

sugars.

Compared to dry ground “as received” samples, ethanol yields from unwashed “as is” samples

(both Day 0 and Day 220) and Day 0 washed “as is” samples were not significantly different.

However, yields from Day 220 washed “as is” samples were significantly higher than yields

from washed “as received” samples. In contrast, yields from dried Day 220 silage were

significantly lower than yields from “as received” samples but there were no significant

differences when compared to dried Day 0 samples. Again, this result suggest that drying ensiled

feedstock could lead to collapsed pore space due to the mild acid pretreatment effected on the

material during storage (See Appendix E, Table E12 for data on “as received” stover). When “as

received” samples were pretreated and fermented without size reduction (grinding), the ethanol

yields were generally low, less than 20% theoretical.

182

Figure 6.5: Lumped comparison of ethanol yield of unwashed samples and washed “as is” and

dried stover. Grey/black =Day 0 (controls); Pink/red = Day 220. Open circles = individual

values; solid circles = mean values; * = outliers (See Appendix E, Table E13 for yield by

moisture and pretreatment time)

UNWASH

WASH

183

Table 6.4 shows lumped summary of pretreatment outcome and ethanol yields of “as is” and

dried samples. Although, on average amounts of total acids in unwashed “as is” silage were

higher than amounts in dried silage by a factor of 1.5, these high acid levels generally did not

affect ethanol yield of “as is” samples. In calculating ethanol yield, it was assumed that liquid hot

water hydrolyzed structural sugars were not further degraded to non-sugar products. The

theoretical ethanol yield was therefore based on amount of sugar present before pretreatment,

some of which were in the solids and some in the pretreatment extract. This assumption was

based on the negligible amounts of furfural and HMF measured in the pretreatment extracts. If

sugars were degraded to furans and or then to acids, then sugars present after pretreatment would

be reduced and yield based on a percentage of the new lower theoretical amount would be much

higher in the “as is” samples. Therefore, besides making an assumption for conservation of

sugars, the significance of using amount of sugar present before pretreatment is that the ethanol

yield as percentage of the theoretical yield factors in losses incurred through degradation.

Table 6.4: Lumped summary profile of dried and “as is” samples after LHW pretreatment

For “as is” samples, acetic and isobutyric acids, which were dominant in unwashed samples had

negative (r = -0.594; p < 0.0001) and positive (r = 0.502; p < 0.0001) correlation with ethanol,

respectively. It can however be observed from data that this negative effect of acetic acid was

pH

Mean total

acids

(% DM) Dominant acid

Glucan

removed

(%)

Xylan removed

(%)

Maximum

Furfural

(% DM)

Ethanol (%

theoretical)

"As is" washed 4.08 ± 0.16 1.87 Acetic 3.68 ± 0.76 45.09 ± 5.04 0.17 61.76 ± 10.49

"As is" unwashed 4.13 ± 0.13 8.94 Acetic/Isobutyric 3.40 ± 0.96 31.11 ± 5.63 0.42 54.42 ± 12.19

Dry unwashed 4.24 ± 0.21 5.93 Lactic/Isobutyric 2.93 ± 0.46 25.05 ± 3.22 0.85 40.56 ± 7.20

"As is" washed 4.27 ± 0.16 1.67 Acetic 3.79 ± 0.92 35.07 ± 6.46 0.12 54.60 ± 10.08

"As is" unwashed 4.43 ± 0.19 5.35 Acetic/Isobutyric 4.65 ± 1.37 30.06 ± 7.00 0.18 53.19 ± 6.53

Dry unwashed 4.44 ± 0.15 5.29 Isobutyric/Acetic 4.49 ± 0.63 25.42 ± 3.37 0.54 48.21 ± 3.81

Washed 4.29 ± 0.16 5.10 Acetic/Isobutyric/Tartaric 4.84 ± 0.25 27.45 ± 3.44 0.75 47.16 ± 3.13

Unwashed 4.32 ± 0.17 6.81 Acetic/Isobutyric/Tartaric 4.68 ± 1.16 30.27 ± 1.95 0.70 46.39 ± 6.04

Day 220

Day 0

"As received"

184

only when concentrations were greater than 6% dry mass of sample ( ≥ 4.0 g/L fermentation

volume) and occurred only in 75% moisture samples. In addition, samples with very high acetic

acids were also high in butyric acids (≥ 4% DM; up to 3.1 g/L fermentation volume). Xu et al.

(2010) observed that up to 10% DM did not inhibit ethanol production. This amount is higher

than observed in this study. The combine effects of acetic and butyric could, therefore, be

responsible for the lower yields. Furthermore, most of these high acids in high moisture samples

were at 5 minute pretreatment retention time. Pretreated samples containing butyric acids had no

isobutyric acid. At longer pretreatment retention time, butyric acid was about half the amount at

10 minutes (lumped mean) and disappeared by 15 minutes. Butyric acid was possibly converted

to isobutyric acid, which increased with pretreatment time. Isobutyric acid concentration of up to

4.3 g/L, which was the maximum in this study, did not have any negative impact on ethanol

yield. Lactic acid after pretreatment was not correlated to ethanol yields. See Figure E2 in

Appendix E.

In dried samples, where acetic acid levels were less than 3% DM, acetic acid a positive but weak

correlation with ethanol (r = 0.376; p = 0.001) and serve to enhance ethanol yield. Generally,

dried Day 0 (unensiled) samples had higher levels of acetic acid and better yields than dried Day

220 (ensiled) samples. See Appendix E, Figure E3. Isobutyric acid also had positive correlation

with ethanol (r = 0.446; p < 0.0001) while lactic had a negative correlation. Although lactic acid

was dominant in Day 220 samples, amounts were generally comparable to amounts in “as is”

samples, which generally had good ethanol yields. In addition, lactic acid is less inhibitory than

acetic acid, which at higher concentration did not affect ethanol yields. Furthermore, the lactic

acid concentration (2.0 g/L ± 0.5) is not expected to inhibit ethanol production. At pH of 5,

Saccharomyces cerevisiae could tolerate 30 g/L of lactic acid (Graves et al., 2006). Although this

pH of 5 is slightly higher than pH of 4.8 used in this study, it shows that lactic acid concentration

in the dried samples were too low to singularly affect ethanol yields.

Dried Day 0 and Day 220 samples were similar in many ways, with respect to pretreatment

outcome (Table 6.4), differing only in glucan removal, pH and dominant acid. This presuming

lower yield in the Day 220 samples associated with relatively higher lactic acid could be a false

correlation, considering the lack of correlation between lactic and ethanol in “as is” samples.

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This false correlation could have been influenced by unmeasured parameters like pore space or

simply a result of complex interaction with other acids or compounds.

Xylan removal was significantly but weakly correlated to ethanol. For “as is” samples the

correlation was 0.400 (p = 0.001) and for dried sample, the correlation was 0.319 (p = 0.006).

When storage duration was controlled, xylan removal was not significantly associated with

ethanol in Day 220 (ensiled) “as is” samples.

6.4 Conclusions

Dried and “as is” stover differ in many ways. When pretreated, ensiled samples of the latter had

more xylan removed, more acids generated and were dominant in acetic and isobutyric acids.

Contrary to expectations that more acids would adversely impact ethanol yields, “as is” samples

had significantly higher yields than dried samples. In dried silage samples, lactic and isobutyric

acids were dominant, while dried unensiled samples had more isobutyric and acetic acids.

Although organic acids were generally high in “as is” samples, adverse effects on ethanol yield

were only observed when the acetic acid concentration was greater than 4 g/L of fermentation

volume and was also interacting with butyric acid in the fermentation broth.

The results show that drying adversely affected the amount of ethanol obtained. The ethanol

yields of Day 0 “as is” samples were about 10% more than yields from dried Day 0 samples. In

addition, the results suggest that low yields of dried silage was a result of partial acid

pretreatment during wet storage that resulted in pore collapse that restricted enzyme activity.

Ethanol yield of unwashed “as is” silage was about 34% more than yield from dried silage.

Fermentation was not allowed to proceed to completion, but it is doubtful that even if allowed,

yields of dried silage would be comparable to “as is” silage. Dried silage could maintain

structural composition after drying, but if there is restricted access to these sugars it could result

in significant loss of revenue. That revenue loss may not justify conversion of silage to a uniform

format through drying. Unground “as received” stover had very poor ethanol yields, perhaps due

to the large particle size. Although dried stover is more convenient for particle size reduction,

wet storage stover can be processed as is, using relatively large particles with good results, and

so can save size reduction cost.

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6.5 References

Budas Z., K. Dallmann, and B. Szajani. 1989. Influence of pH on the growth and ethanol

production of free and immobilized Saccharomyces cerevisiae cells. Biotechnology and

Bioenginerring 34: 882–884.

Danner, H., M. Holzer, E. Mayrhuber, and R. Braun. 2003. Acetic acid increases stability of

silage under aerobic conditions. Applied and Environmental Microbiology 69(1): 562–

567.

Dow. 2008. Assessing on-farm dry matter losses in silage making. Available at

http://msdssearch.dow.com/PublishedLiteratureDOWCOM/dh_012a/0901b8038012a40f.

pdf?filepath=silage/pdfs/noreg//003-09101.pdf&fromPage=GetDoc Accessed on 15

January, 2013.

Dowe N and J, McMillan. 2008. SSF experimental protocols: lignocellulosic biomass hydrolysis

and fermentation. Tech. Report NREL/TP-510-42630. Available at

http://www.nrel.gov/biomass/pdfs/42630.pdf National Renewable Energy Laboratory.

Esteghlalian, A. R., M. Bilodeau, S. D. Mansfield, and J. N. Saddler. 2001. Do enzymatic

hydrolyzability and Simons' stain reflect the changes in the accessibility of

lignocellulosic substrates to cellulose enzymes? Biotechnology Progress 17(6):1049-

1054.

Graves, T., N. V. Narendranath, K. Dawson and R. Power. 2006. Effect of pH and lactic or

acetic acid on ethanol productivity by Saccharomyces cerevisiae in corn mash. Journal of

Industrial Microbiology and Biotechnology 33(6): 469-474.

Grozdits, G. A. 1997. Biological treatment and storage method for wet bagasse for year-round

biomass supply. Newsletter of the International Cane Energy Network. Available at

http://www.scribd.com/doc/7027056/Bagasse-as-Alternate-Fuel Accessed 11 November

2008.

Hess, J. R., K. L. Kenney, L. P. Ovard, E. M. Searcy and C. T. Wright. 2009. Uniform-format

solid feedstock supply system: a commodity-scale design to produce an infrastructure-

compatible bulk solid from herbaceous lignocellulosic biomass. INL/EXT-09-17527.

Idaho National Laboratory (INL).

Holmes, B.J. and R.E. Muck. 2000. Preventing silage storage losses. University of Wisconsin-

Madison. Available at http://www.uwex.edu/ces/crops/uwforage/prevent-silage-

storage7.PDF Accessed on 5 May 2013.

http://msdssearch.dow.com/PublishedLiteratureDOWCOM/dh_012a/0901b8038012a40f.pdf?filepath=silage/pdfs/noreg//003-09101.pdf&fromPage=GetDoc

http://msdssearch.dow.com/PublishedLiteratureDOWCOM/dh_012a/0901b8038012a40f.pdf?filepath=silage/pdfs/noreg//003-09101.pdf&fromPage=GetDoc

http://www.nrel.gov/biomass/pdfs/42630.pdf

http://www.scribd.com/doc/7027056/Bagasse-as-Alternate-Fuel

http://www.uwex.edu/ces/crops/uwforage/prevent-silage-storage7.PDF

http://www.uwex.edu/ces/crops/uwforage/prevent-silage-storage7.PDF

187

Houghton, T. P., D. M. Stevens, P. A. Pryfogle, C. T. Wright, and C. W. Radtke. 2009. The

effect of drying temperature on the composition of biomass. Applied biochemistry and

biotechnology 153: 4-10.

Joosten, M. and M. Peeters. 2010. Yeast and fermentation: the optimal pH level. Available at

http://www.pieternieuwland.nl/Menu_Items/Projecten/Symposium/symposium2009-

2010/PVH/reports/PVH1_Maja_Myrthe_R.doc Accessed on 5 May 2013.

Kaliyan, N. and R.V. Morey, 2009. Densification characteristics of corn stover and switchgrass.

Trans. ASABE 52 (3), 907–920.

Kreuger, E., I. A. Nges and L. Björnsson. 2011. Ensiling of crops for biogas production: effects

on methane yield and total solids determination. Biotechnology for biofuels 4(1): 1-8

Kumar, A., J. Cameron, and P. Flynn. 2005. Pipeline transport and simultaneous saccharification

of corn stover. Bioresource Technology 96:819-829.

Porter, M. G. and R. S. Murray. 2001. The volatility of components of grass silage on oven

drying and the inter‐relationship between dry‐matter content estimated by different

analytical methods. Grass and Forage Science 56(4): 405-411



Selig, M., N. Weiss and Y. Ji. 2008. Enzymatic saccharification of lignocellulosic biomass:

Laboratory analytical procedure. Tech. Report NREL/TP-510-42629 (LAP). Golden,

Colo.: National Renewable Energy Laboratory.

Weißbach, F. and C. Strubelt. 2008. Correcting the dry matter content of grass silages as a

substrate for biogas production. Ref. No. LT 08421 Available at

http://www.nawaro.ag/87 Accessed 21 May, 2013

Xu J, M. H Thomsen, and A. B Thomsen. 2010. Investigation of acetic acid- catalyzed

hydrothermal pretreatment on corn stover," Applied Microbiology & Biotechnology

86(2): 509-516

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http://www.nawaro.ag/87

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Chapter 7

Quality indices and model: predicting biofuel yield and cost based on storage

conditions

Abstract

Although wet storage has been recognized as a potential storage option, its geographical

coordination and its impact on supply chain management or biofuel output has not been well

addressed. Several logistics models have been developed over the past two decades. However,

most of these models are limited as they focus exclusively on dry storage systems and ignore the

wide range of dry matter losses that are likely to occur in a compromised wet or dry storage

system. Dry matter loss may have other implications that may negatively impact biofuel

production. This chapter describes a process cost and ethanol prediction model, which was

developed using relationships derived from studies in Chapters 3, 5 and 6. The model

accommodates 5 dry storage and 5 wet storage configurations. The model gave reasonable

results and showed that low moisture wet storage feedstocks (<45% moisture) had lower

feedstock delivery cost and better ethanol yield compared to corresponding dry storage

feedstock. Dry feedstocks that are kept dry had economic advantages over the high moisture

silage system at ≥45% moisture. For long storage durations, any compromising event in a dry

storage system that increases moisture content above 30% can be very prohibitive in terms of

ethanol production.

Key words: Logistics, delivery cost, ethanol, biofuel, wet storage, dry storage,

7.1 Introduction

As advances are made in the utilization of lignocellulosic materials for biofuel and possible

commercialization, two key players and their roles in the sustainability of lignocellulosic biofuel

production are recognized. These two players, the farming industry (responsible for supply of the

lignocellulosic material) and the biorefining industry (who would convert this material into fuel)

may have a common concern – determination of the true value of the feedstock. Since most

biomass resources like agricultural residues are seasonal, storage becomes critical and an

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important determinant of feedstock value. This value can be evaluated from two perspectives: (1)

Dry matter loss and feedstock composition, which will determine the amount of convertible

sugars present and (2) cost of post-storage processing due to storage impacts on feedstock

quality. The value determined upstream during storage could be directly correlated with the

downstream value in terms of biofuel quantities, costs, and conversion efficiency.

The concept of quantifying feedstock quality or value from an end user perspective is not new. In

the livestock industry, two main indices – the Relative Feed Value (RFV) and Relative Forage

Quality (RFQ) – were developed to measure and define the quality of forages. RFV has been

used for decades and may eventually be replaced by RFQ (Peterson, 2002; Jeranyama and

Garcia, 2004), but both systems are similar in that they use a range of numerical values that

categorize feedstocks into grades. These values are determined from calculations that in the case

of RFV involve potential dry matter (feedstock) intake and digestible dry matter and in the case

of RFQ, total digestible nutrients. The RFV has been useful to both livestock farmers (end users)

and feedstock sellers (forage producers) as a tool for pricing feedstock, comparing different

forages, and predicting animal performance (Jeranyama and Garcia, 2004). A similar index to

ensure fair pricing of feedstock and predict biofuel yield is needed for the biofuel industry. Such

a tool may be of particular interest to those embracing wet storage systems. From Chapters 3, 4,

5 and 6, it can be assumed that the key variables informing this index would be dry matter loss

and the organic acid profile. It would be helpful and reassuring for biorefineries to have a prior

expectation of how wet storage outcomes compares with conventional dry storage, but this

information is missing from the literature at present.

In the livestock industry, silage (feedstock from wet storage) generally reaches a stable state

(anaerobic and pH <4.5) within a few days. Although this is mainly related to microbial

dynamics and fermentative stability, it is expected that there is continuation in chemical reactions

throughout the ensilage process, as organic acids interact with the feedstock. These chemical

reactions are very slow. Traditionally, the goal of wet storage is to conserve the quantity and

quality of the ensiled feedstock. For biofuel production, previous chapters have shown an

additional benefit is the enhancement of feedstock quality through interactions of these acids

with structural components that contribute to better downstream outcomes. The in-situ wet

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storage pretreatment that may result in reduced severity for subsequent post storage pretreatment,

could translate to significant cost saving benefits. In Chapter 5, pretreatment of wet stored

feedstock was just as effective at 5 minutes as it was at 15 minutes. The minimum retention time

at which corresponding unensiled samples gave a comparable outcome was 10 minutes. Because

pretreatment capital as well as operating costs are estimated at roughly one third the cost of

biofuel conversion, doubling the throughput of this process could substantially reduce the costs

of fuel.

Feedstock quality and expected outcome, however, is only one side of the coin. The other side is

the logistics of getting the feedstock to the biorefinery. Both conventional dry storage and

emerging wet storage methods have several configurations that can affect the supply and

delivery cost of the feedstock. Biomass feedstock supply and logistics have become important

foci of biomass engineering research and analysis after decades of a focus on conversion:

tackling and seeking access to the abundant yet recalcitrantly embedded sugars in lignocellulosic

feedstock.

A number of biomass supply studies, projects and logistics models have been developed to

elucidate and in some cases address biomass supply chain challenges. Most of these models are

prescriptive, suggesting or recommending the nature of expected collaborative relationships and

efficient task distribution/coordination among players in the lignocellulosic biofuel industry.

Some of these models explore specific geographical or spatial systems configurations of ‘farms –

storage locations – refinery’ to facilitate the planning, assignment and coordination of feedstock

supply, as well as integration of activities from harvest to end product. Geographical system

configurations are based on assumptions of central or satellite storage systems, and the models

developed are used to determine the optimum number and optimum locations of these storage

systems, optimal radius of supply or optimal locations and capacity of the biorefinery (Judd et

al., 2010; Marvin et al., 2011; Brownell and Liu, 2012; Cundiff and Grisso, 2012). Usually, the

whole logistics chain is considered, embracing multiple farms and (conventional dry) storage

units required to meet a biorefinery annual feedstock demands (Mukunda et al., 2006; Cundiff et

al., 2009; Morey et al., 2010).

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Ebadian et al. (2013) developed a combined optimization and simulation model that gave similar

outputs described above, prescribing the optimal number and locations of farms and storage

units, supply radii, etc. In addition, they focused on analyzing storage systems and their impacts

on the costs incurred by various actors in the supply chain. But again, the storage systems

analyzed were dry storage units, either open or enclosed. Generally, the logistics of wet storage

systems have not been elaborated in existing models, especially as they compare with dry

storage. A rare study that provides a basis to compare the logistics of wet and dry storage is that

of Turhollow and Sokhansanj (2007), who estimated the harvest, storage and delivery cost from

bunker and large pile wet storage systems. They found that feedstock moisture up to 55% had no

impact on transport cost when rail is used and to have reasonable transport cost when truck is

used, feedstock moisture has to be less than 70%.

There are, however, a number of non-logistics studies that compare wet and dry storage. These

studies have compared wet and dry storage focusing on: harvesting efficiency and capacity, as

well as dry matter loss during storage (Shinners et al., 2007); greenhouse gas emissions due to

dry mass losses in storage (Emery and Mosier, 2012); micro-structural changes in feedstock

(Donohoe et al., 2009; Oleskowicz-Popiel et al., 2010; Liu et al., 2013); in situ silage

pretreatment effects on ethanol yield without post storage pretreatment (Oleskowicz-Popiel et al.,

2010); and responses to post storage pretreatment and/or hydrolysis (Chen et al., 2007; Liu et al.,

2013). Most of these studies analyzed wet storage at only one moisture level.

The aim of this study is three-fold, to: (1) classify experiment data to lay the foundation for

development of a wet storage quality index; (2) present a simple model that looks at corn stover

logistics and ethanol production cost mainly through field losses, cost of storage, storage losses,

delivery cost, compositional changes, and ethanol yield, and ultimately estimates a minimum

ethanol selling price (MESP); and (3) provide a basis to evaluate the comparative logistics and

ethanol profitability of different dry and wet storage systems/configurations.

One limitation in existing logistics models is the focus on dry storage systems. Also, in cases

where moisture content or dry matter loss inputs/parameters are required, most models use a

single value. This model analyses both wet and dry storage biomass logistics supply chains and

allows for a wide range of moisture contents. In addition, this model predicts and incorporates a

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wide range of dry matter losses based on moisture content, storage duration and storage

type/configuration.

7.2 Methodology

7.2.1 Classification for quality index or indices

Part of the purpose for the series of experimental investigations in this current study, documented

in Chapters 3 to 6, was to determine the conditions of storage that meet the goal of maximum

biofuel yield at minimum cost. Besides the natural pretreatment capability of wet storage

systems, wet storage presents an avenue for low cost, low severity upstream pretreatment

through the use of additives. This study, however, focuses on plain (unamended) silages. Four

main outputs were investigated in Chapter 3 (Dry matter loss and feedstock composition),

Chapter 4 (response of feedstock to hydrolytic enzymes, measured in sugar yield), and Chapters

5 and 6 (ethanol yield). To examine the pattern and relationship between these variables and

organic acids, Agglomerative Hierarchical Clustering (AHC) analysis was performed using

XLSTAT (Addinsoft, New York, NY), a Microsoft Excel add-in. This analysis provides a basis

for defining a more precise relation to define feedstock quality.

7.2.2 Modeling Approach

The model presented in this chapter is structured in two parts. Figure 7.1 gives the general idea

behind the model. The first part, a “farm-to-refinery-gate” process cost model, provides options

of harvest and storage conditions that can impact feedstock delivery cost through dry matter

losses. This first part estimates dry matter losses in the field and during storage as well as the

minimum feedstock delivery cost required to break even. The assumption is that the farmer is

responsible for collection and storage of the feedstock, as well as delivery to the biorefinery. The

model user can choose [or enter] harvest date, harvesting operations, storage type/configuration,

storage duration and distance to the biorefinery. The user can choose from 5 wet storage

configurations and 5 dry storage configurations. The storage systems can be analyzed at

moistures levels of up to 85% wet basis. Four different stacking options are also available for

bale storage. Stacking method can affect the storage space requirement. The input options are

provided as direct input (i.e. user enters value using keyboard), through drop-down menus and

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check boxes. Figure 7.2 shows the inputs and outputs of the model. Appendix F, Figure F1

shows the model input interface for the process cost model. Dry matter loss is calculated based

on storage moisture, storage duration and storage configuration. Feedstock delivery cost is also

affected by these factors as well as dry matter losses (incurred during harvesting and collection

operations and during storage) and distance to the refinery.

The second part of the model is an ethanol yield prediction model. Ethanol yield is estimated as

percentage of theoretical yield based on organic acid content of feedstock after liquid hot water

(LHW) pretreatment. Three organic acids (acetic, butyric and isobutyric) that showed significant

correlation with ethanol are used in the yield estimation. Although there was a good direct

regression relation between storage moisture content and these pretreatment acids, pretreatment

acid was estimated from storage acid so as to account for acid variability with storage moisture

and duration. Regression equations were developed using acetic and isobutyric acid to predict

ethanol conversion as percentage of theoretical. Since butyric acids were generated in only some

of the 75% moisture samples pretreated at 5 and 10 minutes, the “RANDBETWEEN” function

in Microsoft Excel is used to factor in this chance. The amount of sugar present in the feedstock

and used in calculating the theoretical yield is a function of storage method, storage moisture

content and storage duration. Currently, the model assumes all dry storage systems undergo the

same compositional changes during storage. Similarly, all wet storage systems also undergo the

same compositional changes. Compositional changes in dry storage systems are different from

those of wet storage systems and are defined using relationships developed in this study (see

Appendix F, Table F8). Yield factors are then applied to the yield to correct for the following

preprocessing options when applicable: drying feedstock effect, drying silage effect, particle size

effect and washing effect. See Appendix F, Table F9, for yield factor equations and all other

equations used in the model development. References for equations and various parameters are

also provided in Appendix F. The pretreatment times are limited to times tested in this study – 5,

10 and 15 minutes. The user has the option to estimate sugar (glucan and xylan) composition and

dry matter loss if feedstock pre-production contract exist between farmer and biorefinery or

allow model estimate these parameters. The model uses the Microsoft Excel platform in selecting

and combining various processes/operations, in performing calculations and for graphical

presentation of outputs.

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Figure 7.1: Relationship between various parameters informing the model output. (1 and 2 are

the two parts of the models. The first draws on dry matter loss and moisture change data

developed in this study and the second draws on compositional changes and organic acid effect

on downstream processes)

Most of the relationships used in this model were derived from Chapter 3, 5 and 6. See Appendix

F, Tables F8 and F9. In Chapters 3, 5 and 6, experiments were performed to determine the

relationships between storage conditions and effect on dry matter loss, feedstock composition,

response to pretreatment and ethanol yield. Two feedstock types were used in these experiments.

From empirical data obtained from these experiments, regression equations were developed

using mean data from one feedstock type. Data from other feedstock was used in model

validation. These regression equations together with cost data from the literature were

formulated into a process model that estimates the delivery cost of feedstock to a biorefinery or

the cost of ethanol production. For ethanol costing, fixed cost and operating cost data were

mainly from the NREL (2010) Ethanol Production Process Engineering Analysis spreadsheet

195

and a few estimations from Hofstrand (2009). Pretreatment equipment from the NREL

spreadsheet was for the dilute acid process. To estimate equipment cost for the liquid hot water

process, all equipment costs directly related to handling sulfuric acid were subtracted. Tables

F11 and F12 in Appendix F contain the capital cost components and operating cost components

used in the model.

In Chapter 3, three storage variables – moisture, temperature and duration - were investigated for

both aerobic and anaerobic conditions. Temperature effects on dry matter loss were found to be

insignificant. In addition, a number of studies in real silo systems have shown that although there

were changes in storage temperature from time of ensiling until the time of removal, these

changes did not affect silage quality in any appreciable way (Bechdel, 1918). These studies

measured wall and center temperatures during ensilage. Temperature variation over time and at

the different locations within the silo ranged from less than 10oC to 60

oC and in one case, up to

73oC. Temperatures above 38

oC were usually near-top temperatures or cases with some

allowance for air infiltration. Kung (2008) also noted that normal silage temperature could be as

high as 35oC in large piles and up to 54

oC at shallow surface locations or when loosely packed.

Silage temperatures exceeding 43 - 49oC should be indicative of active aerobic deterioration, as

it is only under aerobic conditions that significant heat can be generated (Kung, 2008). Samples

used in this study were placed in controlled environments, and core temperatures were not

measured. Aerobic indicators showed that active aerobic deterioration in was virtually absent

under our anaerobic ensiled storage conditions, hence the effects of temperature were ignored in

the model.

Most of the assumptions regarding field moisture and cost of field operations used in the study

are based on Pennsylvania farming systems. The model does not go into details of harvest

equipment and the various factors that affect performance and cost. Rather it uses custom rates

for the various operations. That is, the farmer does not own the equipment but outsources the

required services. This is based on average farm size in Pennsylvania (~231 acres), which is

smaller than the size that makes economic sense to own many types of equipment (Brechbill and

Wallace, 2008). The PA custom rates used in the model were from Pike (2013). This model does

not address the logistics of how many trucks are required to deliver feedstock, optimization of

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packing arrangement, the rate of delivery, feasibility of delivering daily refinery targets, traffic

congestion or delays that could lead to further loss in dry matter or other intangible cost during

transportation, queuing or handling.

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Figure 7.2: Inputs and outputs of process cost and ethanol prediction model. Model equations and provided parameters are in

Appendix F, Tables F6 to F10.

Inputs Outputs Inputs Outputs

Farm size From first part of model Plant nameplate capacity (gal/yr.)

Stover yield (dry tons) Amount of stover required (dry tons)

Percentage harvested Amount of stover supplied (dry tons)

Moisture content at Harvest (% w.b) From what storage configuration

Amount of feedstock harvested (tons) Feedstock cost ($)

Amount feedstock collected (tons) Operating capacity of plant (gal/yr.)

Cost of field operations ($) Fixed Nameplate capacity (Ethanol, gals/yr.) Ethanol production cost ($/ton)

Amount of feedstock lost on field (tons) but Biorefinery fixed cost components Feedstock cost ($/ gal)

Storage duration (days) can be changed Biorefinery operating cost components Feedstock cost ($/ ton)

Storage configuration Cost of feedstock as percent of production cost (%)

Amount of feedstock going into storage (tons) Choose Who oversees off-site storage (Farmer or Biorefinery) Theoretical ethanol yield (gals)

Number of bales Preprocessing options Theoretical ethanol yield (gals/ton)

Moisture content at start of storage (% w.b) Pretreatment time Actual ethanol yield (gal)

Cost of storage structure ($) Fermentation capability: C6 or C6&C5 sugars Actual ethanol yield (gal/ton)

Amount of feedstock lost in storage, mean (tons) Actual ethanol yield as percentage of theoretical

Amount of feedstock lost in storage, lower l imit Model estimation Percentage glucan and xylan Effect of storage loss on production cost

Amount of feedstock lost in storage, upper limit tools (Estimate from storage type and moisture at start of storage) Net Present Value (NPV)

Cost of storing feedstock Amount of feedstock required to meet nameplate capacity Minimum Ethanol Selling Price ($/gal)

Moisture at end of storage period (%) (Estimate from nameplate capacity and % sugar in feedstock) Ethanol production cost ($/gal)

Distance to biorefinery (miles) Number of farms to required to supply feedstock

Cost of transportation to biorefinery (Estimate from feedstock required and amount produced per farm)

Amount of feedstock reaching refinery Amount of ethanol produced Graphical output

Total cost (Harvest to delivery) (Estimate from feedstock supplied and sugar composition; Ethanol yield (gals/yr.)

Total cost per ton of feedstock collected from percentage theoretical amount estimated using organic acids Ethanol cost ($/gal)

Total cost per ton of feedstock leaving storage produced during pretreatment, and pretreatment time) Feedstock cost (% of total cost)

Percentage dry matter loss in storage Cost of producing ethanol Internal Rate of Return (IRR)

Minimum delivery cost (break even) ($/ton) Minimum Ethanol Selling Price (MESP) Dry matter loss (Chosen system vs. wet storage)

Potential monetary loss ($) (That is when Net Present Value (NPV) = 0) Transportation cost

Comparison of various cost component

Feedstock delivery cost estimation model Ethanol cost prediction model

Feedstock supply frameworkFarm size

Crop yieldHarvest cut height

Predict field moisture Estimate percentage harvested

Harvesting and collection Operations

Custom rates of operationEstimate field losses

Estimate cost of operations

Storage Storage type, moisture,

duration, stacking configurationMass of feedstock storedEstimate structural cost

Estimate moisture changeEstimate dry matter loss

TransportationTransportation distance

Estimate mass of feedstock transported

Estimate transportation cost

Storage durationStorage configuration

Moisture at start and end of storageAmount of feedstock reaching refineryMinimum delivery cost

Harvest to delivery

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7.2.3 Notes on some model process components

7.2.3.1 Harvesting and collection

Wide variations in harvesting and collecting efficiencies have been reported ranging from about

30% to 95% (Lang, 2002; Sokhansanj and Turhollow, 2002; Shinners et al., 2003; Prewitt et al.,

2007). The machine type, the cutting blades, the types and sequence of operation and the

moisture content of the feedstock are some of the factors that affect the amount of feedstock that

can be collected. Moisture content can affect collection at various levels but generally, collection

is better at higher moisture. Gathering and pick-up losses, as well as shatter losses are lesser at

higher moisture. The implication is that silage harvest and collecting efficiencies are usually

higher than that of dry bales, with up to 41% more yield (Shinners et al., 2003). Modeling the

factors that impact on efficiencies can be complex, especially when interactions among the

factors are considered. This model uses a simplified scenario where the amount collected is

based on harvesting operations chosen with the assumption that machine configuration and

parameterization are adequate. The percentage of feedstock harvested is determined from the

harvest cutting height using the equation derived by Wilhelm et al. (2011), Equation 7.1.

Moisture content regression is based on the model of Igathinathane et al. (2006) using non-

dimensional height and “days after sowing grain”, and was remodeled using derived variables of

percentage harvested and “days after grain harvest”. This new regression, Equation 7.2, is used

in determining moisture content at time of harvest. Alternatively, if “days after grain harvest” is

unknown but harvest date is known, a simple linear regression equation developed from data in

the Penn State Biomass Conversion Laboratory (The Richard lab) (Equation 7.3) is used to

calculate moisture. The Richard lab is where the research reported in Chapters 3, 4, 5, and 6 was

conducted.

7.1

Where (

), is to account for variability in plant

height

199

7. 2

Where H = Percentage stover harvested

D = Days after grain harvest

7.3

7.2.3.2 Storage

The storage method determines the harvest operations and dry matter losses both on field and in

storage. Storage is therefore critical in determining feedstock cost. During storage, especially dry

storage, there are varieties of interactions that can influence dry matter losses. These include the

mechanical damage during harvesting/collection, cardinal direction of the storage structure, the

spatial arrangement of bales, environmental conditions, initial feedstock quality, moisture and

density. Dry matter losses determined in this research are assumed to be representative of indoor

storage systems and vertical silo system for dry and wet storage systems respectively.

Drawing rough relationships from the dry matter losses reported by Huhnke (2003) and Saxe

(2007), the model estimates dry storage losses using the relational assumptions in Appendix F,

Tables F6 and F7. These dry matter loss factors are the ratio of mean values obtained for indoor

storage to the mean values of other configurations in Huhnke (2003) and Saxe (2007). It is

assumed that these values take into account weather variations, especially rainfall. Losses due to

lodging from wind and rain storms are ignored. Rough relationships were also drawn for wet

storage using dry matter losses in Bickert et al. (2000). Dry matter loss, to which the dry matter

loss factors are applied, is estimated from Equations 7.4 and 7.5, developed from this research.

The upper and lower 95% confidence limits for dry matter loss were also calculated. In addition

to dry matter loss, equations representing change in moisture content during storage were also

developed (see Equations 7.6 and 7.7).

200

{

⁄

7. 4

{

⁄

7.5

{

7.6

{

7.7

Dry storage structural cost are estimated using cost ($) per square foot values in Wilkerson

(2012). The area used for storage is dependent on the stacking configuration. Four stacking

configurations options in the model are (1) pyramid pattern for round bales; (2) overlapping,

alternating placement for square bales; (3) End stacking – vertically and (4) End stacking –

horizontally. Wet storage structural costs are estimated using relationships in Equations 7.8 and

7.9 developed from data from Reilly (2011).

7. 8

201

7.9

7.2.3.2 Transportation

Custom rates existing for transportation are usually for short distances within farm or from farm

to storage. The 2008 Nebraska farm custom rate (Jose and Bek, 2008) gave an average of ~$3.41

per loaded mile and a most common rate of $4 per loaded mile, for bales up to an average 21.5

ton-load. For silage, the average rate was $3.28/ton and a most common rate of $2.75/ton.

However, when mileage exceeded 2.75, an additional $5 was charged per ton. In 2012 custom

rates, round bales maintained the common rate of $4, for up to an average 18 tons, but square

bales had a lower rate of $2. For silage, the most common rate per ton increased to $3 but the

additional charge when mileage exceeded 2.14 was on average $0.23. Iowa 2013 custom rate

survey gave average cost per bale as $2.85 and $3.55 for large round and square bale

respectively (Edwards et al., 2013). Most logistics economic models use assumed transportation

rates. These rates are varied and reported in various units like $/ton, $/mile or loaded mile,

$/mile/truck, $/day/truck, $/truck and $/ton-mile. In addition, there is a general suggestion that

transportation cost increases with mileage (Gallagher and Johnson, 1999; Schechinger and

Hettenhaus, 1999; Perlack and Turhollow, 2002) although Petrolia (2008) assumes it decreases

with mileage. This model estimates transportation cost using cost per [up to 125] miles

relationship in Schechinger and Hettenhaus (1999), which was based on dry tons and adjusted it

using a $2.33/ton rate for within 25 mile (Gallagher and Baumes, 2012) to get a conversion ratio

for more current cost based on wet tons. The following relationship in Equation 7.10 was

developed for transportation cost:

7.10

The $/ton-mile cost from this relationship was 0.16 for 0 - 5 miles to 0.32 for 125 miles.

Estimates from Petrolia (2008) were 0.26 for 25 miles or less to 0.18 for greater than 99 miles.

Gallagher and Baumes, (2012) estimated $/ton-mile to be 0.14 for up to 25 miles.

202

7.3 Results

7.3.1 Quality index

Dry matter loss data (Chapter 3), glucose yield from fiber reactivity test (Chapter 4) and ethanol

yield (Chapters 5 and 6) were classified into three groups, each with an associated organic acid

profile (see Appendix F, Tables F1 to F5). A simple and quick predictive tool that relates storage

conditions or organic acid profiles and dry matter loss resulting from different storage conditions

to feedstock composition and ethanol yield would be ideal. This would save feedstock suppliers

and buyers the time and money that is required by traditional analytical methods that are

laborious. In addition, it would provide a basis for feedstock suppliers and buyers (both of whom

are key players, with critical roles in the commercial viability of lignocellulosic biofuel

production) to predict feedstock quality, and as a result, create fair and efficient markets for this

new renewable energy commodity.

The wide variability in dry matter losses and organic acid profile resulting in overlapping

outcomes requires a stochastic approach that factors in the randomness of experimental results.

Working with mean values of classification outcome, a qualitative matrix is presented in Figure

7.3. The use of low, high and moderate terms in this classification scheme are relative. Organic

acid in this matrix represents the complete portfolio of storage acids. See Appendix F, Tables F1

and F5 to see how storage and pretreatment acid are related. The qualitative matrix suggests that

lactic acid silage is generally more desirable in biofuel wet storage systems, similar to the

livestock industry. High lactic is associated with high glucose yield during fiber reactivity test

and high ethanol yield when silage post-storage processes are carried out without first drying the

feedstock. On the contrary, high acetic acid is associated with low ethanol yields. For acetic and

Isobutyric acids, “low” and “high” levels in most cases reflect few extreme samples, with respect

to impact on ethanol yield. However, these extreme samples drastically reduce ethanol yield

resulting in the overall negative correlation.

203

Organic acids: Low = < 1% DM; Moderate = 1 - 2% DM; High = >2% DM

For dry matter loss, glucose and ethanol, high, moderate, low are relative to each other. See Appendix F, Tables F1 to F4 for quantification

Pattern fill represents relative difference in each acid type (defined vertically by low, moderate or high) within a particular rectangle that relates it to dry matter loss, glucose or ethanol. For example, acetic acid amount is low for both high and moderate yield of glucose. However, amount corresponding to high glucose yield is closer to 1%, while amount corresponding to moderate glucose yield is closer to 0%., hence differentiated by their pattern fill. Relatively, high moderate low

Arrows in same direction indicate proportional relationship; opposite direction indicate inverse relationship. Dotted arrows indicate probable relationship

#: There are 28 possible combinations of acid levels, however in this study, only the 9 combinations listed show some relationship with dry matter loss, glucose or ethanol.

Figure 7.3: Qualitative relationship between organic acids from wet storage of corn stover, dry

matter loss, glucose and ethanol yields.

7.3.2 Process cost model – field to farm gate outputs

The Output for the process cost model (part 1) gives a graphical comparison of dry matter loss in

any chosen storage system against wet storage at moistures of 25% to 85%. Figure 7.4 is an

Dry matter

loss

Glucose yield

without

pretreatment

# Lactic Acetic Isobutyric Silage "As is" Dried silage

1 Low High Moderate High low

2 High Low Low Moderate

3 Moderate Low Low low

4 High Low Low High Moderate

5 Low Low Low Moderate

6 Low High Moderate Low Low

7 Moderate Moderate Low Moderate

8 Moderate Low Low High

9 High Low High High

Independent variables Dependent variables

Storage organic acids

Ethanol yield (%

theoretical)

204

example of the output window. Other graphical comparisons are delivery cost per ton and

different cost components of the logistics supply chain.

Figure7.4: Sample output window of process cost model (part 1). The output displayed here

compares indoor dry storage at 35% moisture to wet storage, particularly, 35% and 65%

moisture. Values in red are losses.

The results depicted in Figure 7.4 show indoor dry storage at 35% moisture has an advantage

over ensilaged feedstock in a tower silo of the same moisture, but this advantage is only for

storage and transportation cost. The high dry matter loss associated with this storage system

resulted in higher delivery cost. Comparisons of indoor dry storage and wet storage at 25%

moisture (not shown) indicate that the wet storage system had slightly lower delivery cost than

dry storage of same moisture level. The delivery cost of the latter was comparable to that of wet

You chose Dry storage System for Analysis

Farm size 500

Stover yield (dry tons) 1722

Percentage harvested 90.0

Moisture content at Harvest (%) 57.3

Storage duration (days) 360

Storage configuration Under roof, enclosed barn

Distance to biorefinery (miles) 25

Dry ton $ $/ dry ton

Amount of feedstock harvested 1,549.8

Amount feedstock collected 1,217.6

Cost of field operations 27,064.95 22.23

Amount of feedstock lost on field 332.2 4,668.00

Amount of feedstock going into storage 1,215.9

Number of bales 439

Moisture content at start of storage (%) 35.0

Cost of storage structure 17,922.00

Cost of storing feedstock 4,516.34 2.41

Amount of feedstock lost in storage, mean 345.16 8,505.74 24.64

Amount of feedstock lost in storage,lower limit* 319.73 7,879.09 24.64

Amount of feedstock lost in storage, upper limit* 370.65 9,134.06 24.64

Moisture at end of storage period (%) 5.0

Cost of transportation to biorefinery 5,394.45 6.18

Cost per wet ton = 5.87

Amount of feedstock reaching refinery 872.4

Total cost (Harvest to delivery) 36,975.74

Total cost per ton of feedstock collected 30.37

Total cost per ton of feedstock leaving storage 42.38

Min Mean Max

Percentage dry matter loss in storage 26.26 28.35 30.44

Minimum delivery cost (break even) ($/ton) 41.18 42.38 43.66

Potential monetary loss ($) 16,683.33 17,033.55 17,405.83

* Based on 95% confidence interval

Back to inputs

DRY MATTER LOST and DELIVERY COST ESTIMATION

-50.00

0.00

50.00

100.00

150.00

200.00

250.00

300.00

350.00

400.00

5 15 25 35 45 55 65 75 85

Dry

mat

ter

loss

(to

ns)

Moisture content (%)

DMLmean

DML upper confidence limit

DML lower confidence limit

Your mean storage loss

Your minimum storage loss

Your maximum storage loss

Wet storage in vertical siloFarm size = same as yoursPercentage harvested = same as yoursStorage duration = same as yours

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

5 15 25 35 45 55 65 75 85

De

live

ry c

ost

($

/to

n)

Moisture content (%)

Min delivery cost

Mean delivery cost

Max delivery cost

Your mean delivery price

Your min delivery cost

Your max delivery cost

Wet storage in vertical siloFarm size = same as yoursPercentage harvested = same as yoursStorage duration = same as yours

COMPARATIVE CHARTS

0.00

10.00

20.00

30.00

40.00

50.00

60.00

Fieldoperation

Storage Transportto

Biorefinery

per tonfeedstock

collected

Deliverycost

Co

st (

$)

Your storage

35% Wet storage

65% Wet storage

205

storage at 35% moisture. Dry storage typically should be at moisture levels of 25% or less, in

which case dry storage would have a lower delivery cost when compared to wet storage at higher

moistures. What this model shows is that dry stover presents a delivery cost advantage over wet

storage only if feedstock is properly stored at the right moisture and protected from moisture

absorption from rainfall events. This advantage also holds when compared to high moisture

(>35%) wet storage. Comparison of delivery cost estimates for some storage configurations are

shown in Table7.1.

Table 7.1: Comparison of some model outputs of six storage configurations

Storage moisture = 35%

Storage duration = 180 days

Dry matter loss (%)

in storage

Minimum feedstock delivery cost ($)*

Ethanol (% theoretical)

Ethanol production

cost ($)

Feedstock cost (% of total cost)

MESP ($)†

On-field dry storage (uncovered) 78.24 108.33 58.3 2.5 66.2 2.69

Enclosed dry (barn) storage 17.39 37.58 58.3 1.4 40.5 2.56

On-field dry storage (covered) 26.08 36.25 58.3 1.4 39.6 2.56

Wrapped silage (Baleage) 1.30 21.76 72.0 1.0 27.6 2.61

Vertical silo 0.65 30.72 72.0 1.1 35.0 2.62

Bunker silo 1.82 22.71 72.0 1.0 28.5 2.61

* The delivery cost includes cost of harvesting and collection operations, cost of storage and eventual

transportation to the refinery and accounts for feedstock losses at the various logistics units; does not

include growers payment or payment for nutrient removed from field.

† MESP = Minimum Ethanol Selling Price, calculated when net present value is zero, however the

internal rate of return for the different configurations are different: 5%, 27%, 28%, 44%, 41%, 44%

respectively as listed in table)

7.3.3 Ethanol prediction and cost model – biorefinery outputs

Figure 7.5 is a sample output from the ethanol prediction model. Enclosed dry storage at 35%

moisture is compared to wet storage at 35% and 65% moisture. Although the production cost per

206

gallon ethanol from dry storage was higher than wet storage at these conditions, feedstock cost as

a percentage of total cost and MESP was lower. Conventional dry storage is however

recommended at moisture levels less than 25% and at most 25%. Using the model, the net

present value (NPV) if feedstock is sourced from an enclosed dry storage facility is about $435

million with an internal rate of return of 31%, if initial [starting] storage moisture is 25%. If

feedstock is sourced from a wet storage facility of 35% and 65% moistures, the NPVs would be

about $994 million and $493 million with IRR of 61% and 34% respectively. The results indicate

that wet storage at 35% moisture is preferable to dry storage at 25% moisture. These calculations

are based on the following scenario: an initial capital investment cost of more than $199 million;

half the capital cost is borrowed and payback period is 10 years at an interest rate of 8.5%, which

is also used as the discounted rate; and a 20-year time frame.

For field storage that is uncovered, when moisture content exceeds 31% at duration greater than

314 days, MESP cannot be determined because cash flow was always negative over the life of

the plant. Also NPV is negative when storage duration exceeds 270 days. These indeterminate

and negative values are a result of the very high dry matter loss under this storage configuration.

NPV remains negative even when future values are not discounted. For enclosed dry storage,

when moisture content is 48% at durations greater than 240 days, NPV becomes negative and at

durations greater than 339 days, cash flows become constantly negative and determination of

MESP becomes impossible. For indoor storage, at 48% moisture with 339-day storage, the cost

of production of ethanol was about $3.1/gal compared to $1.3/gal at 25% moisture. The

feedstock delivery cost at this 48% moisture level range from about $73/ton to $81/ton.

207

Figure 7.5: Sample output window of ethanol prediction model (part 2). The output displayed

here compares indoor dry storage at 25% moisture to wet storage at 35% and 65% moisture.

Values in red are losses.

7.3.4 Model validation

The process cost model and ethanol prediction model were validated mainly in three ways: (1)

by checking the predictive performance of different key components of the model, (2) through

sensitivity analysis and (3) comparison with other observations and predictions from literature.

Additional evaluation methods include calculation of the mean prediction error, root mean

square error (RMSE), mean absolute error (MAE) and the statistical significance of the

You chose Dry storage System for Analysis

Plant nameplate capacity (gal/yr) 70,000,000

Amount of stover required (dry tons) 694,140

Amount of stover supplied (dry tons) 606,654

Feedstock cost ($) 19,676,078

Form what storage configuration Under roof, enclosed barn

Operating capacity of plant (gal/yr) 42,319,069

Preprocessing

2.57

1.3

89.89

0.46

32.4

36.1

Ethanol, market price ($) 2.97

Theoretical ethanol yield (gals) 43,529,188.5

Theoretical ethanol yield (gals/ton) 71.8

Actual ethanol yield (gal) 42,319,068.75

Actual ethanol yield (gal/ton) 69.76

Actual ethanol yield as percentage of theoretical 97.2

Effect of storage loss on production cost

Initial investment ($) 199,099,234.1

Operational cost/year ($) 54,533,681.5

Net Present Value (NPV) ($198,297,281.69)

Feedstock cost ($/ ton)

Cost of feedstock as percent of production cost (%)

Back to inputs

Minimum Ethanol Selling Price ($/gal)

Ethanol production cost ($/gal)

Ethanol production cost ($/ton)

Feedstock cost ($/ gal)

Fermentation of C5 and C6 sugars

ETHANOL YIELD and PRODUCTION COST

0%

10%

20%

30%

40%

50%

60%

70%

Operatingcapacity for

suppliedfeedstock

Nameplatecapacity, for

suppliedfeedstock

Silage at 35%,no

preprocessing

Silage at 65%,no

preprocessing

IRR

1.29

0.970.87

1.25

2.572.71 2.71 2.69

0.00

0.50

1.00

1.50

2.00

2.50

3.00


suppliedfeedstock


suppliedfeedstock

Silage at 35%, nopreprocessing


Eth

ano

l ($

/Gal

)

Ethanol Productioncost($/gal)

Minimum Ethanol SellingPrice (MESP)

COMPARATIVE CHARTS

0.00

10,000,000.00

20,000,000.00

30,000,000.00

40,000,000.00

50,000,000.00

60,000,000.00

70,000,000.00

80,000,000.00Et

han

ol

(Gal

lon

s)

Operating capacity forsupplied feedstock

Nameplate capacity, forsupplied feedstock



36.1

48.0

42.2

58.0

0

10

20

30

40

50

60

70


suppliedfeedstock


suppliedfeedstock



Pe

rce

nta

geo

f to

tal c

ost

(%

)

Feedstock cost (% of total)

Operating capacity for suppliedfeedstock

Nameplate capacity, for suppliedfeedstock

Silage at 35%, no preprocessing

Silage at 65%, no preprocessing

208

prediction error using a t-test. The process cost model and ethanol prediction model both consist

of multiple regression models that form the different components of the model. Two corn stover

feedstock types (IA and PA) and different storage conditions and outcomes from Chapter 3, 5, 6

were used in informing relationships used in this model. In developing regression equations, data

from one feedstock type or treatment condition was used and tested against the other feedstock

type or other conditions in model validation. That is, model validation by the predictive

performance method was performed by applying real, observed data not included in developing

the regression equation. Validation was performed on these key components: storage organic

acid (predicted from storage moisture and duration), storage dry matter loss (predicted from

storage moisture and duration) and ethanol yield predictions (predicted from organic acids from

pretreated and corrected using yield factors). Figures 7.6, 7.7 and 7.8 compare predicted and

observed values for acetic and isobutyric acids, dry matter loss and ethanol yields.

Acetic and isobutyric acid regression equations were developed using data from IA stover

(Chapter 3) and validated with data from PA stover as observed values. Considering the

heterogeneous nature of biomass feedstock that results in variability and uncertainty in physical

and biochemical responses, the acetic and isobutyric acid prediction models are reasonable

(Figure 7.6).

Dry matter losses for aerobic conditions were predicted using regression equations developed

from dry matter loss in PA stover and validated with dry matter losses in IA stover. For

anaerobic conditions, the regression equation was developed from losses in IA stover and

validated with losses in PA stover. The respective stover selected for developing the regression

equation was based on which gave a better regression fit. Predictions under aerobic conditions

were quite reasonable (Figure 7.7). Under anaerobic conditions, more variability is seen and

predictions showed good correlation only at 37oC. However the range of dry matter loss values at

23oC were within range that are typical in wet storage systems.

Ethanol yield was predicted using organic acid generated in “as is”, day 220 IA stover at 15

minute pretreatment time. For conditions other than this 220-day “as is” silage the ethanol

prediction model applies yield factors to correct for the “unensiled” effect and pre-processing

effects such as drying and washing. The predictive validity was tested against “as is”, day 220 IA

209

stover at other pretreatment retention times (5 and 10 minutes) (Figure 7.8, top); against day 0

samples of “as is”, with and without yield factors (Figure 7.8, middle); and against dried day 0

feedstock and dried silage (day 220) (Figure 7.8, bottom).

Figure 7.6: Comparison of predicted and observed values of acetic acid (top) and isobutyric acid

concentration after wet storage. See Appendix F for other acid types.

R² = 0.5969

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

2.2

0 0.5 1 1.5 2 2.5

Pre

dic

ted

(A

ceti

c ac

id (

%))

Observed (Acetic acid (%))

Acetic

R² = 0.5113

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

0 0.5 1 1.5 2

Pre

dic

ted

(Is

ob

uty

ric

acid

(%

))

Observed (Isobutyric acid (%))

Isobutyric

210

Figure 7.7: Comparison of predicted and observed values of dry matter loss after aerobic and

wet anaerobic storage. A= aerobic storage; B= anaerobic storage

211

Figure 7.8: Comparison of predicted and observed values of ethanol yield.

Model was developed using “as is” day 220 samples pretreated for 15 minutes. Top: model compared to

“as is” samples pretreated at 5 min. and 10 min. Middle: model compared to unensiled “as is” feedstock

not controlling for pretreatment time. Bottom: model applied to dried silage and dried unensiled samples.

212

Yield factors are ratios of yields from ensiled “as is” stover (Day 220) to yields from other

treatments. Applying these yield factors did not improve the predictions of ethanol for the “as is”

samples. Table F14, Appendix F, shows that the percentage of samples with underestimated

yields when yield factors were applied was as high (33%) as the number of samples with

overestimated yield when these yield factors were not applied. A t-test of the difference between

predicted and observed values of “as is” samples, without applying the yield factor to the

unensiled samples, was not significant when compared to a test mean of 0 (i.e. model with no

prediction error). However, when yield factors where applied using mean values for each

pretreatment time or equations accounting for effect of moisture (see Table F13, Appendix F),

the prediction errors were significant. Based on these results yield factors are not needed for

unensiled “as is” stover, and in general the model gave a good prediction of yields. The mean

test error for “as is” stover is 0.80. Test error refers to prediction error from independent samples

not used in model development. Using the model for dried feedstock gave higher estimates of

ethanol yield than actually observed. The t-test for dried samples was significant both without

yield factors and with yield factors, regardless of whether the yield factor was applied using the

yield factor equations or as means of each pretreatment time. Yield factors are however

necessary because without their applications, predicted yields were higher than observed yields

by a factor of 1.02 to 2.24.

A yield factor regression to predict pre-processing effects is attractive, as this regression

accounts for feedstock moisture variation. However, the randomness of the outcomes based on

moisture also leads to huge potentials for overestimation at high moistures. Using the mean yield

factor as a yield correcting coefficient addresses these huge overestimations. Applying the mean

yield factors to dried samples, therefore, halved the root mean squared error and the mean

absolute error relative to using the yield factor equations (see Appendix F, Table 15). The mean

test error for dried stover is 1.79, about twice that of “as is” stover. However, the root mean

squared error, which is analogous to standard deviation, and the mean absolute error, are slightly

lower for dried stover. The mean predicted yield and RMSE are 55.35 and 7.99 for “as is” stover

and 44.83 and 6.79 for dried stover.

213

7.3.5 Sensitivity analysis

Feedstock delivery cost

Sensitivity analysis was carried out using SensIt®

1.45 (TreePlan Software, San Francisco, CA), a

Microsoft Excel add-in to examine the effect of some input parameters on feedstock delivery

cost and ethanol profitability. Two extreme case scenarios and a base scenario were defined for

each analysis. For both wet and dry storage, the base scenario was defined by 45% moisture and

the extremes by 25% and 65% moisture levels, resulting in a ±20% point variation about the base

case. The analysis was performed for a 500 acre farm with 360-day storage duration. Figure 7.9

shows the impact and ranking of various factors on feedstock delivery cost. The most influential

parameter on delivery cost of wet storage feedstock was moisture content. The impact of

moisture content on feedstock price has the highest relative variance of 49%, varying feedstock

delivery cost by up to 65% from base case delivery cost. The variance is calculated as the square

of the output range divided by the sum of squares of range for all factors used in the sensitivity

analysis. Transportation cost, which was suspected to be a major bottle neck for wet feedstock

has a variance of less than 6%., and varied feedstock delivery cost up to 22% from base case

cost. For the two wet storage system (vertical silo and wrapped silage) analyzed, the three most

influential parameters were consistent in their impact hence ranking. The lowest impact factor is

dry matter loss and storage cost for vertical silo and wrapped silage systems respectively. In

contrast to the sensitivity outcome of wet storage, the most important parameter affecting the

delivery cost of the dry storage systems analyzed (dry enclosed and on-field without cover) was

dry matter loss in storage. This impact is measured by the highest relative variance of 48 and

44% and storage loss impact on feedstock delivery cost showed variation by a factor of up to 5

and 1 compared to the base case for the dry enclosed and on-field without cover respectively.

Ethanol profitability

For ethanol profitability, the same extremes and base case scenarios were applied to an ethanol

production facility with 70 million gallons per year nameplate capacity. Profitability was

calculated by subtracting production cost from revenue generated from selling ethanol at

$2.97/gal ethanol produced. Figure 7.10 shows the impact of various factors on ethanol

214

profitability. For dry storage, only two factors had impact on profits, the amount of feedstock

required to meet the nameplate production capacity and the cost of feedstock, with measured

impact of more than 80% and 13% variance respectively. The sensitivity analysis shows huge

losses incurred at the extreme scenarios of high moisture levels, which greatly impact dry matter

loss and delivery cost (Figure 7.9). Moisture levels above 25% are not practical for effective dry

storage systems and are not recommended. For wet storage systems, all 7 variables had some

impact on ethanol profitability, but the most influential factor is ethanol conversion efficiency,

with a variance of about 58%.

7.3.6 Comparison with other observations and predictions from literature

Table 7.2 compares farm-scale dry matter loss observations from Shinners (2003) and Shinners

(2009) with predictions from the model developed in this study. These comparisons confirm that

the dry matter loss predictions from the model developed in this study were within a reasonable

range. The predictions for on-field storage were generally higher than observations from the

Shinners’ studies. However, the model also predicts confidence intervals, and these measured

on-farm field losses were generally within those confidence errors. Dry matter losses in

uncovered on-field storage are the most variable, as they are subject to various weather

conditions and microbial degradation processes that can affect the degree and rate of loss. As an

example, at a moisture level of 27.5%, Shinners (2004) observed dry matter loss of 17.7% and

36.1% in 2002 and 2003 respectively, and this latter value is higher than 20.6%, the value

predicted by the model in this study. A switchgrass predictive model developed for outdoor on-

field storage (Mooney et al., 2012) estimated dry matter loss to be above 40% at 7 months of

storage and up to 90.8% after 16 months of storage.

Various delivery cost estimates are presented in Table 7.3. Analyses leading to these estimates

are based on several varying factors that can make cost comparisons on the surface problematic.

Some of these factors like cropping systems and practices are not obvious cost determinants,

while others are regionally influenced. However, rough comparisons show that the estimates of

delivery cost from the model in this study is reasonable and similar to estimates from Gallagher

and Baumes (2012), and Perlack and Turhollow (2002) but higher than Tyndall (2007) when

215

nutrient cost is not considered. These model estimates may compare well with other estimates in

Table 7.3, if those other intangible costs and opportunity costs could be factored in.

The ethanol production costs in this model are also reasonable. In 2005, the National Renewable

Energy Lab (NREL) production cost of ethanol was $2.25/gal with an anticipated decrease to

$1.07 by 2012 with improvement in yield from 65 gals/ton to 90 gals/ton (Pacheco, 2006). In

2009, POET cellulosic pilot plant using corn cobs was producing ethanol at $2.35/gal (Green Car

Congress, 2009) and this dropped to $2.20 (Helman, 2013). The cost of production of cellulosic

ethanol in 2012 at the National Renewable Energy Lab (NREL) pilot plant was $2.15 (Pezzullo,

2013). While this value is not as low as forecasted in 2006, the $1.07 is still a target that is

achievable. In this current study, ethanol production cost was estimated at $1.80/gal and

$1.50/gal at stover feedstock moisture of 25% and 20% respectively with corresponding ethanol

yields of 73 gals/ton and 86 gals/ton. This lower cost is partly due to the higher yield per ton and

also the LHW pretreatment method as opposed to the dilute acid pretreatment method used by

the POET and NREL. Dilute acid pretreatment would require acid corrosion resistance leading to

more expensive equipment, increasing the capital cost as well as requiring a neutralization unit.

The acid and neutralization chemicals also increase the operating cost. Several other factors,

some of which were not documented in the referenced studies, could also affect cost. These

could include facility size, plant financing/interest rate, plant operating life used in cost

calculation, feedstock location, type and cost, etc.

216

Table 7.2: Comparing corn stover dry matter loss prediction of model to other studies after 7

months of storage

Storage configuration


Shinners et al. (2003, 2009)

Model prediction from this study

Enclosed 25.9 4.9 4.69

23.9 4.8 4.53

On-field, uncovered (on ground)

22.2 10.7 18.21

24.4 14.3 21.04

32.0 29.1 46.20

On-field, uncovered (on pallets)

22.3 7.0 13.45

23.0 11.4 14.11

27.5 17.7 20.56

Silo bag 41.7 1.4 1.41

42.7 6.0 1.56

55.4 3.8 2.77

Wrapped silage (Baleage)

29.4 1.2 0.50

44.3 2.9 3.43

217

Table 7.3: Delivery cost comparison of estimates from this model and other studies

Estimated delivery cost of

stover ($/ton)* Comment Reference

37.50 – 40.50 TP: 25 miles Gallagher and Baumes (2012)

51.00 – 165.00

Accounts for opportunity cost of land: up to 60%.

Include crop land rent at farm-gate (Wide cost

range due to regional location and different agro-

practices)

Khanna and Huang (2012)

64.85, 79.34†

TP: 50 miles; Moisture: 15%; on-field storage,

elevated from ground and covered with tarp.

Include nutrient replacement cost ($19.07/ton).

Thompson and Tyner (2012)

60.00 – 100.00

Actual market price. Also include nutrient

[replacement] cost?(Cost expected to drop to $45 -

$75ᵟ)

University of Missouri

extension (2012)

87.00

TP: 50 miles; Moisture: 16 -20%; Growers cost: $22;

Harvest: estimated assuming an 8% IRR; Storage:

6% IRR profit

Gonzalez et al. (2011)

31.40

Include nutrient replacement cost. Iowa farmers

willing to supply stover at minimum average cost

of $50.47 = 61% profit

Tyndall (2007)

43.10 – 51.60

Payments to farmer ($10/dry ton compensation for

nutrient removed and profit). Cost differ with

Biorefinery size, which determines transportation

radiusᴽ

Perlack and Turhollow (2002)

30.69 – 32.60 TP: 25 miles; Moisture: 15 - 20%; indoor storage This studyᴼ

28.33 – 30.06TP: 25 miles; Moisture: 15 - 20%; on-field storage,

elevated from ground and covered with tarp. This studyᴼ

29.48 – 35.40TP: 25 miles; Moisture: 15 - 20%; on-field storage,

elevated from ground and uncovered. This studyᴼ

* Most of these studies are theoretical estimations based on various assumptions of amount harvested/collected, losses and

cost. The estimates are mainly break-even cost that includes harvesting, collection, storage and transportation cost. TP =

transportation distance

† 64.85 from a continuous corn rotation and 79.34 from corn-soybean rotation. Transportation include backhaul (return trip);

most transportation estimates are on loaded mile basis as is in the model developed in this study.

ᴽ Minimum facil ity size = 500 dry tons/day with average loaded transport distance of 22 miles but up to 64 mi. The average

distance to the largest facil ity of 4000 drt tons/day is 62 miles but up to 181 mi.

ᴼ Storage duration of 12 months. No nutrient removal or farmer's payment cost. Also assume no moisture increase over

storage period due to rainfall event

ᵟ The $60/ton -$100/ton, which includes farmer's profit, is said to be due to the 2012 drought and hence expected to come

down. At this expected price drop without nutrient, this cost is comparible to that predicted by model

218

Figure 7.9: Sensitivity analysis showing effect of various parameters on minimum feedstock delivery cost of some wet and dry storage

systems. Base case (Vertical line) = 45% moisture

219

Figure 7.10: Sensitivity analysis showing effect of various storage systems on ethanol profitability. Base case (Vertical line) = 45%

moisture

220

7.4 Conclusions

A process cost and ethanol prediction model was developed to evaluate biomass supply chain

logistics that include wet storage systems, and to provide a basis for comparison of different

storage types and configurations. Ten storage configurations are presented in the model as well

as four stacking configuration for bales. Dry matter and yield prediction components were

developed from research presented in Chapters 3, 5 and 6. Equipment cost and dry matter loss

factors for storage systems not experimentally investigated in this study were developed from

literature. The results show that at low moisture levels ≤ 40%, wet storage had a lower delivery

cost than conventional dry storage. Although wet storage at higher moisture may cost more when

compared to a well preserved dry storage system, this cost disadvantage is not extreme and may

disappear with low cost bunker storage systems. However, when moisture levels exceed 48% in

dry storage systems, the cost of ethanol production can be prohibitive due to the very high dry

matter losses. This condition is possible, especially in on-field, uncovered storage configuration.

Although a number of values were assumed in process costing, the model generally gave

reasonable outputs. Sensitivity analysis show that moisture content of wet storage feedstock had

the most influence on feedstock delivery cost. Dry matter loss, on the other hand, was the most

influential parameter under dry storage conditions.

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226

Chapter 8

Conclusions

8.1 Overview

A series of experiments, in four phases, were performed to characterize the impact of wet storage

systems on biofuel production and elucidate how this storage option compares with conversional

dry storage systems. Wet storage simulates ensilage, which is used in the livestock industry and

which has been practiced for centuries. Feedstock preservation – quality and quantity – in wet

storage systems depends on naturally produced organic acids, which reduce the pH to levels that

eradicate most spoilage organisms. The presence of organic acid and the high moisture content

associated with wet storage present major concerns and defines the delay in the readiness to

adopt wet storage, even though it is recognized as an option for the biofuel industry.

The concern posed by wet storage acids is the perception that the microorganisms required to aid

subsequent processes cannot survive due to the inhibitory nature of organic acids. The impact of

organic acids, in particular, was therefore a major focus of this study. The other concern of high

moisture has to with transportation logistics. In this case wet storage is presumed to have higher

transportation cost that could make it economically less competitive than dry feedstock. A model

was developed to access this concern.

Using rewetted dry corn stover, this study explored feedstock quality issues as well as tested

assumptions about the pretreatment capability of organic acids produced during wet storage. The

model developed was used to explore the cost implications of wet storage and effect on ethanol

production, comparing it to dry storage systems. The results show wet storage is a suitable

storage option for ethanol production, with downstream outcomes that are similar for moisture

levels of 35% to 65%. In addition, modeling output showed that at moisture levels ≤ 35%, the

minimum delivery cost of wet storage feedstock was lower than cost of dry storage at 25%

moisture. The delivery cost factors in all costs incurred from harvesting and collection

operations, storage and eventual transportation to the refinery, accounting for feedstock losses at

the various logistic units.

227

8.2 Experimental approach

The present study was intended to address several questions about wet storage systems. These

include (1) what effect storage conditions have on feedstock composition or quality; (2) how

effective is the pretreatment capability of organic acids produced in plain silage; (3) how organic

acids produced during storage interact with subsequent downstream processes like pretreatment

and fermentation; (4) how wet storage system outcomes compare with dry storage systems and

(5) whether wet storage can be economically competitive with dry storage.

The research was carried out in four phases. Phase 1 addresses the first question in Chapter 3.

Phase 2 addresses the second question in Chapter 4. Phase 3 addresses the third question in

Chapters 5 and 6 and Phase 4 addresses the fourth and fifth questions in Chapter 7. The initial

experimental set-up included 519 (aerobic and anaerobic) storage units of 15% to 75% moisture

levels. Two temperatures were investigated (23oC ± 1

oC and 37

oC). Storage durations were 0, 21,

90 and 220 days.

To examine the effect of storage conditions on feedstock composition (Phase 1), a quantitative

saccharification method was used in structural compositional analysis. The difference in

feedstock composition indicated the change that occurred during storage. Other feedstock

changes were examined through dry matter loss, organic acids produced and pH.

In Phase two, the pretreatment capability was examined by thoroughly washing the wet stored

feedstock (day 220, ensiled) and corresponding controls (day 0, unensiled). The goal of washing

was to get rid of sugars and acids that could interfere with the reactivity assay. These feedstocks

were treated with hydrolytic enzymes and incubated at 50oC for 3 days. Glucose yield was used

as a quantitative measure of how reactive the fibers were to the enzymes.

In Phase 3, dried ground silage was tested against “as is” silage (i.e. silage with no post-storage

processing). Half the samples of the dry silage and “as is” silage together with corresponding

controls were washed. Both washed and unwashed samples were pretreated using liquid hot

water method. The pretreatment extracts, which contain the acids and inhibitors generated during

pretreatment, were collected separately. Fermentation was then carried out with and without

pretreatment extracts. Organic acid interaction with the pretreatment process was examined by

228

comparing washed and unwashed samples. The impact of acids and inhibitors on the

fermentation process was examined by comparing samples fermented with extract to those

fermented without extracts. Wet storage effect was examined by comparing wet storage samples

to control samples (day 0). Drying effect was examined by comparing dry silage to “as is” silage.

A model was developed in Phase 4 that draws on the data from aerobic storage in phase one to

inform the behavior of dry storage at moistures greater than 25%. The model uses relationships

derived from Phases 1 -3 and cost data from the literature to determine minimum feedstock

delivery cost, ethanol yield and ethanol production cost. The minimum ethanol selling price was

also determined for each scenario. The model present a simple tool for comparing different

storage conditions as well as storage types/configurations. This model uses and Microsoft Excel

platform and accepts inputs through direct keyboard entering, selection from drop-down menus

and check boxes.

8.3 Key findings

Ensilage was effective at preserving corn stover even at low moisture levels of 25%

Generally, there was little or no moisture change during anaerobic wet storage.

The most influential factor with respect to change in feedstock composition during anaerobic wet

storage is storage duration

Hemicellulose degradation ranged from 6% to 30% during wet storage at moisture levels of 25%

to 75%

Storage at moisture levels of 35% – 65% was similarly consistent in its impact on the biofuel

production process. Hence storing at 35% is just as effective as 65%. Feedstock suppliers can

enjoy the benefits of silage at this low moisture and the benefit of lesser transportation cost.

Although there were some variations in silage outcome due to the different storage moistures,

these variations did not have any significant impact on upstream process (pretreatment and

fermentation) outcomes, if extreme moisture levels (25% and 75%) are avoided.

Of the two extreme moistures (25% and 75%) studied, 75% had consistently poorer outcomes

and should be avoided if possible. Compared to the alternate (dry storage), if drying rates are

slow, wet storage could still be a better option

229

There is evidence that wet storage, without biological and chemical additives, has pretreatment

capability which was attributed to lactic acid dominance. However, for corn stover the extent of

this effect is not adequate to serve as sole pretreatment; post storage pretreatment would

therefore still be necessary.

Effect of organic acids can be observed at two levels (1) at the storage level potentially altering

feedstock structure by degrading hemicellulose and making the feedstock more susceptible to

subsequent pretreatment. This can be observed when organic acid is not interfering with the

pretreatment process as in washed samples; (2) during subsequent pretreatment which

accelerates as well as limits xylan removal (when organic acid is interacting with the

pretreatment process).

Total organic acids produced during wet storage (<1% DM to 9.1%DM) were generally below

levels that constitute any form of ethanol fermentation inhibition. The levels of these acids were

altered in downstream processes leading to fermentation. During liquid hot water pretreatment, it

is possible to generate acids higher than the amounts initially present during storage. Again,

these amounts were usually not at levels to cause concern, except in some 75% moisture samples

where high levels of butyric acid (>3.7%) interacted with high levels acetic acid (>6.0% DM) to

inhibit ethanol production, as evident in lower yields. Even at a projected 30% solid loading,

organic acids were not at levels to present any significant inhibition.

If samples stored at 75% are excluded, organic acid had no inhibitory effect on ethanol

fermentation and in fact enhanced the yield by a mean factor of about 1.11

Pretreatment of ensiled stover could be carried out at lower severity [shorter retention time],

compared to unensiled stover, and still be as effective. However, as pretreatment severity

increases, the benefits derived from ensilage decreases.

Fermentation with pretreatment extract showed organic acids, furfural and HMF generally had

no inhibitory effect on ethanol production. The amount of furfural generated during pretreatment

was about 10x the amount of 5-HMF. On average, furfural was less than 0.5% DM. The

exception is 75% moisture sample, which could generate high acetic and butyric acids.

Drying wet stored feedstock significantly reduced ethanol yield by a factor of 0.6 to 0.8,

probably due to the in-situ storage pretreatment that results in permanent collapse of cell wall

during the drying process.

230

Modeling result indicated that low moisture wet storage feedstocks (≤ 35%) had lower feedstock

delivery cost and better ethanol yield compared to dry storage feedstock at 25% to 35% moisture.

Delivery cost of wet storage at any particular moisture level was always lower than the costs at

the corresponding dry storage supply chain at the same moisture.

A sensitivity analysis of model parameters showed the most influential factor on feedstock

delivery cost was moisture content and storage losses for wet and dry storage respectively.

8.4 Potential applications

The most important outcome of this study is the elucidation of the effects and cost of wet storage

systems at multiple levels from storage to end use. The results presented in this study address

several concerns about wet storage systems relating to their effect on subsequent processes and

the logistics of transporting water. This study shows wet storage can be effective even at low

moisture. In addition, this study will provide feedstock suppliers and feedstock buyers with a

good understanding of wet storage systems that can facilitate its adoption for biofuel production.

This is particularly useful for high humid regions, where drying rates are slow and could result in

extensive field losses and contamination. With a prior knowledge on what to expect from wet

storage systems, the risk associated with adopting this alternate storage strategy is minimized and

suppliers are encouraged to participate.

The study also illustrates a strategy to classify wet storage outcomes that can be used in

developing quality indices for the biofuel market to ensure fair trade between suppliers and

buyers. In the absence of a biofuel specific index or classification system, this study shows that

quality standards in the livestock industry apply well to the biofuel industry. Wet storage for

biofuel production, however, covers a wider range of moisture.

Finally, the model developed in this study provides a simple tool for comparing various storage

configurations and ultimately their impact on biofuel production cost. This can serve as a handy

decision tool. Using a Microsoft Excel platform means it can be accessed freely by many.

8.5 Recommendations or direction for future research

Results from this research suggest that where options exist regarding storage moisture, low

moisture silage (<45%) is recommended over high moisture silage, since downstream processing

231

outcomes are similar but with lower transportation costs. At this moisture level, feedstock

delivery cost is similar to that of well-preserved dry feedstock.

This research has covered many aspects of wet storage but not much on the transportation and

handling aspects. Silage, from bunker, piles or unloaded from a tower silo, will likely be

transported and handled uncovered from storage to biorefinery. There is a need to expand our

knowledge about wet storage transportation, handling and queuing systems in terms of physical

losses and aerobic deterioration. From this perspective, wrapped silage or silage bags might be

recommended for biofuel system where multiple storage or staging area are required. Current

logistic models and demonstration programs should be expanded to address these logistics

concerns.

The model developed in this research can also be improved in many ways: (1) incorporate

aerobic deterioration data when silage is exposed during transportation. (2) include nutrient

removal/cost (3) include sustainability constrains that suggest sustainable harvest/collection

based on requirement to meet nutrient, soil carbon and soil moisture and erosion concerns (4)

incorporate timeliness of operation and effect on losses and cost, especially when custom rates

are charged per hour.

If a uniform feedstock format that includes downstream drying is to be adopted, further research

would be needed in characterizing full impact of drying on silage material, response to varying

drying temperatures, interactions of silage organic acids with drying process, causes of/

transformation of silage acids during drying, degree of pore space collapse and impacts of

decomposition and acids on quantitative saccharification.

Finally, although the model can be used in estimating feedstock quality based on ethanol output,

there will be a need to develop a quality index that provides a common basis for pricing

feedstock. This research has laid the foundation for such an index or indices. A fiber reactivity

index, ethanol yield factor and dry matter loss are some parameters that could be considered. All

of these parameters could be related to organic acids, which could be predicted by silage

moisture.

232

APPENDIX A: General overview of research

(Chapter 1)

Table F1: Null hypotheses tested in this research

(Ho): Feedstock composition after storage = Function (Dry matter loss)

(Ha): Feedstock composition after storage ≠ Function (Dry matter loss) ?

(Ho): μ hydrolytic yield, ensiled feedstock = μ hydrolytic yield, unensiled feedstock

(Ha): μ hydrolytic yield, ensiled feedstock > μ hydrolytic yield, unensiled feedstock

(Ho): μ pretreatment severity, ensiled feedstock = μ pretreatment severity, unensiled feedstock

(Ha): μ pretreatment severity, ensiled feedstock < μ pretreatment severity, unensiled feedstock

(Ho): μ biofuel yield, with silage organic acid = μ biofuel yield, no organic acid (unensiled/washed silage)

(Ha): μ biofuel yield,with silage organic acid ≠ μ biofuel yield, no organic acid (unensiled/washed silage)

(Ho): μ process cost ensiled feedstock = μ process cost unensiled feedstock

(Ha): μ process cost ensiled feedstock < μ process cost unensiled feedstock

(Ho) = Null hypothesis (Ha) = Alternative hypothesis μ = mean value

Ensiled = wet storage feedstock; Unensiled = corresponding day 0 (control samples)

= null hypothesis rejected after research

= null hypothesis rejected after research but alternative not always true

? = Dry matter loss affects feedstock composition but there was no correlation between the

two

233

Figure A1: Schematic overview of research

234

Figure A2: Detailed flow chart of research showing number of samples analyzed at each stage

Bold = measurements taken

Bold red= yet to be taken

Temperature

(oC)

0 3 reps 3 reps 3 reps 3 reps 3 reps 3 reps 3 reps


90 3 reps 3 reps 3 reps 3 reps 3 reps 3 reps 3 reps Washed Unwashed Washed Unwashed

0 3 reps 3 reps 3 reps 3 reps 3 reps 3 reps 3 reps *Moisture content 273 273

21 3 reps 3 reps 3 reps 3 reps 3 reps 3 reps 3 reps * Organic acids 273 1365

90 3 reps 3 reps 3 reps 3 reps 3 reps 3 reps 3 reps * pH 273 273

0 3 reps 3 reps 3 reps 3 reps 3 reps 3 reps 3 reps *DML 273 273

21 3 reps 3 reps 3 reps 3 reps 3 reps 3 reps 3 reps * Composition ?






372

744

Temperature 912

(oC) 1488 Fermentation controls

0 3 reps 3 reps 3 reps 3 reps 3 reps 3 reps 3720 Enzyme-yeast blanks 21

21 3 reps 3 reps 3 reps 3 reps 3 reps 3 reps 456 As received stover 24

90 3 reps 3 reps 3 reps 3 reps 3 reps 3 reps 8 random Avicel 12

0 3 reps 3 reps 3 reps 3 reps 3 reps 3 reps *Moisture content 234 234 204 12 random

21 3 reps 3 reps 3 reps 3 reps 3 reps 3 reps * Organic acids 234 1170 7712 Ethanol 372

90 3 reps 3 reps 3 reps 3 reps 3 reps 3 reps * pH 234 273 429

0 3 reps 3 reps 3 reps 3 reps 3 reps 3 reps *DML 234 273

21 3 reps 3 reps 3 reps 3 reps 3 reps 3 reps *Composition ? Total

220 3 reps 3 reps 3 reps 3 reps 3 reps 3 reps 24

0 3 reps 3 reps 3 reps 3 reps 3 reps 3 reps Moisture content (after washing, 2 per moisture) 12

21 3 reps 3 reps 3 reps 3 reps 3 reps 3 reps Controls Batch 1 Batch 2

90 2 reps 2 reps 2 reps 2 reps 2 reps 2 reps 2FPU 5FPU 15FPU 2FPU 5FPU 15FPU Enzyme blanks 9 9 18

220 3 reps 3 reps 3 reps 3 reps 3 reps 3 reps Substrate blanks 36 36 72

Avicel 3 3 6

216

216

18 18 18 18 18 18

18 18 18 18 18 18

72 72 72

36 36 36 108 108 108 996

Xylan removal

Glucan removal

Glucose yields Total mass b4 rnx/Crude density of reagents

Dry fiber after hydrolysis

Moisture

NO

22

Fiber reactivity test Residual sugars/organic acid

37

Washed

Non-pretreated Pretreated

65 75Glucan removal

HMF & Furfural

YES

22

Organic acids

Volume of prt extract

Solids removal

37

Aeration

Ensilage

time

(days)

25 35 45 55

Ethanol yield

Experimental plan for storage of PA corn stover. PA stover was obtained from PA dairy. Additional sample information: was left standing

in the field all winter, up till mid-April, before being harvested and baled. Initial mositure content was ~30.6% at time of collection.

Moisture was adjusted to the various moisture indicted in plan by drying and/or rewetting

pH

Independent variables Moisture content (% wet basis) Xylan removal

NO

22

Fiber reactivity test

37

Washed

Non-pretreated

15 FPU

Glucose yields

45 55 65 75

YES

22

37

WET STORAGE INVESTIGATION FOR BIOFUEL PRODUCTION

Experimental plan for storage of IAcorn stover. IA corn stover was obtained from Idaho National Lab although originally from [grown and

air-dried in] Iowa. It was stored in dry bales indoors for 6 months before size reduction. Additional sample information: Initial mositure

content was ~7.5% at time of arrival in PA. Moisture was adjusted to the various moisture indicted in plan by rewetting. DRY GOUND

Independent variables Moisture content (% wet basis)

Aeration Ensilage

time (days) 15 25 35

Test in triplicates (Batch 3)

These samples are 12 because assume day zero samples of 22C samples should be

applicable to these samplesDRY GROUND

Storage replicates combined




Individually sampled

Individually sampled

151051510515105 15

DRY STOVER AS IS

Pretreatment time (mins):

15105

15105

Unwashed

Ferment with extracts

Ferment without

extracts

Ferment without

extracts




@190o C

Organic acidsHMF and furfural

HMF and

Sugars washed outResidual sugars

Residual sugars



Moisture content Sugars washed outOrganic acids out

pH extractResidual sugarsResidual acids

Tota

l n

o.

of

sam

ple

s

Dat

a p

oin

ts

Tota

l n

o.

of

sam

ple

s

(3 for each enzyme loading)

Total no. of samples = 6




Total no. of samples, Day 0 :

Total no. of samples, Day 220:

Total data points:

Xylan and glucan data points (measured in duplicates):

Total no. of samples = 28 (in duplicates)Total data points = 140 (acids)

56 (HMF and furfural)



Total no. of samples = 14 Total data points = 28 (in duplicates)

= 14 (residual)

Samples = 14Data points 28

Samples = 14Data points = 14

242424242424121212 12 121212 121212

242424 242424

Total no. of samples:

Data points:

Test in triplicates (Batch 1:no pretreat; Batch 2: pretreat)

Test in triplicates (Batch 4)

(3 for each moisture levelfor both day 0 and day 220)


Total no. of samples = 18(Did not include Day 90 anarobic

samples)

Total data pts = 78= 258 (Dry)

Dat

a p

oin

ts

Washed

Same types of contols as in Batch 1& 2. in addtition, Batch 4 has As received

stover ( dry ground) as part of control

42

42

42

42

18

18

Total data pts (moisture)= 12

AS RECEIVED AS IS (not ground)

Batch 3 Batch 2Batch 1 Batch 4

Batch 5

15105 15105

Chapter 3 Chapter 4

Chapter 5 & 6

235

APPENDIX B: Corn stover - relating storage conditions to outcomes

(Chapter 3)

Figure B1: Process Chart and experimental plan for studying effect of storage conditions on

storage outcome

Bef

ore

sto

rageAdjust moisture: 15-75%

Storage temperature: 23OC , 37O C

Aerobic/anaerobic

Independent variables Dependent variables of interest

Aft

er s

tora

ge

pH

Moisture content

Feedstock composition

Dry matter loss

Organic acid profile

Storage duration: 0, 21, 90, 220 days

Dry

stover

at 5

5oC

Wet chemistry

Grind with Wiley mills to 2 mmW

eigh s

tover

Extr

acti

on

of

wat

er

solu

ble

s

Wet Storage

ofstover

Independent variables Moisture content (% wet basis)

Aeration

Temperature

(oC)

Ensilage

time

(days)

15 25 35 45 55 65 75

IA PA IA PA IA PA IA PA IA PA IA PA IA PA

YES

22

0 3 reps * 3 reps 3 reps 3 reps 3 reps 3 reps 3 reps 3 reps 3 reps 3 reps 3 reps 3 reps 3 reps



37




NO

22




37



90 3 reps * 3 reps * 3 reps * 3 reps * 3 reps * 3 reps * 3 reps *


236

Figure B2: Storage samples showing aerobic filter lids, anaerobic indicator, data logger and

molding

Figure B3: Some physical changes in aerobic samples during storage

237

Figure B4: Some precautions taken to minimize errors in dry matter determination

(Samples placed in dry desiccator so samples can cool down without moisture absorption (right)

and scale covered during weighing to prevent unstable/fluctuating reading due to air movement

(left))

238

Table B1: Moisture content and dry matter losses in Fall Harvested, IA, stover

Storage

Temperature

(oC)

Storage

duration

(days)

Target

Moisture

content (%)

Actual Moisture

content before

storage (%)

Moisture

content after

storage (%)

Change in Moisture

(percentage points

difference)

Percentage

change in

moisture

Dry matter loss

(% dry basis)

22 21 15 14.91 ± 0.55 14.67 ± 1.05 0.23 ± 0.80 1.58 ± 5.34 -0.23 ± 1.07

22 21 25 25.13 ± 0.43 24.29 ± 0.22 0.84 ± 0.53 3.33 ± 2.03 -1.16 ± 0.77

22 21 35 33.47 ± 1.02 33.76 ± 0.29 -0.29 ± 0.81 -0.91 ± 2.49 0.57 ± 1.25

22 21 45 43.33 ± 0.23 43.83 ± 0.25 -0.50 ± 0.48 -1.16 ± 1.12 1.17 ± 0.92

22 21 55 54.89 ± 1.16 54.83 ± 0.42 0.06 ± 0.75 0.10 ± 1.38 0.23 ± 1.80

22 21 65 64.44 ± 0.76 65.48 ± 0.70 -1.05 ± 0.47 -1.63 ± 0.75 3.18 ± 1.27

22 21 75 74.87 ± 0.10 74.7 ± 0.37 0.18 ± 0.43 0.23 ± 0.57 -0.34 ± 1.88

22 220 15 14.91 ± 0.55 15.7 ± 0.17 -0.79 ± 0.71 -5.44 ± 5.01 1.53 ± 0.93

22 220 25 25.13 ± 0.43 25.09 ± 0.17 0.04 ± 0.51 0.12 ± 2.02 0.81 ± 0.89

22 220 35 33.47 ± 1.02 34.09 ± 0.66 -0.62 ± 1.29 -1.92 ± 3.96 1.54 ± 1.99

22 220 45 43.33 ± 0.23 45.22 ± 0.29 -1.89 ± 0.31 -4.37 ± 0.74 4.12 ± 0.58

22 220 55 54.89 ± 1.16 55.78 ± 0.22 -0.88 ± 1.35 -1.64 ± 2.52 2.48 ± 2.88

22 220 65 64.69 ± 0.57 65.54 ± 0.29 -0.85 ± 0.77 -1.32 ± 1.21 2.88 ± 2.10

22 220 75 74.93 ± 0.70 75.04 ± 0.35 -0.11 ± 0.82 -0.16 ± 1.10 1.04 ± 3.26

37 21 15 15.61 ± 0.30 14.39 ± 0.31 1.22 ± 0.33 7.80 ± 2.05 -1.6 ± 0.33

37 21 25 25.05 ± 0.78 23.81 ± 0.31 1.24 ± 1.07 4.88 ± 4.10 -1.67 ± 1.45

37 21 35 34.21 ± 0.69 33.36 ± 0.58 0.86 ± 1.07 2.47 ± 3.11 -1.22 ± 1.62

37 21 45 43.12 ± 0.41 44.38 ± 0.41 -1.26 ± 0.76 -2.94 ± 1.78 2.43 ± 1.27

37 21 55 53.29 ± 1.32 53.49 ± 0.59 -0.19 ± 1.66 -0.41 ± 3.13 0.55 ± 3.54

37 21 65 64.32 ± 0.64 65.56 ± 0.32 -1.25 ± 0.94 -1.94 ± 1.48 3.67 ± 2.59

37 21 75 75.34 ± 0.73 76.35 ± 0.63 -1.01 ± 0.41 -1.34 ± 0.55 4.32 ± 1.32

37 90 15 15.30 ± 0.08 13.56 ± 0.41 1.75 ± 0.40 11.41 ± 2.61 -0.02 ± 1.38

37 90 25 24.05 ± 0.24 22.65 ± 0.30 1.40 ± 0.49 5.79 ± 1.96 -1.30 ± 0.76

37 90 35 32.63 ± 0.18 31.68 ± 0.32 0.96 ± 0.49 2.92 ± 1.49 -0.92 ± 0.58

37 90 45 42.70 ± 0.10 44.73 ± 0.44 -2.02 ± 0.53 -4.73 ± 1.24 4.02 ± 0.96

37 90 55 55.51 ± 0.03 54.51 ± 0.05 1.00 ± 0.04 1.79 ± 0.06 -1.50 ± 0.38

37 90 65 64.62 ± 0.07 64.49 ± 0.64 0.14 ± 0.58 0.21 ± 0.90 0.53 ± 1.56

37 90 75 74.81 ± 0.15 75.58 ± 0.39 -0.77 ± 0.25 -1.03 ± 0.33 4.52 ± 0.17

37 220 15 15.61 ± 0.29 14.44 ± 0.39 1.18 ± 0.19 7.55 ± 1.25 -1.47 ± 0.36

37 220 25 25.05 ± 0.78 24.64 ± 0.14 0.40 ± 0.68 1.56 ± 2.66 0.06 ± 0.87

37 220 35 34.21 ± 0.69 33.23 ± 0.51 0.98 ± 0.92 2.83 ± 2.68 -0.67 ± 1.44

37 220 45 43.12 ± 0.41 44.65 ± 0.30 -1.53 ± 0.18 -3.54 ± 0.45 3.49 ± 0.36

37 220 55 53.29 ± 1.32 55.00 ± 0.92 -1.71 ± 1.66 -3.25 ± 3.14 4.23 ± 3.48

37 220 65 64.69 ± 0.64 65.55 ± 0.77 -0.86 ± 0.73 -1.34 ± 1.12 2.85 ± 2.11

37 220 75 75.70 ± 0.64 76.36 ± 0.58 -0.65 ± 0.28 -0.86 ± 0.37 4.08 ± 1.10

Anaerobic storage

239

Table B1 cont:

Note: For moisture data, positive values imply decrease in moisture content with reference to before storage values,

while negative values imply an increase. Moisture before storage were obtained from same batch used for storage

but not the exact sample stored hence the difference in before and after measurements may not be real but a result of

heterogeneous nature of feedstock. Differences could also be due to error [using different weighing scales, several

people taking measurement, relative humidity of weighing environment potentially resulting in hygroscopic

moisture absorption]

Positive value for dry matter loss indicates a reduction in dry matter while a negative value shows dry matter

accumulation. Dry matter accumulation could be a result of inaccuracy in moisture determination

Storage

Temperature

(oC)

Storage

duration

(days)

Target

Moisture

content (%)

Actual Moisture

content before

storage (%)

Moisture

content after

storage (%)

Change in Moisture

(percentage points

difference)

Percentage

change in

moisture

Dry matter loss

(% dry basis)

22 21 15 14.83 ± 0.24 9.47 ± 0.21 5.36 ± 0.43 36.11 ± 2.32 0.37 ± 0.50

22 21 25 24.03 ± 0.65 15.32 ± 0.41 8.71 ± 0.81 36.2 ± 2.54 2.99 ± 0.50

22 21 35 33.34 ± 0.19 27.72 ± 0.57 5.63 ± 0.66 16.87 ± 1.93 10.61 ± 1.16

22 21 45 42.68 ± 1.04 42.58 ± 0.34 0.10 ± 1.07 0.20 ± 2.51 22.2 ± 2.04

22 21 55 55.16 ± 0.89 54.07 ± 0.17 1.09 ± 1.02 1.95 ± 1.83 20.1 ± 1.85

22 21 65 65.64 ± 0.44 65.63 ± 0.28 0.01 ± 0.51 0.01 ± 0.78 19.01 ± 1.02

22 21 75 75.68 ± 1.64 74.22 ± 0.56 1.46 ± 1.22 1.91 ± 1.57 13.17 ± 4.72

22 90 15 14.83 ± 0.24 6.83 ± 0.16 8.00 ± 0.29 53.91 ± 1.28 0.20 ± 0.57

22 90 25 24.03 ± 0.65 7.19 ± 0.41 16.84 ± 0.84 70.05 ± 2.01 3.98 ± 1.06

22 90 35 33.34 ± 0.19 9.91 ± 0.60 23.44 ± 0.67 70.29 ± 1.86 17.03 ± 0.62

22 90 45 42.68 ± 1.04 22.89 ± 1.50 19.79 ± 0.58 46.4 ± 2.28 30.44 ± 0.32

22 90 55 55.16 ± 0.89 41.41 ± 2.99 13.74 ± 3.84 24.85 ± 6.60 32.43 ± 3.94

22 90 65 65.64 ± 0.44 59.27 ± 1.24 6.37 ± 1.05 9.70 ± 1.62 35.27 ± 1.84

22 90 75 75.68 ± 1.64 74.22 ± 1.23 1.45 ± 1.22 1.91 ± 1.57 40.73 ± 2.60

37 21 15 14.76 ± 0.36 6.03 ± 0.84 8.74 ± 0.52 59.23 ± 4.72 0.96 ± 0.76

37 21 25 24.95 ± 0.76 7.34 ± 0.46 17.61 ± 0.36 70.58 ± 1.05 4.60 ± 0.63

37 21 35 33.49 ± 0.49 18.45 ± 0.28 15.04 ± 0.77 44.9 ± 1.64 13.15 ± 1.37

37 21 45 44.76 ± 0.57 35.86 ± 0.77 8.90 ± 0.21 19.9 ± 0.70 21.93 ± 0.36

37 21 55 54.36 ± 0.38 48.46 ± 0.73 5.90 ± 1.09 10.85 ± 1.94 23.77 ± 0.62

37 21 65 65.04 ± 1.07 63.00 ± 1.53 2.04 ± 1.79 3.13 ± 2.75 27.47 ± 3.74

37 21 75 75.19 ± 0.77 74.48 ± 0.99 0.71 ± 0.26 0.95 ± 0.35 28.36 ± 0.67

37 90 15 14.76 ± 0.36 3.15 ± 0.15 11.61 ± 0.48 78.63 ± 1.42 0.84 ± 0.14

37 90 25 24.95 ± 0.76 3.30 ± 0.33 21.65 ± 0.70 86.78 ± 1.22 5.37 ± 1.70

37 90 35 33.49 ± 0.49 3.55 ± 0.16 29.94 ± 0.40 89.39 ± 0.37 16.17 ± 0.76

37 90 45 44.76 ± 0.57 3.54 ± 0.21 41.23 ± 0.69 92.09 ± 0.53 23.60 ± 1.28

37 90 55 54.36 ± 0.38 2.83 ± 0.23 51.53 ± 0.16 94.79 ± 0.38 29.83 ± 1.47

37 90 65 65.04 ± 1.07 8.12 ± 1.98 56.93 ± 3.05 87.48 ± 3.25 35.58 ± 1.03

37 90 75 75.19 ± 0.77 38.59 ± 7.93 36.60 ± 7.18 48.75 ± 10.09 39.41 ± 1.24

Aerobic storage

240

Table B2: Moisture content and dry matter losses in Spring Harvested, PA, stover

Notes for Table B1 applies

Storage

Tempera

ture (oC)

Storage

duration

(days)

Target

Moisture

content

(%)

Actual Moisture

content before

storage (%)

Moisture

content after

storage (%)

Change in

Moisture

(percentage

points

difference)

Percentage

change in

moisture

Dry matter loss (%

dry basis)

22 21 25 23.89 ± -0.65 24.54 ± 0.55 -0.65 ± 1.42 -2.76 ± 4.08 0.98 ± 0.64

22 21 35 33.57 ± -0.35 33.92 ± 1.98 -0.35 ± 0.58 -1.09 ± 1.26 0.84 ± 1.64

22 21 45 44.49 ± -1.33 45.81 ± 2.37 -1.33 ± 4.08 -3.18 ± 5.73 2.63 ± 0.29

22 21 55 58.14 ± 0.57 57.57 ± 0.27 0.57 ± 0.83 0.98 ± 0.65 -1.23 ± 0.34

22 21 65 66.06 ± -0.02 66.08 ± 0.37 -0.02 ± 1.4 -0.03 ± 0.77 0.25 ± 0.21

22 21 75 77.02 ± 0.67 76.35 ± 0.18 0.67 ± 1.51 0.87 ± 0.68 4.40 ± 0.63

22 220 25 23.89 ± -2.63 26.53 ± 0.55 -2.63 ± 1.07 -11.03 ± 1.16 4.52 ± 0.74

22 220 35 33.57 ± -1.41 34.98 ± 1.98 -1.41 ± 2.36 -4.39 ± 5.26 2.67 ± 0.74

22 220 45 44.49 ± -1.91 46.4 ± 2.37 -1.91 ± 3.11 -4.46 ± 4.71 5.56 ± 0.67

22 220 55 58.14 ± -0.73 58.88 ± 0.27 -0.73 ± 1.67 -1.27 ± 1.15 2.49 ± 0.52

22 220 65 66.06 ± -0.76 66.82 ± 0.37 -0.76 ± 1.22 -1.15 ± 0.65 2.95 ± 0.27

22 220 75 77.02 ± -0.55 77.57 ± 0.18 -0.55 ± 2.37 -0.72 ± 0.77 3.37 ± 0.41

37 21 25 25.03 ± -0.22 25.25 ± 1.03 -0.22 ± 2.08 -1.05 ± 5.89 0.68 ± 0.47

37 21 35 33.25 ± -0.16 33.42 ± 1.52 -0.16 ± 2.68 -0.65 ± 5.16 0.52 ± 0.40

37 21 45 44.74 ± -0.37 45.11 ± 0.17 -0.37 ± 0.22 -0.84 ± 0.29 1.26 ± 0.30

37 21 55 57.65 ± 0.25 57.4 ± 0.26 0.25 ± 0.32 0.44 ± 0.26 -0.19 ± 0.36

37 21 65 66.26 ± 0.16 66.11 ± 0.28 0.16 ± 1.41 0.24 ± 0.71 -0.30 ± 0.29

37 21 75 76.49 ± -0.23 76.72 ± 0.19 -0.23 ± 1.34 -0.3 ± 0.44 1.60 ± 0.15

37 220 25 25.03 ± -0.33 25.36 ± 1.03 -0.33 ± 1.07 -1.35 ± 2.18 1.56 ± 0.62

37 220 35 33.25 ± -1.20 34.45 ± 1.52 -1.20 ± 2.63 -3.78 ± 6.02 2.63 ± 0.38

37 220 45 44.74 ± -1.61 46.35 ± 0.17 -1.61 ± 0.44 -3.63 ± 7.68 1.77 ± 3.24

37 220 55 57.65 ± -1.89 59.54 ± 0.26 -1.89 ± 2.15 -3.28 ± 1.65 5.56 ± 0.73

37 220 65 66.26 ± -0.91 67.17 ± 0.28 -0.91 ± 1.57 -1.37 ± 0.98 4.46 ± 0.42

37 220 75 76.49 ± -1.08 77.57 ± 0.19 -1.08 ± 0.92 -1.41 ± 0.28 5.92 ± 0.08

22 21 25 24.09 ± 7.09 17.00 ± 0.40 7.09 ± 0.68 29.42 ± 1.43 1.19 ± 0.26

22 21 35 34.21 ± 5.67 28.54 ± 1.65 5.67 ± 1.43 16.43 ± 4.50 8.77 ± 0.26

22 21 45 47.35 ± 2.57 44.78 ± 0.44 2.57 ± 2.74 5.43 ± 2.36 14.1 ± 1.28

22 21 55 57.28 ± 2.50 54.79 ± 0.46 2.5 ± 4.7 4.34 ± 4.28 15.31 ± 2.06

22 21 65 66.15 ± 1.41 64.74 ± 0.51 1.41 ± 1.01 2.14 ± 0.61 15.9 ± 0.90

22 21 75 76.37 ± 0.37 76.00 ± 0.10 0.37 ± 0.63 0.49 ± 0.39 16.66 ± 0.27

22 90 25 24.09 ± 16.23 7.86 ± 0.40 16.23 ± 1.82 67.38 ± 0.91 1.87 ± 0.13

22 90 35 34.21 ± 23.61 10.6 ± 1.65 23.61 ± 2.15 68.95 ± 2.30 12.75 ± 0.54

22 90 45 47.35 ± 22.55 24.79 ± 0.44 22.55 ± 1.77 47.64 ± 2.40 19.78 ± 1.17

22 90 55 57.28 ± 20.42 36.87 ± 0.46 20.42 ± 1.88 35.63 ± 1.66 29.62 ± 0.66

22 90 65 66.15 ± 8.62 57.53 ± 0.51 8.62 ± 1.89 13.02 ± 1.49 34.10 ± 0.55

22 90 75 76.37 ± 2.48 73.89 ± 0.10 2.48 ± 0.35 3.25 ± 1.22 39.17 ± 0.84

37 21 25 24.52 ± 20.02 4.50 ± 0.23 20.02 ± 0.23 81.67 ± 1.01 2.16 ± 0.25

37 21 35 33.19 ± 21.11 12.09 ± 2.06 21.11 ± 0.92 63.73 ± 3.73 12.11 ± 1.92

37 21 45 45.37 ± 16.74 28.63 ± 0.76 16.74 ± 1.09 36.91 ± 1.52 14.98 ± 1.08

37 21 55 57.67 ± 14.04 43.64 ± 0.05 14.04 ± 2.24 24.34 ± 4.25 15.39 ± 2.45

37 21 65 66.67 ± 8.76 57.90 ± 0.99 8.76 ± 0.66 13.14 ± 1.27 16.66 ± 0.59

37 21 75 76.44 ± 5.40 71.03 ± 0.38 5.40 ± 1.96 7.07 ± 0.68 16.87 ± 0.30

37 90 25 24.52 ± 23.53 0.99 ± 0.23 23.53 ± 1.13 95.97 ± 5.57 1.50 ± 1.37

37 90 35 33.19 ± 30.81 2.38 ± 2.06 30.81 ± 3.00 92.85 ± 0.62 11.86 ± 0.32

37 90 45 45.37 ± 43.42 1.95 ± 0.76 43.42 ± 1.01 95.71 ± 0.31 16.75 ± 0.14

37 90 55 57.67 ± 55.25 2.42 ± 0.05 55.25 ± 2.42 95.80 ± 0.71 20.72 ± 0.41

37 90 65 66.67 ± 62.73 3.94 ± 0.99 62.73 ± 1.16 94.08 ± 1.13 22.08 ± 0.74

37 90 75 76.44 ± 41.67 34.77 ± 0.38 41.67 ± 4.03 54.51 ± 4.53 25.79 ± 3.51

Anaerobic storage

Aerobic storage

241

Table B3: pH values of moisture adjusted corn stover before and after storage

Day 0 Day 21 Day 90 Day 21 Day 90 Day 220

15 6.69 ± 0.08 6.81 ± 0.22 6.77 ± 0.02 6.18 ± 0.55 * 6.53 ± 0.21

25 6.69 ± 0.02 8.58 ± 0.03 8.42 ± 0.02 6.12 ± 0.12 * 4.95 ± 0.05

35 6.69 ± 0.11 8.42 ± 0.19 8.38 ± 0.02 4.67 ± 0.03 * 4.41 ± 0.03

45 6.65 ± 0.02 8.37 ± 0.07 8.11 ± 0.10 4.30 ± 0.02 * 4.18 ± 0.02

55 6.71 ± 0.03 8.41 ± 0.27 8.46 ± 0.12 4.10 ± 0.05 * 4.05 ± 0.03

65 6.54 ± 0.08 8.49 ± 0.11 8.83 ± 0.15 4.16 ± 0.02 * 4.23 ± 0.03

75 6.73 ± 0.05 8.39 ± 0.08 9.09 ± 0.03 4.32 ± 0.09 * 4.24 ± 0.01

15 * * * * * *

25 6.55 ± 0.07 7.23 ± 0.04 6.89 ± 0.05 4.96 ± 0.18 * 4.97 ± 0.91

35 6.46 ± 0.03 7.28 ± 0.08 7.08 ± 0.05 3.94 ± 0.05 * 4.07 ± 0.01

45 6.14 ± 0.04 7.23 ± 0.47 7.40 ± 0.19 3.77 ± 0.06 * 4.16 ± 0.11

55 5.73 ± 0.09 7.71 ± 0.34 7.41 ± 0.17 3.73 ± 0.03 * 4.00 ± 0.09

65 5.90 ± 0.10 7.89 ± 0.26 7.80 ± 0.07 3.71 ± 0.02 * 4.09 ± 0.04

75 5.69 ± 0.07 8.15 ± 0.08 7.87 ± 0.06 3.92 ± 0.12 * 4.58 ± 0.02

15 6.71 ± 0.10 6.91 ± 0.19 6.68 ± 0.03 6.56 ± 0.03 7.70 ± 0.89 6.26 ± 0.66

25 6.71 ± 0.03 8.29 ± 0.27 7.95 ± 0.04 5.92 ± 0.12 5.48 ± 0.03 4.90 ± 0.05

35 6.69 ± 0.02 8.29 ± 0.18 7.97 ± 0.01 4.73 ± 0.03 4.61 ± 0.07 4.46 ± 0.03

45 6.69 ± 0.06 8.40 ± 0.18 7.35 ± 0.09 4.33 ± 0.01 4.34 ± 0.01 4.24 ± 0.02

55 6.66 ± 0.01 8.66 ± 0.14 7.45 ± 0.26 4.12 ± 0.01 4.19 ± 0.03 4.11 ± 0.02

65 6.56 ± 0.10 8.75 ± 0.10 7.79 ± 0.08 4.17 ± 0.01 4.57 ± 0.46 4.08 ± 0.01

75 6.76 ± 0.07 8.77 ± 0.23 7.99 ± 0.07 4.25 ± 0.02 4.86 ± 0.08 4.75 ± 0.03

15 * * * * * *

25 6.50 ± 0.06 7.04 ± 0.05 6.84 ± 0.08 4.26 ± 0.04 * 4.40 ± 0.12

35 6.49 ± 0.07 7.07 ± 0.06 6.85 ± 0.06 4.01 ± 0.04 * 4.10 ± 0.09

45 6.19 ± 0.12 7.27 ± 0.19 6.95 ± 0.10 3.87 ± 0.02 * 4.16 ± 0.14

55 5.69 ± 0.05 7.07 ± 0.07 6.86 ± 0.05 4.08 ± 0.04 * 4.39 ± 0.24

65 5.81 ± 0.05 7.49 ± 0.05 7.10 ± 0.03 3.92 ± 0.03 * 4.50 ± 0.24

75 5.79 ± 0.13 7.86 ± 0.05 7.27 ± 0.06 4.53 ± 0.08 * 4.59 ± 0.01

23oC

37oC

IA

PA

IA

PA

pHStorage

Temperature

Stover

type

Storage

moisture

(%)Aerobic Storage Anaerobic Storage

242

Box 1: More about dry matter determination from organic acids (Stoichio-gasometric method)

Dry matter loss was estimated based on the organic acid content of silage, assuming that these acids

are a measureable and perhaps significant part of that dry matter loss. The goal was to assess a

mechanistic explanation for the wide variability within replicates as determined through gravimetric

methods, which included many suspicious results indicating dry matter accumulation rather than loss.

Dry matter losses under anaerobic conditions are usually termed fermentation losses and are a result of

microbial action. Fermentation of water soluble sugars to organic acids is necessary to reduce the pH

to the levels required for proper preservation of the larger bulk of the feedstock, which generally

requires pH below 5.0. These losses are therefore in part unavoidable. The organic acids produced

could potentially degrade structural bonds leading to release of more soluble sugars from the

hemicellulose in the cell wall, and these sugars could be degraded if the anaerobic condition is

compromised. If the pH is not sufficiently low, there can also be losses from secondary fermentations

such as butyric acid with higher associated losses of CO2. Either of these circumstances can increase

dry matter loss (DML) above what would be anticipated in an idealized ensiled storage system.

Different microbial communities have been identified in silage and the extent of DML would depend

on the community that dominates. In Table B4 are stoichiometries of some silage fermentation

reactions. From these reactions, it can be observed that acetic acid is usually a by-product or co-

product of other fermentation reactions. Most acetic acid formed in silage are formed by heterolactic

Lactobacillus sp so accounting for CO2 from acetic acid production will be double accounting for the

same dry matter loss. Depending on the substrate concentration (glucose) and species of

microorganism, different product outcomes are expected as shown in Table B4. The highlighted

equations were used in the calculation of DML. These choices of equations were in part based on

experimental observations in which there was a slight increase in moisture content of most samples

after storage, the presence of isobutyric acid and the absence of succinic acid. In addition these

equations give higher DML, assuming a bias to higher dry matter losses is more acceptable. In butyric

acid fermentation, when lactic is absent, the assumption is sugars are first converted to lactic before

butyric resulting in an additional mole of CO2. Carbon dioxide and hydrogen are lost as gases.

Although water may increase the wet weight, it is lost during drying. Contributions of hydrogen and

water to DML are usually very little.

243

Lactic vs Moisture: Best fit Regression R2 Acetic vs Moisture: Best fit Regression R2

Lactic220 = -0.0024 x Moisture2 + 0.2486 x Moisture- 3.5731 0.91 Acetic220 = 0.0326 x Moisture- 0.5382 0.88

Lactic21 = -0.0008 x Moisture2 + 0.104 x Moisture- 1.5137 0.90 Acetic21 = 0.0117 x Moisture - 0.1242 0.83

Lactic0 = -4E-05 x Moisture2 + 0.0015 x Moisture+ 0.1115 0.59 Acetic0 = 0.0038 0.00

Lactic220 = -0.0027 x Moisture2 + 0.2441 x Moisture - 3.3406 0.95 Acetic220 = 0.046 x Moisture - 1.0642 0.80

Lactic21 = -0.0016 x Moisture2 + 0.1664 x Moisture- 2.1227 0.79 Acetic21 = 0.0175 x Moisture - 0.2027 0.74

Lactic0 = -0.0002 x Moisture2 + 0.0252 x Moisture - 0.4466 0.61 Acetic0 = -0.0002 x Moisture2 + 0.0193 x Moisture - 0.3917 0.92

PA

IA

Figure B5: Relating Lactic acid and acetic acid to moisture content

244

Table B4: Stoichiometry of some silage fermentation reactionsₒ

Reactions Reference Main Acid Mols of CO2 per mol acid

g CO2 per gram acid

Acid production by lactic acid bacteria (LAB)

Lactobacillus sp

Lactic 0 0

Glucose → Lactic McGechan, 1990

Lactic 1 0.4885

Glucose→ Lactic + Ethanol McGechan, 1990

Glucose→ Lactic + Acetic

Lactic 1 0.4885

Glucose → Lactic + Acetic + Butyric

Lactic & Isobutyric

3 & 2 1.4654 & 0.9988

Glucose → Lactic + Acetic + Isobutyric Suzzi et al., 1990*

Lactic 1 0.4885

Fructose or glucose → Lactic + Acetic + Mannitol McGechan, 1990

Lactic 0 0

[Pentose] → Lactic + Acetic McGechan, 1990

Acetic 2 1.4654

Lactic Acetic + 1,2 Propandiol + Ethanol Oude Elferink et al., 2001

Lactic Acetic + 1,2 Propandiol Oude Elferink et al., 2001

Streptococcus sp

Glucose → Lactic + Acetic + Formic + Ethanol Moat et al., 2003*

Acid production by Clostridium sp

Butyric 2 0.9988

Glucose → Butyric Hungate, 1966

245

Butyric 8/3 1.3317

Glucose → Butyric + Acetic Moat et al., 2003*

Butyric 2 0.9988

Glucose → Butyric + Lactic + Acetic

Butyric 2 0.9988

Lactic → Butyric McGechan, 1990

Acid production by Propionibacterium sp

Propionic 1 0.5940

Glucose → Propionic

Propionic 0 0

Glucose → Propionic Hungate, 1966

Propionic 3 1.1879 Glucose → Propionic

Propionic 1 0.5940

Glucose → Propionic + Acetic

Propionic 1/2 0.2970

Glucose → Propionic + Acetic Moat et al., 2003

Propionic 1 0.5940

Glucose → Propionic + Acetic + Succinic Papoutsakis and Meyer, 1985*

Propionic 1/2 0.2970

Lactic → Propionic + Acetic Todar, 2008*

Acetic Acid production

Acetic 1 0.7327

Glucose → Acetic Hungate, 1966

Glucose → Acetic + Ethanol (E. coli)

Yeast fermentation

ₒ There are many other reactions not listed here especially the metabolism of protein/amino acids

* These references gave main products. So there were additions of hydrogen, carbon-dioxide or water to balance equation

Reactions that are not referenced were deduced from combination of ideas from literature cited.

246

References for stoichiometric reactions

Hungate, R. E. 1966. The rumen and its microbes. New York, Academic Press.

Moat, A. G., J. W. Foster and M. P. Spector. 2003. Microbial physiology. 4th edition. New York:

Wiley-Liss Inc. (Chapter 11: Fermentation Pathways, 412-433)

McGechan M.B. 1990. A review of losses arising during conservation of grass forage: part 2.

Storage losses. Journal of Agricultural Engineering Research 45: 1 - 30.

Oude Elferink S. J. W. H., J. Krooneman, J. C. Gottschal, S. F. Spoelstra, F. Faber, and F.

Driehuis. 2001. Anaerobic conversion of lactic acid to acetic acid and 1,2-propanediol

by Lactobacillus buchneri. Applied Environmental Microbiology 67:125-132

Papoutsakis E.T. and C.L. Meyer. 1985. Fermentation equations for propionic- acid bacteria and

for production of assorted oxychemicals from various sugars. Biotechnology and

Bioengineering 27: 67-80

Suzzi G., L. Grazia and G. Ferri. 1990. Studies on isobutyric acid-producing bacteria in silage.

Letters in Applied Microbiology 10: 69-72

Todar, K. 2008. Diversity of metabolism in procaryotes. Available at

http://textbookofbacteriology.net/metabolism_3.html Accessed 09 May 2013.

http://textbookofbacteriology.net/metabolism_3.html%20Accessed%2009%20May%202013

247

APPENDIX C: Corn stover reactivity to cellulolytic enzymes

(Chapter 4)

This appendix contains supplementary materials to Chapter 4. These include micrographic

images from other studies, process charts for the fiber reactivity study, some pictures relating to

the hydrolytic process, experimental data on organic acids, glucose yields from both pretreatment

and hydrolysis, graphs of organic acid relationship with moisture and statistical results.

Figure C1: Microscopic images comparing unensiled and ensiled corn stover (Adapted from

Oleskowicz-Popiel et al. 2010).The microbial additive used was Biomax Si (CHR Hansen,

Denmark) containing lactic acid.

248

Figure C2: TEM (Transmission Electron Microscopy) micrographs comparing unensiled, ensiled

and dilute acid pretreated stover (Adapted from Donohoe et al. 2009).

249


organic acid. Each cellulase enzyme loading has a corresponding β-glucosidase loading as

described under methodology.

250


organic acid. Cellulase enzyme loading has a corresponding β-glucosidase loading as described

under methodology.

251

Figure C5: Sample of “as is” corn stover after washing (A) for fiber reaction compared to

unwashed “as is” stover (B)

Figure C6: Washed corn stover in centrifuge tubes for hydrolysis at 15% solid loading in a

Barnstead Max Q5000 SHKE 5000-7 floor shaker/ incubator (Barnstead International, Dubuque,

IA).

A B

252

Figure C7: Relating hemicellulose degradation during storage and hydrolytic glucose yield

* Percentage theoretical from washed IA stover without pretreatment. Data label shows storage

moisture or adjusted moisture for day 0 samples in red.

0

5

10

15

20

25

30

-5 0 5 10 15 20

Hyd

rlo

ytic

glu

cose

yie

ld *

% Hemicellulose degradation during storage

Glucose(Day 220) day 0

IA-45

IA-25

IA-55

IA-35

IA-65

IA-75

IA-55

IA-35

IA-45

IA-75

IA-65

IA-25

253

Table C1: Xylan removal in pretreated PA stover assuming no xylan degradation during storage

and 5% xylan degradation during storage

Storage

moisture Storage

% Xylan removed

(with 0%

degradation

during storage)

% Xylan removed

(with 5%

degradation

during storage)

25 Ensiled 46.7 50.0

35 Ensiled 52.3 56.0

45 Ensiled 54.1 57.5

55 Ensiled 53.7 57.5

65 Ensiled 52.0 55.5

75 Ensiled 50.3 53.5

25 Unensiled 51.3 51.0



55 Unensiled n.m n.m



0.023* 0.135p-value (ensile vs unensiled):

* Significant difference in ensiled and unensiled samples was found for

samples at 35% and 55% moisture levels. See Figure 2 of main text.

254

Table C2: Glucose yield of non-pretreated corn stover after fiber reactivity test

IA

0 2 5 15 15

Wet storage (Day 220) 25 0 ± 0 6.88 ± 1.59 7.8 ± 3.01 6.9 ± 1.64 27.53 ± 1.03

Wet storage (Day 220) 35 0 ± 0 13.62 ± 1.75 17.05 ± 0.54 23.25 ± 0.81 24.7 ± 0.73

Wet storage (Day 220) 45 0 ± 0 9.31 ± 1.17 14.15 ± 0.89 16.07 ± 0.53 25.56 ± 2.23

Wet storage (Day 220) 55 0 ± 0 7.39 ± 0.84 10.66 ± 1.34 17.38 ± 4.34 20.98 ± 2.19

Wet storage (Day 220) 65 0 ± 0 9.15 ± 2.66 14.27 ± 3.18 18.4 ± 0.88 23.88 ± 0.13

Wet storage (Day 220) 75 0 ± 0 6.65 ± 0.92 8.76 ± 1.79 11.22 ± 2.71 18.99 ± 0.64

Control (Day 0) 25 0 ± 0 0.67 ± 0.52 2.81 ± 2.53 7.38 ± 1.62 18.09 ± 5.4

Control (Day 0) 35 0 ± 0 6.51 ± 2.08 12.31 ± 2.3 13.88 ± 4.05 11.76 ± 1.5

Control (Day 0) 45 0 ± 0 8.59 ± 0.3 11.28 ± 2.68 13.62 ± 4.22 12.6 ± 5.93

Control (Day 0) 55 0 ± 0 7.66 ± 0.9 9.9 ± 2.62 8.31 ± 1.48 11.16 ± 4.48

Control (Day 0) 65 0 ± 0 9.05 ± 1.69 11.74 ± 2.59 10.93 ± 3.02 17.72 ± 6.49

Control (Day 0) 75 0 ± 0 9.07 ± 1.21 9.58 ± 1.48 12.22 ± 1.04 14.48 ± 1.23

Stover type PA

StorageMoisture

(%)

Cellulase enzyme loading (FPU/ g glucan)

255

Table C3: Glucose and total organic acid - amount, ranking and cluster group*

* Cluster analysis for glucose was performed using yields from two cellulose enzyme loading rates (0 and

15 FPU/g glucan) with corresponding β-glucosidase.

Stover

type

Storage

Duration

Moisture

content

(%)

Glucose (%

theoretical)

Total acids (%

dry matter)

Glucose

yield

ranking

Total

organic

acids,

ranking

Glucose,

cluster group

Total acid,

cluster

group

IA 220 75 18.99 ± 0.63 6.59 ± 2.36 18 24 Moderate High

PA 220 55 17.38 ± 4.33 5.82 ± 0.72 14 22 Moderate High

PA 220 65 18.4 ± 0.87 5.31 ± 1.28 17 21 Moderate High

PA 220 75 11.22 ± 2.71 6.05 ± 1.03 6 23 Low High

IA 0 25 18.08 ± 5.4 0 ± 0 16 3.5 Moderate Low

IA 0 35 11.77 ± 1.5 0 ± 0 7 3.5 Low Low

IA 0 45 12.6 ± 5.93 0.05 ± 0.09 9 7 Low Low

IA 0 55 11.16 ± 4.48 0 ± 0 5 3.5 Low Low

IA 0 65 17.73 ± 6.49 0 ± 0 15 3.5 Moderate Low

IA 0 75 14.48 ± 1.23 0 ± 0 12 3.5 Low Low

PA 0 25 7.38 ± 1.62 0 ± 0 2 3.5 Low Low

PA 0 35 13.88 ± 4.05 0.32 ± 0.12 11 10 Low Low

PA 0 45 13.62 ± 4.21 0.3 ± 0.18 10 9 Low Low

PA 0 55 8.31 ± 1.48 0.38 ± 0.08 3 11 Low Low

PA 0 65 10.93 ± 3.02 0.5 ± 0.3 4 13 Low Low

PA 0 75 12.21 ± 1.04 0.3 ± 0.3 8 8 Low Low

IA 220 25 27.53 ± 1.03 0.44 ± 0.76 24 12 High Low

PA 220 25 6.9 ± 1.64 1.36 ± 0.37 1 14 Low Low

PA 220 45 16.07 ± 0.53 1.58 ± 1.37 13 15 Moderate Low

IA 220 35 24.7 ± 0.73 2.77 ± 0.15 22 17 High Moderate

IA 220 45 25.56 ± 2.23 3.92 ± 0.24 23 18 High Moderate

IA 220 55 20.98 ± 2.19 4.43 ± 0.78 19 20 High Moderate

IA 220 65 23.88 ± 0.13 4.43 ± 2.16 21 19 High Moderate

PA 220 35 23.25 ± 0.81 2.63 ± 0.26 20 16 High Moderate

256

Table C4: pH data of storage (Day 220) and control (day 0) samples*

IA stover PA stover

Moisture

(%) Day 220 Day 0 Day 220 Day 0

25 4.92 6.67 4.31 6.49

25 4.93 6.73 4.36 6.56

25 4.84 6.73 4.53 6.45

35 4.48 6.69 4.17 6.47

35 4.43 6.67 4.12 6.57

35 4.48 6.70 4.00 6.44

45 4.25 6.64 4.26 6.30

45 4.25 6.75 7.04 6.22

45 4.21 6.67 4.06 6.06

55 4.12 6.66 4.67 5.73

55 4.11 6.67 4.23 5.64

55 4.09 6.66 4.28 5.71

65 4.08 6.57 4.22 5.80

65 4.08 6.65 4.64 5.76

65 4.07 6.45 4.64 5.86

75 4.74 6.83 4.59 5.65

75 4.78 6.69 4.58 5.91

75 4.73 6.77 4.60 5.81

Each sample has three replicates hence the three values for each moisture level. All three

replicates were mixed together for the fiber reactivity test. pH of 45% moisture, PA stover, is

highlighted to show the odd pH that raises the mean pH to 5.12.

257

Table C5: Cluster grouping based on organic acid content of individual IA and PA storage samples

Moisture

content (%) Storage Stover type

Moisture

content (%)

Lactic (%

DM)

Ac (%

DM)etic (%

DM)

Propionic

(% DM)

Isobutyric

(% DM)

Butyric (%

DM)

Total aids

(% DM)

Cluster

group Classification

75 Ensiled IA 75 0.00 2.72 0.00 1.13 2.59 6.44 0 High

75 Ensiled IA 75 0.00 3.48 0.00 2.41 3.14 9.03 0 High

75 Ensiled IA 75 0.00 2.03 0.00 0.30 1.98 4.31 0 High

75 Ensiled PA 75 0.00 2.40 0.28 1.81 0.99 5.48 0 High

75 Ensiled PA 75 0.00 3.41 0.42 1.83 1.58 7.24 0 High

75 Ensiled PA 75 0.00 2.38 0.31 1.79 0.96 5.44 0 High

65 Ensiled PA 65 0.64 4.17 0.46 0.00 0.00 5.27 0 High

65 Ensiled PA 65 0.00 1.88 0.33 2.38 2.01 6.60 0 High

65 Ensiled PA 65 0.00 1.49 0.28 1.25 1.03 4.05 0 High

55 Ensiled PA 55 1.41 1.90 0.00 1.69 0.00 5.00 0 High

55 Ensiled PA 55 1.42 2.54 0.00 2.37 0.00 6.33 0 High

55 Ensiled PA 55 1.25 2.17 0.00 2.41 0.29 6.12 0 High

35 Ensiled IA 35 2.07 0.64 0.00 0.21 0.00 2.92 1 Moderate












45 Ensiled PA 45 1.95 0.20 0.00 0.18 0.00 2.33 1 Moderate





258

Table C5 cont.: Cluster grouping based on organic acid content of individual IA and PA storage samples

Moisture

content (%) Storage Stover type

Moisture

content (%)

Lactic (%

DM)

Ac (%

DM)etic (%

DM)

Propionic

(% DM)

Isobutyric

(% DM)

Butyric (%

DM)

Total aids

(% DM)

Cluster


25 Unensiled IA 25 0.00 0.00 0.00 0.00 0.00 0.00 2 Low

25 Unensiled IA 25 0.00 0.00 0.00 0.00 0.00 0.00 2 Low

25 Unensiled IA 25 0.00 0.00 0.00 0.00 0.00 0.00 2 Low

35 Unensiled IA 35 0.00 0.00 0.00 0.00 0.00 0.00 2 Low

35 Unensiled IA 35 0.00 0.00 0.00 0.00 0.00 0.00 2 Low

35 Unensiled IA 35 0.00 0.00 0.00 0.00 0.00 0.00 2 Low

45 Unensiled IA 45 0.00 0.00 0.00 0.00 0.00 0.00 2 Low

45 Unensiled IA 45 0.00 0.00 0.00 0.00 0.00 0.00 2 Low

45 Unensiled IA 45 0.00 0.16 0.00 0.00 0.00 0.16 2 Low

55 Unensiled IA 55 0.00 0.00 0.00 0.00 0.00 0.00 2 Low

55 Unensiled IA 55 0.00 0.00 0.00 0.00 0.00 0.00 2 Low

55 Unensiled IA 55 0.00 0.00 0.00 0.00 0.00 0.00 2 Low

65 Unensiled IA 65 0.00 0.00 0.00 0.00 0.00 0.00 2 Low

65 Unensiled IA 65 0.00 0.00 0.00 0.00 0.00 0.00 2 Low

65 Unensiled IA 65 0.00 0.00 0.00 0.00 0.00 0.00 2 Low

75 Unensiled IA 75 0.00 0.00 0.00 0.00 0.00 0.00 2 Low

75 Unensiled IA 75 0.00 0.00 0.00 0.00 0.00 0.00 2 Low

75 Unensiled IA 75 0.00 0.00 0.00 0.00 0.00 0.00 2 Low

25 Ensiled IA 25 0.00 0.00 0.00 0.00 0.00 0.00 2 Low

25 Ensiled IA 25 0.00 0.00 0.00 0.00 0.00 0.00 2 Low

25 Ensiled IA 25 0.79 0.41 0.00 0.12 0.00 1.32 2 Low

75 Unensiled PA 75 0.00 0.00 0.00 0.00 0.00 0.00 2 Low

75 Unensiled PA 75 0.25 0.34 0.00 0.00 0.00 0.59 2 Low

75 Unensiled PA 75 0.30 0.00 0.00 0.00 0.00 0.30 2 Low

65 Unensiled PA 65 0.26 0.25 0.00 0.00 0.00 0.51 2 Low

65 Unensiled PA 65 0.00 0.19 0.00 0.00 0.00 0.19 2 Low

65 Unensiled PA 65 0.51 0.28 0.00 0.00 0.00 0.79 2 Low

55 Unensiled PA 55 0.13 0.16 0.00 0.00 0.00 0.29 2 Low

55 Unensiled PA 55 0.17 0.26 0.00 0.00 0.00 0.43 2 Low

55 Unensiled PA 55 0.19 0.24 0.00 0.00 0.00 0.43 2 Low

45 Unensiled PA 45 0.13 0.17 0.00 0.00 0.00 0.30 2 Low

45 Unensiled PA 45 0.13 0.00 0.00 0.00 0.00 0.13 2 Low

45 Unensiled PA 45 0.38 0.10 0.00 0.00 0.00 0.48 2 Low

35 Unensiled PA 35 0.08 0.11 0.00 0.00 0.00 0.19 2 Low

35 Unensiled PA 35 0.31 0.12 0.00 0.00 0.00 0.43 2 Low

35 Unensiled PA 35 0.33 0.00 0.00 0.00 0.00 0.33 2 Low

25 Unensiled PA 25 0.00 0.00 0.00 0.00 0.00 0.00 2 Low

25 Unensiled PA 25 0.00 0.00 0.00 0.00 0.00 0.00 2 Low

25 Unensiled PA 25 0.00 0.00 0.00 0.00 0.00 0.00 2 Low

45 Ensiled PA 45 0.00 0.00 0.00 0.00 0.00 0.00 2 Low

25 Ensiled PA 25 1.16 0.42 0.00 0.15 0.00 1.73 2 Low

25 Ensiled PA 25 0.72 0.20 0.00 0.08 0.00 1.00 2 Low

25 Ensiled PA 25 1.08 0.12 0.00 0.15 0.00 1.35 2 Low

259

Table C6: Cluster grouping based on glucose yield of individual IA and PA samples hydrolyzed

at cellulase enzyme loadings of 0 and 15 FPU /g glucan. Glucose yield is reported as a percent of

theoretical yield based on the glucan content of the biomass.

Moisture

content (%) Replicate Storage Stover type

Glucose

yield @ 0

FPU

Glucose

yield @15

FPU

Cluster


25 I Ensiled IA 0 28.65 1 High

25 II Ensiled IA 0 27.33 1 High

25 III Ensiled IA 0 26.61 1 High












25 II Unensiled IA 0 23.18 1 High

65 I Unensiled IA 0 23.98 1 High

35 I Ensiled PA 0 23.58 1 High

35 II Ensiled PA 0 22.33 1 High

35 III Ensiled PA 0 23.85 1 High

55 II Ensiled IA 0 18.56 2 Moderate

75 I Ensiled IA 0 18.61 2 Moderate

75 II Ensiled IA 0 19.72 2 Moderate

75 III Ensiled IA 0 18.63 2 Moderate

25 I Unensiled IA 0 18.65 2 Moderate

45 II Unensiled IA 0 18.72 2 Moderate

55 II Unensiled IA 0 15.53 2 Moderate

65 III Unensiled IA 0 18.17 2 Moderate

75 I Unensiled IA 0 15.74 2 Moderate

45 I Ensiled PA 0 16.24 2 Moderate

45 II Ensiled PA 0 16.49 2 Moderate

45 III Ensiled PA 0 15.48 2 Moderate





65 III Ensiled PA 0 19.16 2 Moderate

35 I Unensiled PA 0 18.53 2 Moderate

45 I Unensiled PA 0 15.55 2 Moderate

45 II Unensiled PA 0 16.52 2 Moderate

260

Table C6 cont.

Moisture

content (%) Replicate Storage Stover type

Glucose

yield @ 0

FPU

Glucose

yield @15

FPU

Cluster


25 III Unensiled IA 0 12.43 0 low

35 I Unensiled IA 0 10.09 0 low

35 II Unensiled IA 0 12.23 0 low









25 I Ensiled PA 0 7.68 0 low

25 II Ensiled PA 0 5.02 0 low

25 III Ensiled PA 0 8.01 0 low


75 I Ensiled PA 0 14.29 0 low

75 II Ensiled PA 0 10.24 0 low


25 I Unensiled PA 0 5.87 0 low

25 II Unensiled PA 0 9.09 0 low

25 III Unensiled PA 0 7.18 0 low













261

Table C7: Another variant of cluster grouping based on percentage glucose yield of individual IA

and PA samples (treated as though yields from IA and PA stover are properties of feedstock at

particular moisture and storage duration).

Moisture

content

(%) Storage Replicate 0 FPU

Glucose

yield, IA

stover

(15FPU)

Glucose

yield, PA

stover

(15FPU)

Cluster


35 Ensiled I 0 25.32 23.58 1 High

35 Ensiled II 0 23.90 22.33 1 High

35 Ensiled III 0 24.88 23.85 1 High

45 Ensiled I 0 23.48 16.24 1 High

45 Ensiled II 0 27.91 16.49 1 High


55 Ensiled I 0 22.84 19.80 1 High

65 Ensiled I 0 24.03 18.59 1 High

65 Ensiled II 0 23.82 17.44 1 High


25 Ensiled I 0 28.65 7.68 2 Moderate

25 Ensiled II 0 27.33 5.02 2 Moderate

25 Ensiled III 0 26.61 8.01 2 Moderate


75 Ensiled II 0 19.72 10.24 2 Moderate


25 Unensiled I 0 18.65 5.87 2 Moderate

25 Unensiled II 0 23.18 9.09 2 Moderate

55 Unensiled II 0 15.53 7.19 2 Moderate

65 Unensiled I 0 23.98 9.11 2 Moderate

55 Ensiled II 0 18.56 19.96 0 Low

75 Ensiled I 0 18.61 14.29 0 Low

25 Unensiled III 0 12.43 7.18 0 Low

35 Unensiled I 0 10.09 18.53 0 Low

35 Unensiled II 0 12.23 11.96 0 Low


45 Unensiled I 0 12.22 15.55 0 Low



55 Unensiled I 0 11.37 7.75 0 Low




75 Unensiled I 0 15.74 11.44 0 Low



262

Table C8: Cluster grouping based on mean glucose yield of PA samples hydrolyzed at cellulase enzyme loadings of 0, 2, 5 and 15

FPU/g glucan. Glucose yield is reported as a percent of theoretical yield based on the glucan content of the biomass.

Moisture

content

(%) Storage

Glucose

yield @

0 FPU

Glucose

yield @

2 FPU

Glucose

yield @

5 FPU

Glucose

yield @

15 FPU

Cluster


35 Ensiled 0 13.62 17.05 23.25 1 High

45 Ensiled 0 9.31 14.15 16.07 1 High

55 Ensiled 0 7.39 10.66 17.38 1 High

65 Ensiled 0 9.15 14.27 18.40 1 High

25 Ensiled 0 6.88 7.80 6.90 3 Moderate

75 Ensiled 0 6.65 8.76 11.22 3 Moderate

35 Unensiled 0 6.51 12.31 13.88 3 Moderate





25 Unensiled 0 0.67 4.23 7.38 2 Low

263

Figure C8: Ward cluster (using squared Euclidean distance) grouping of PA using mean values of glucose yields at 2, 5, 15 FPU/g

glucan

264

Figure C9: Relationship between organic acids grouping and fiber reactivity grouping of PA corn

stover without pretreatment and grouped using glucose yield from four different enzyme

loadings

265

Figure C10: Relating glucose from fiber reactivity grouping of PA corn stover without

pretreatment with lactic acid. Data for lactic acid is a wider set that includes data of corn stover

feedstock not used in the fiber reactivity test

R² = 0.61

R² = 0.95

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

5 15 25 35 45 55 65 75

Lact

ic A

cid

(% D

ry M

atte

r)

Moisture Content (%)

day 0 day 220

High Glucose Average yield at 15 FPU = 19%

Moderate Glucose Average yield at 15 FPU = 11%

Low Glucose Average yield at 15 FPU = 7 %

Ensiled, 25%

Ensiled, 35%

Ensiled, 45%

Ensiled, 55%

Ensiled, 65%

Unesiled, 65%

Ensiled, 75%

Unensiled,75%Unensiled, 55%

Unensiled, 45%Unensiled, 35%

Unensiled, 25%

Lactic (day 220) = -0.0027 x Moisture2 + 0.2441 x Moisture - 3.3406; R² = 0.9504

Lactic (day 0) = -0.0002 x Moisture2 + 0.0212 x Moisture- 0.3711; R² = 0.6021

266

Figure C11: Relationship between organic acids grouping and fiber reactivity grouping of PA

and IA corn stover without pretreatment and grouped using actual values of glucose yields (from

15 FPU/g enzyme loadings) and actual amount of organic acids as percentage of dry matter.

Filled circles = Day 220; Opened, unlabeled circles =Day 0. Data labels for Day 0 not included

because congested.

0

5

10

15

20

25

30

-2 0 2 4 6 8 10

Glu

cose

Yie

ld (

% T

he

ore

tica

l)

Total Organic Acid (% DM)

IA-25

IA-55

IA-65

IA-45 IA-35

PA-35

PA-75

IA-75

PA-55

PA-65

PA-25

PA-45

267

Table C9: Properties of organic acids identified in ensiled corn stover

Compound Molecular Formula

Boiling point °C

pKa Molar mass g/mol

Density g/cm3

Lactic acid (2-Hydroxypropionic acid; 2-hydroxypropanoic acid; 1-hydroxyethane 1-carboxylic acid)

C3H6O3 122oC @12 mmHg

3.85 90.08 1.21

Acetic acid (Ethanoic acid) C2H4O2 118.1oC 4.76 60.05 1.05

Butyric acid (Butanoic acid) C4H8O2 163.5 °C 4.82 88.11 0.96

Isobutyric (2-Methylpropionic acid; Valerianic acid)

C4H8O2 155 °C 4.84 88.11 0.97

Propionic acid (propanoic acid; ethane carboxylic acid)

C3H6O2 141 °C 4.88 74.08 0.99

268

Figure C12: Verifying YSI reliability of quantification (a) Before fiber reactivity sample

measurements (b) Measurements within and after fiber reactivity sample measurements. For the

verification process, known concentrations of glucose were prepared and analyzed. Actual fiber

reactivity samples were diluted so that concentrations were within YSI calibration range, less

than 2.5 g/L.

y = 1.0297x R² = 0.9999

0.0

0.5

1.0

1.5

2.0

2.5

0.0 0.5 1.0 1.5 2.0 2.5

Act

ual

glu

cose

co

nce

ntr

atio

n (

g/L)

YSI quantification (g/L)

y = 1.0059x R² = 0.9989

0.0

0.5

1.0

1.5

2.0

2.5

0.0 0.5 1.0 1.5 2.0 2.5

Act

ual

glu

cose

co

nce

ntr

atio

n (

g/L)

YSI quantification (g/L)

b

a

269

Figure C13: Variation in total organic acids and relationship with storage moisture. Total acid

data reflect lumped IA and PA acids. Regression line is fitted to means of total acid at the

different moisture levels.


To

ta

l a

cid

s (

% d

ry

ba

sis

)

756555453525

9

8

7

6

5

4

3

2

1

0

Total acids = - 1.384 + 0.1030 x 'Storage moisture'

S 0.621401

R-Sq 92.3%

R-Sq (adj) 90.4%

270

STATISTICAL ANALYSIS (A)

ANOVA results showing differences in organic acids (IA and PA combined) across

moisture

One-way ANOVA: Lactic acid versus Moisture Source DF SS MS F P

Moisture1 5 22.67 4.53 4.51 0.003

Error 30 30.16 1.01

Total 35 52.83

S = 1.003 R-Sq = 42.91% R-Sq(adj) = 33.40%

Individual 95% CIs For Mean Based on

Pooled StDev

Level N Mean StDev --------+---------+---------+---------+-

25 6 0.625 0.512 (-------*--------)

35 6 1.812 0.261 (-------*-------)

45 6 2.055 1.093 (--------*-------)

55 6 2.157 0.910 (--------*-------)

65 6 1.623 1.918 (-------*--------)

75 6 0.000 0.000 (-------*-------)

--------+---------+---------+---------+-

0.0 1.0 2.0 3.0

Pooled StDev = 1.003

Tukey 95% Simultaneous Confidence Intervals

All Pairwise Comparisons among Levels of Moisture1

Individual confidence level = 99.51%

Moisture1 = 25 subtracted from:

Moisture1 Lower Center Upper +---------+---------+---------+---------

35 -0.573 1.187 2.947 (--------*--------)

45 -0.330 1.430 3.190 (--------*--------)

55 -0.228 1.532 3.292 (--------*-------)

65 -0.762 0.998 2.758 (--------*--------)

75 -2.385 -0.625 1.135 (--------*--------)

+---------+---------+---------+---------

-4.0 -2.0 0.0 2.0



45 -1.517 0.243 2.003 (--------*--------)

55 -1.415 0.345 2.105 (--------*--------)

65 -1.948 -0.188 1.572 (--------*--------)

75 -3.572 -1.812 -0.052 (--------*--------)

+---------+---------+---------+---------

271

-4.0 -2.0 0.0 2.0



55 -1.658 0.102 1.862 (--------*-------)

65 -2.192 -0.432 1.328 (--------*--------)

75 -3.815 -2.055 -0.295 (--------*--------)

+---------+---------+---------+---------

-4.0 -2.0 0.0 2.0



65 -2.293 -0.533 1.227 (-------*--------)

75 -3.917 -2.157 -0.397 (--------*--------)

+---------+---------+---------+---------

-4.0 -2.0 0.0 2.0



75 -3.383 -1.623 0.137 (--------*--------)

+---------+---------+---------+---------

-4.0 -2.0 0.0 2.0

One-way ANOVA: Acetic acid versus Moisture Source DF SS MS F P

Moisture1 5 29.145 5.829 15.64 0.000

Error 30 11.177 0.373

Total 35 40.322

S = 0.6104 R-Sq = 72.28% R-Sq(adj) = 67.66%


Pooled StDev

Level N Mean StDev ---+---------+---------+---------+------

25 6 0.1917 0.1889 (----*----)

35 6 0.6967 0.1076 (----*----)

45 6 0.4417 0.3189 (----*-----)

55 6 1.6733 0.6268 (----*----)

65 6 1.8750 1.1595 (----*----)

75 6 2.7367 0.5910 (----*----)

---+---------+---------+---------+------

0.0 1.0 2.0 3.0






272

Moisture1 Lower Center Upper -------+---------+---------+---------+--

35 -0.5665 0.5050 1.5765 (-----*----)

45 -0.8215 0.2500 1.3215 (----*-----)

55 0.4101 1.4817 2.5532 (----*-----)

65 0.6118 1.6833 2.7549 (----*-----)

75 1.4735 2.5450 3.6165 (-----*----)

-------+---------+---------+---------+--

-2.0 0.0 2.0 4.0



45 -1.3265 -0.2550 0.8165 (-----*----)

55 -0.0949 0.9767 2.0482 (----*----)

65 0.1068 1.1783 2.2499 (----*----)

75 0.9685 2.0400 3.1115 (----*-----)

-------+---------+---------+---------+--

-2.0 0.0 2.0 4.0



55 0.1601 1.2317 2.3032 (----*-----)

65 0.3618 1.4333 2.5049 (----*-----)

75 1.2235 2.2950 3.3665 (----*-----)

-------+---------+---------+---------+--

-2.0 0.0 2.0 4.0



65 -0.8699 0.2017 1.2732 (----*----)

75 -0.0082 1.0633 2.1349 (----*-----)

-------+---------+---------+---------+--

-2.0 0.0 2.0 4.0



75 -0.2099 0.8617 1.9332 (----*-----)

-------+---------+---------+---------+--

-2.0 0.0 2.0 4.0

One-way ANOVA: Propionic acid versus Moisture Source DF SS MS F P

Moisture1 5 0.2407 0.0481 3.71 0.010

Error 30 0.3890 0.0130

Total 35 0.6296

S = 0.1139 R-Sq = 38.22% R-Sq(adj) = 27.93%


Pooled StDev

Level N Mean StDev ---------+---------+---------+---------+

273

25 6 0.0000 0.0000 (--------*--------)

35 6 0.0000 0.0000 (--------*--------)

45 6 0.0000 0.0000 (--------*--------)

55 6 0.0000 0.0000 (--------*--------)

65 6 0.1783 0.2040 (---------*--------)

75 6 0.1683 0.1902 (---------*--------)

---------+---------+---------+---------+

0.00 0.10 0.20 0.30






Moisture1 Lower Center Upper ---------+---------+---------+---------+

35 -0.1999 0.0000 0.1999 (---------*---------)

45 -0.1999 0.0000 0.1999 (---------*---------)

55 -0.1999 0.0000 0.1999 (---------*---------)

65 -0.0216 0.1783 0.3782 (---------*---------)

75 -0.0316 0.1683 0.3682 (---------*---------)

---------+---------+---------+---------+

-0.20 0.00 0.20 0.40



45 -0.1999 0.0000 0.1999 (---------*---------)

55 -0.1999 0.0000 0.1999 (---------*---------)

65 -0.0216 0.1783 0.3782 (---------*---------)

75 -0.0316 0.1683 0.3682 (---------*---------)

---------+---------+---------+---------+

-0.20 0.00 0.20 0.40



55 -0.1999 0.0000 0.1999 (---------*---------)

65 -0.0216 0.1783 0.3782 (---------*---------)

75 -0.0316 0.1683 0.3682 (---------*---------)

---------+---------+---------+---------+

-0.20 0.00 0.20 0.40



65 -0.0216 0.1783 0.3782 (---------*---------)

75 -0.0316 0.1683 0.3682 (---------*---------)

---------+---------+---------+---------+

-0.20 0.00 0.20 0.40


274


75 -0.2099 -0.0100 0.1899 (---------*--------)

---------+---------+---------+---------+

-0.20 0.00 0.20 0.40

One-way ANOVA: Isobutyric acid versus Moisture Source DF SS MS F P

Moisture1 5 11.413 2.283 5.35 0.001

Error 30 12.804 0.427

Total 35 24.217

S = 0.6533 R-Sq = 47.13% R-Sq(adj) = 38.32%


Pooled StDev

Level N Mean StDev -------+---------+---------+---------+--

25 6 0.0833 0.0695 (-------*-------)

35 6 0.1917 0.0248 (-------*-------)

45 6 0.1883 0.1055 (-------*------)

55 6 1.2467 1.0385 (-------*-------)

65 6 0.6833 0.9640 (-------*-------)

75 6 1.5450 0.7324 (-------*-------)

-------+---------+---------+---------+--

0.00 0.70 1.40 2.10







35 -1.0385 0.1083 1.2552 (-------*------)

45 -1.0418 0.1050 1.2518 (-------*------)

55 0.0165 1.1633 2.3102 (-------*------)

65 -0.5468 0.6000 1.7468 (-------*-------)

75 0.3148 1.4617 2.6085 (-------*------)

-------+---------+---------+---------+--

-1.5 0.0 1.5 3.0



45 -1.1502 -0.0033 1.1435 (-------*-------)

55 -0.0918 1.0550 2.2018 (-------*-------)

65 -0.6552 0.4917 1.6385 (------*-------)

75 0.2065 1.3533 2.5002 (-------*-------)

-------+---------+---------+---------+--

-1.5 0.0 1.5 3.0


275


55 -0.0885 1.0583 2.2052 (-------*-------)

65 -0.6518 0.4950 1.6418 (------*-------)

75 0.2098 1.3567 2.5035 (-------*-------)

-------+---------+---------+---------+--

-1.5 0.0 1.5 3.0



65 -1.7102 -0.5633 0.5835 (------*-------)

75 -0.8485 0.2983 1.4452 (-------*-------)

-------+---------+---------+---------+--

-1.5 0.0 1.5 3.0



75 -0.2852 0.8617 2.0085 (-------*------)

-------+---------+---------+---------+--

-1.5 0.0 1.5 3.0

One-way ANOVA: Butyric acid versus Moisture Source DF SS MS F P

Moisture1 5 16.714 3.343 13.44 0.000

Error 30 7.461 0.249

Total 35 24.174

S = 0.4987 R-Sq = 69.14% R-Sq(adj) = 63.99%


Pooled StDev

Level N Mean StDev ------+---------+---------+---------+---

25 6 0.0000 0.0000 (-----*-----)

35 6 0.0000 0.0000 (-----*-----)

45 6 0.0000 0.0000 (-----*-----)

55 6 0.0483 0.1184 (-----*-----)

65 6 0.5067 0.8439 (-----*-----)

75 6 1.8733 0.8752 (-----*-----)

------+---------+---------+---------+---

0.00 0.70 1.40 2.10






Moisture1 Lower Center Upper --------+---------+---------+---------+-

35 -0.8754 0.0000 0.8754 (-----*-----)

45 -0.8754 0.0000 0.8754 (-----*-----)

55 -0.8271 0.0483 0.9238 (-----*-----)

276

65 -0.3688 0.5067 1.3821 (----*-----)

75 0.9979 1.8733 2.7488 (----*-----)

--------+---------+---------+---------+-

-1.5 0.0 1.5 3.0



45 -0.8754 0.0000 0.8754 (-----*-----)

55 -0.8271 0.0483 0.9238 (-----*-----)

65 -0.3688 0.5067 1.3821 (----*-----)

75 0.9979 1.8733 2.7488 (----*-----)

--------+---------+---------+---------+-

-1.5 0.0 1.5 3.0



55 -0.8271 0.0483 0.9238 (-----*-----)

65 -0.3688 0.5067 1.3821 (----*-----)

75 0.9979 1.8733 2.7488 (----*-----)

--------+---------+---------+---------+-

-1.5 0.0 1.5 3.0



65 -0.4171 0.4583 1.3338 (-----*-----)

75 0.9496 1.8250 2.7004 (-----*-----)

--------+---------+---------+---------+-

-1.5 0.0 1.5 3.0



75 0.4912 1.3667 2.2421 (-----*-----)

--------+---------+---------+---------+-

-1.5 0.0 1.5 3.0

277

STATRISTICAL ANALYSIS (B)

ANOVA results showing differences in glucose yields across moisture

One-way ANOVA: IA glucose yield (% theoretical, at cellulase enzyme loading of 15FPU/g

glucan) versus moisture

Source DF SS MS F P

Moisture 5 146.11 29.22 14.88 0.000

Error 12 23.57 1.96

Total 17 169.68

S = 1.402 R-Sq = 86.11% R-Sq(adj) = 80.32%


Pooled StDev

Level N Mean StDev -+---------+---------+---------+--------

25 3 27.530 1.034 (----*----)

35 3 24.696 0.728 (----*----)

45 3 25.563 2.226 (----*----)

55 3 20.980 2.194 (----*----)

65 3 23.883 0.130 (----*----)

75 3 18.989 0.635 (----*----)

-+---------+---------+---------+--------

17.5 21.0 24.5 28.0



All Pairwise Comparisons among Levels of Moisture


Moisture = 25 subtracted from:

Moisture Lower Center Upper -+---------+---------+---------+--------

35 -6.677 -2.834 1.010 (-----*------)

45 -5.810 -1.967 1.877 (------*-----)

55 -10.393 -6.550 -2.706 (-----*-----)

65 -7.490 -3.647 0.197 (-----*-----)

75 -12.384 -8.541 -4.697 (------*-----)

-+---------+---------+---------+--------

-12.0 -6.0 0.0 6.0



45 -2.977 0.867 4.710 (-----*------)

55 -7.560 -3.716 0.127 (------*-----)

65 -4.657 -0.813 3.030 (------*-----)

75 -9.551 -5.707 -1.864 (-----*------)

-+---------+---------+---------+--------

-12.0 -6.0 0.0 6.0

278



55 -8.427 -4.583 -0.739 (-----*------)

65 -5.523 -1.680 2.164 (-----*------)

75 -10.417 -6.574 -2.730 (-----*-----)

-+---------+---------+---------+--------

-12.0 -6.0 0.0 6.0



65 -0.940 2.903 6.747 (------*-----)

75 -5.834 -1.991 1.853 (------*-----)

-+---------+---------+---------+--------

-12.0 -6.0 0.0 6.0



75 -8.738 -4.894 -1.051 (------*-----)

-+---------+---------+---------+--------

-12.0 -6.0 0.0 6.0

One-way ANOVA: PA glucose yield (% theoretical, at cellulase enzyme loading of

15FPU/g glucan) versus moisture

Source DF SS MS F P

Moisture 5 493.70 98.74 19.41 0.000

Error 12 61.06 5.09

Total 17 554.75

S = 2.256 R-Sq = 88.99% R-Sq(adj) = 84.41%


Pooled StDev

Level N Mean StDev ---+---------+---------+---------+------

25 3 6.902 1.641 (----*---)

35 3 23.254 0.814 (----*---)

45 3 16.070 0.526 (----*----)

55 3 17.377 4.335 (----*----)

65 3 18.396 0.872 (----*---)

75 3 11.222 2.710 (----*---)

---+---------+---------+---------+------

6.0 12.0 18.0 24.0



All Pairwise Comparisons among Levels of Moisture


279


Moisture Lower Center Upper -----+---------+---------+---------+----

35 10.165 16.351 22.537 (-----*----)

45 2.982 9.168 15.354 (-----*----)

55 4.288 10.474 16.660 (----*----)

65 5.308 11.494 17.680 (-----*----)

75 -1.867 4.319 10.505 (-----*----)

-----+---------+---------+---------+----

-12 0 12 24



45 -13.369 -7.183 -0.997 (----*----)

55 -12.063 -5.877 0.309 (----*----)

65 -11.044 -4.858 1.328 (----*----)

75 -18.218 -12.032 -5.846 (----*----)

-----+---------+---------+---------+----

-12 0 12 24



55 -4.880 1.306 7.492 (----*----)

65 -3.860 2.326 8.512 (----*----)

75 -11.035 -4.849 1.337 (----*----)

-----+---------+---------+---------+----

-12 0 12 24



65 -5.167 1.019 7.205 (----*----)

75 -12.341 -6.155 0.031 (----*----)

-----+---------+---------+---------+----

-12 0 12 24



75 -13.360 -7.174 -0.988 (----*----)

-----+---------+---------+---------+----

-12 0 12 24

280

APPENDIX D: Impact of wet storage organic acids on pretreatment and

bioconversion of corn stover to ethanol

(Chapter 5)

This appendix contains supplementary materials to Chapter 5. This includes a flowchart of

sample processing and analysis, the experimental design, experimental data on pretreatment (pH,

organic acids, 5-HMF and furfural, glucan and xylan removal) as well as ethanol yields from

fermentation.

Figure D1: Flowchart of sample processing and analysis for determining effect of storage

organic acids on ethanol fermentation process

281

Table D1: Experimental design for examining effect of organic acids on fermentation

Dependent Variable: GLUCOSE YIELD

Independent Factors

Storage

Duration

(Days)

Nominal Moisture ContentNegative

Control

Positive

Control

25% 35% 45% 55% 65% 75%No

Feedstock

(Blanks)

Alpha-

cellulose

(Avicel)

Fermentation

Pretreatment

retention time

(min)

23oC 37oC 23oC 37oC 23oC 37oC 23oC 37oC 23oC 37oC 23oC 37oC

Corn stover

not washed

before

pretreatment

With

pretreatment

extract

50 2 reps 2 reps 2 reps 2 reps 2 reps 2 reps 2 reps 2 reps 2 reps 2 reps 2 reps 2 reps

2 reps 2 reps






Without

pretreatment

extract

50 2 reps * 2 reps * 2 reps * 2 reps * 2 reps * 2 reps *

2 reps 2 reps






Corn stover

Washed

before

pretreatment

With

pretreatment

extract

5 220 * 2 reps * 2 reps * 2 reps * 2 reps * 2 reps * 2 reps

2 reps 2 reps10 220 * 2 reps * 2 reps * 2 reps * 2 reps * 2 reps * 2 reps

15 220 * 2 reps * 2 reps * 2 reps * 2 reps * 2 reps * 2 reps

Without

pretreatment

extract

15 220 * 2 reps * 2 reps * 2 reps * 2 reps * 2 reps * 2 reps 2 reps 2 reps

282

Equation D1*:

(

)

Where: V72% = volume of concentrated sulfuric acid to add (mL)

C72% = concentration of 72% (w/w) sulfuric acid (1173.6 g/L)

C4% = concentration of 4% (w/w) sulfuric acid (41 g/L)

Vi = initial volume of sample (mL)

[H+] = concentration of hydrogen ions in sample = 10-pH

(moles/L)

98.08 = molar mass of sulfuric acid (H2SO4)

2 = number of moles of H+ in 1 mole of sulfuric acid

* Sluiter, A., Hames, B., Ruiz, R., Scarlata, C., Sluiter, J., and Templeton, D. (2008). "Determination of sugars,

byproducts, and degradation products in liquid fraction process samples." National Renewable Energy Laboratory,

Tech. Report NREL/TP-510-42623. http://www.nrel.gov/biomass/pdfs/42623.pdf

283

Table D2: Sugar removal and inhibitors generated during pretreatment of unwashed and washed

37oC samples†

Storage Duration


Nominal storage

moisture (%)

Glucan removed (%)

Xylan removed (%)

Furfural generated (%)

HMF generated (%)

Day 220

5 25 3.76 ± 0.50 23.94 ± 2.74 0.31 ± 0.31 0.03 ± 0.03

5 35 3.08 ± 0.61 22.75 ± 2.64 0.16 ± 0.04 0.03 ± 0.01

5 45 3.18 ± 0.20 30.15 ± 2.86 0.32 ± 0.13 0.05 ± 0.02

5 55 3.02 ± 0.07 27.63 ± 0.72 0.22 ± 0.12 0.03 ± 0.01

5 65 2.68 ± 0.14 24.37 ± 1.19 0.12 ± 0.01 0.01 ± 0.00

5 75 2.07 ± 0.21 18.86 ± 1.25 0.19 ± 0.04 0.02 ± 0.01

10 25 3.71 ± 0.04 27.14 ± 0.33 0.33 ± 0.04 0.03 ± 0.00

10 35 2.94 ± 0.20 23.36 ± 0.95 0.41 ± 0.01 0.05 ± 0.01

10 45 2.88 ± 0.15 29.03 ± 1.42 0.54 ± 0.16 0.06 ± 0.01

10 55 2.96 ± 0.01 28.09 ± 0.83 0.38 ± 0.21 0.03 ± 0.01

10 65 2.88 ± 0.33 25.24 ± 1.85 0.33 ± 0.01 0.02 ± 0.00

10 75 2.81 ± 0.20 27.01 ± 1.39 0.41 ± 0.11 0.03 ± 0.01

15 25 2.74 ± 1.23 21.93 ± 5.78 0.60 ± 0.12 0.05 ± 0.01

15 35 2.80 ± 0.04 22.74 ± 1.54 0.35 ± 0.40 0.04 ± 0.03

15 45 2.89 ± 0.04 24.81 ± 1.57 0.78 ± 0.01 0.06 ± 0.00

15 55 3.18 ± 0.16 26.96 ± 1.01 0.54 ± 0.44 0.05 ± 0.04

15 65 2.74 ± 0.01 23.62 ± 2.34 0.30 ± 0.23 0.02 ± 0.01

15 75 2.53 ± 0.18 23.28 ± 2.35 0.50 ± 0.20 0.04 ± 0.01

WASHED

15 25 3.43 ± 0.55 31.75 ± 1.95 0.93 ± 0.05 ND*

15 35 3.41 ± 0.52 38.62 ± 6.75 1.20 ± 0.10 ND

15 45 3.32 ± 0.34 39.60 ± 5.13 1.33 ± 0.06 ND

15 55 3.24 ± 0.37 36.97 ± 2.72 1.41 ± 0.02 ND

15 65 2.67 ± 0.14 32.77 ± 0.88 1.19 ± 0.06 ND

15 75 2.74 ± 0.41 32.85 ± 1.53 0.69 ± 0.01 ND

† Washed data are for samples fermented without extract. The data for washed samples fermented with extract at 5,

10 and 15 minutes was missing. Glucan and xylan removal are percentage of the total glucan (or xylan) polymer in

the feedstock before pretreatment. Furfural and HMF generated are percentage of dry matter. Same definitions apply

to Table D3.

*ND = Not detected

284

Table D3: Sugar removal and inhibitors generated during pretreatment of unwashed 23oC

samples (includes replicates of samples fermented with and without pretreatment extract)

Storage Duration


Nominal storage

moisture (%)

Glucan removed (%)

Xylan removed (%)

Furfural generated

(%)

HMF generated

(%)

Day 220

5 25 3.35 ± 0.18 25.28 ± 0.18 0.22 ± 0.21 0.03 ± 0.01

5 35 3.34 ± 0.83 29.52 ± 2.53 0.19 ± 0.01 0.03 ± 0.01

5 45 2.64 ± 0.45 24.70 ± 3.19 0.29 ± 0.08 0.04 ± 0.02

5 55 3.74 ± 1.35 23.91 ± 0.03 0.24 ± 0.07 0.02 ± 0.01

5 65 3.16 ± 0.99 27.34 ± 0.90 0.16 ± 0.03 0.01 ± 0.01

5 75 3.8 ± 1.69 29.10 ± 0.75 0.20 ± 0.03 0.02 ± 0.01

10 25 3.12 ± 0.26 26.88 ± 3.01 0.32 ± 0.04 0.03 ± 0.00

10 35 3.00 ± 0.39 30.42 ± 1.35 0.47 ± 0.04 0.05 ± 0.01

10 45 2.43 ± 0.35 30.09 ± 1.62 0.52 ± 0.06 0.05 ± 0.01

10 55 3.65 ± 1.26 32.16 ± 0.30 0.48 ± 0.18 0.04 ± 0.01

10 65 3.50 ± 1.47 27.70 ± 3.86 0.45 ± 0.03 0.03 ± 0.00

10 75 3.19 ± 0.67 27.68 ± 1.44 0.47 ± 0.04 0.03 ± 0.00

15 25 3.46 ± 0.22 26.24 ± 0.53 0.59 ± 0.08 0.05 ± 0.01

15 35 2.92 ± 0.85 27.51 ± 1.87 0.59 ± 0.34 0.06 ± 0.02

15 45 2.58 ± 0.25 24.28 ± 0.29 0.94 ± 0.02 0.07 ± 0.01

15 55 3.64 ± 1.00 27.17 ± 1.32 0.79 ± 0.37 0.06 ± 0.02

15 65 2.79 ± 2.00 19.88 ± 5.17 0.68 ± 0.13 0.05 ± 0.01

15 75 2.98 ± 0.38 30.44 ± 1.64 0.89 ± 0.12 0.05 ± 0.01

Day 0

5 25 3.74 ± 0.82 22.27 ± 1.28 0.07 ± 0.04 0.02 ± 0.01

5 35 3.29 ± 1.31 23.52 ± 0.84 0.09 ± 0.01 0.02 ± 0.01

5 45 3.61 ± 1.25 21.65 ± 0.05 0.09 ± 0.00 0.02 ± 0.00

5 55 4.26 ± 0.45 22.84 ± 2.82 0.08 ± 0.01 0.02 ± 0.01

5 65 3.66 ± 0.83 19.10 ± 0.72 0.08 ± 0.01 0.01 ± 0.00

5 75 5.32 ± 0.34 22.30 ± 4.50 0.07 ± 0.01 0.01 ± 0.00

10 25 3.94 ± 0.66 28.76 ± 0.21 0.26 ± 0.01 0.03 ± 0.01

10 35 3.76 ± 1.27 28.64 ± 1.60 0.25 ± 0.03 0.04 ± 0.01

10 45 3.12 ± 2.44 26.79 ± 3.25 0.25 ± 0.01 0.03 ± 0.01

10 55 4.31 ± 0.74 29.46 ± 0.24 0.23 ± 0.03 0.03 ± 0.01

10 65 4.41 ± 0.63 23.76 ± 3.15 0.22 ± 0.02 0.02 ± 0.00

10 75 4.84 ± 1.06 25.99 ± 1.65 0.21 ± 0.02 0.02 ± 0.00

15 25 3.80 ± 0.80 27.70 ± 1.30 0.49 ± 0.02 0.05 ± 0.00

15 35 3.96 ± 1.33 27.25 ± 0.64 0.55 ± 0.08 0.07 ± 0.02

15 45 3.66 ± 1.44 26.88 ± 1.81 0.53 ± 0.03 0.05 ± 0.01

15 55 4.35 ± 0.78 27.71 ± 1.82 0.47 ± 0.05 0.04 ± 0.00

15 65 4.03 ± 0.60 26.71 ± 0.64 0.49 ± 0.05 0.04 ± 0.00

15 75 5.00 ± 0.29 26.34 ± 2.35 0.41 ± 0.04 0.03 ± 0.00

285

Table D4: Organic acids and pH after pretreatment of unwashed 23oC samples

Storage duration


Nominal storage

moisture (%) pH

Acetic acid

(% DM) Lactic acid

(% DM)

Isobutyric acid

(% DM)

Formic acid

(% DM) Malic acid

(% DM)

Pyruvic acid

(% DM)

Tartaric acid

(% DM) Total acids

(% DM)

Day 220

5 25 4.52 ± 0.19 0.69 ± 0.18 1.68 ± 0.15 1.15 ± 0.57 0.2 ± 0.08 0.12 ± 0.09 0.22 ± 0.11 0.72 ± 0.69 4.78 ± 0.84

5 35 4.44 ± 0.07 0.53 ± 0.14 2.38 ± 0.58 1.1 ± 0.37 0.17 ± 0.06 0.23 ± 0.06 0.24 ± 0.06 0.31 ± 0.09 4.94 ± 0.80

5 45 4.24 ± 0.07 0.55 ± 0.23 3.15 ± 0.26 1.46 ± 0.41 0.18 ± 0.06 0.93 ± 1.66 0.26 ± 0.14 0.26 ± 0.17 6.78 ± 2.30

5 55 4.21 ± 0.09 0.35 ± 0.25 3.60 ± 0.17 1.18 ± 0.15 0.15 ± 0.02 0.67 ± 1.11 0.23 ± 0.14 0.18 ± 0.16 6.36 ± 0.74

5 65 4.43 ± 0.06 0.48 ± 0.02 3.41 ± 0.24 0.00 ± 0.00 0.13 ± 0.01 0.05 ± 0.01 0.14 ± 0.00 0.01 ± 0.01 4.20 ± 0.23

5 75 4.37 ± 0.03 0.53 ± 0.05 3.46 ± 0.05 0.00 ± 0.00 0.14 ± 0.01 0.08 ± 0.01 0.14 ± 0.01 0.03 ± 0.01 4.40 ± 0.03

10 25 4.36 ± 0.05 0.55 ± 0.36 1.76 ± 0.19 1.30 ± 0.06 0.21 ± 0.01 0.14 ± 0.02 0.21 ± 0.05 0.39 ± 0.26 4.55 ± 0.19

10 35 4.28 ± 0.05 0.79 ± 0.07 2.87 ± 0.36 1.77 ± 0.09 0.26 ± 0.03 0.19 ± 0.04 0.23 ± 0.05 0.35 ± 0.10 6.46 ± 0.49

10 45 4.15 ± 0.05 0.46 ± 0.54 3.12 ± 0.09 2.08 ± 0.51 0.17 ± 0.11 1.16 ± 1.53 0.28 ± 0.15 0.09 ± 0.11 7.36 ± 2.12

10 55 4.15 ± 0.12 0.39 ± 0.39 3.52 ± 0.21 1.59 ± 0.41 0.19 ± 0.05 0.78 ± 0.81 0.40 ± 0.17 0.00 ± 0.00 6.88 ± 1.18

10 65 4.26 ± 0.07 0.46 ± 0.33 2.14 ± 1.54 1.20 ± 0.99 1.1 ± 0.99 1.86 ± 1.92 0.10 ± 0.11 0.8 ± 1.45 7.66 ± 1.44

10 75 4.19 ± 0.10 0.86 ± 0.03 3.46 ± 0.30 2.52 ± 0.19 0.29 ± 0.05 0.31 ± 0.21 0.16 ± 0.01 0.03 ± 0.01 7.63 ± 0.45

15 25 4.35 ± 0.17 0.87 ± 0.06 1.71 ± 0.25 1.83 ± 0.35 0.27 ± 0.05 0.23 ± 0.03 0.23 ± 0.04 0.34 ± 0.18 5.47 ± 0.47

15 35 4.21 ± 0.21 0.88 ± 0.61 2.50 ± 0.40 2.30 ± 1.13 0.34 ± 0.13 0.36 ± 0.25 0.25 ± 0.03 0.20 ± 0.10 6.83 ± 2.40

15 45 4.11 ± 0.07 0.62 ± 0.31 3.29 ± 0.07 2.29 ± 0.30 0.29 ± 0.04 0.58 ± 0.48 0.2 ± 0.020 0.35 ± 0.15 7.61 ± 0.96

15 55 4.11 ± 0.16 0.50 ± 0.46 3.79 ± 0.11 2.20 ± 0.79 0.26 ± 0.09 1.68 ± 1.51 0.28 ± 0.17 0.05 ± 0.10 8.76 ± 2.19

15 65 4.14 ± 0.15 1.00 ± 0.11 3.59 ± 0.23 2.26 ± 0.61 0.36 ± 0.06 0.67 ± 0.32 0.20 ± 0.02 0.01 ± 0.01 8.08 ± 0.86

15 75 4.18 ± 0.18 1.04 ± 0.08 3.61 ± 0.20 2.93 ± 0.79 0.36 ± 0.03 1.00 ± 0.52 0.19 ± 0.01 0.00 ± 0.00 9.15 ± 0.92

286

Table D4 cont:

Storage duration


Nominal storage

moisture (%) pH

Acetic acid

(% DM) Lactic acid

(% DM)

Isobutyric acid

(% DM)

Formic acid

(% DM) Malic acid

(% DM)

Pyruvic acid

(% DM)

Tartaric acid

(% DM) Total acids

(% DM)

Day 0

5 25 4.64 ± 0.06 0.76 ± 0.22 0.00 ± 0.00 0.61 ± 0.19 0.13 ± 0.08 0.12 ± 0.06 0.20 ± 0.28 0.58 ± 0.96 2.40 ± 1.61

5 35 4.61 ± 0.09 0.83 ± 0.21 0.00 ± 0.00 0.49 ± 0.07 0.16 ± 0.03 0.13 ± 0.04 0.48 ± 0.03 0.83 ± 0.12 2.91 ± 0.37

5 45 4.62 ± 0.09 0.86 ± 0.14 0.35 ± 0.49 0.77 ± 0.34 0.17 ± 0.02 0.19 ± 0.02 0.35 ± 0.13 0.75 ± 0.88 3.43 ± 1.12

5 55 4.64 ± 0.08 0.91 ± 0.12 0.27 ± 0.35 0.74 ± 0.17 0.15 ± 0.02 0.1 ± 0.03 0.36 ± 0.10 1.08 ± 0.31 3.60 ± 0.71

5 65 4.61 ± 0.15 0.79 ± 0.39 0.00 ± 0.00 0.74 ± 0.12 0.12 ± 0.08 0.07 ± 0.02 1.46 ± 0.80 2.76 ± 0.73 5.93 ± 1.37

5 75 4.71 ± 0.03 1.21 ± 0.33 0.00 ± 0.01 0.82 ± 0.11 0.32 ± 0.33 0.07 ± 0.03 0.79 ± 0.43 1.67 ± 1.84 4.88 ± 2.78

10 25 4.43 ± 0.07 0.99 ± 0.13 0.00 ± 0.02 1.18 ± 0.23 0.26 ± 0.02 0.22 ± 0.03 0.50 ± 0.22 0.33 ± 0.43 3.47 ± 0.53

10 35 4.37 ± 0.07 0.95 ± 0.05 0.28 ± 0.55 1.24 ± 0.4 0.23 ± 0.04 0.19 ± 0.07 0.43 ± 0.11 0.75 ± 0.26 4.07 ± 0.85

10 45 4.39 ± 0.08 1.02 ± 0.17 0.21 ± 0.33 1.28 ± 0.41 0.27 ± 0.03 0.31 ± 0.15 0.31 ± 0.07 0.06 ± 0.07 3.44 ± 0.49

10 55 4.44 ± 0.07 0.98 ± 0.12 0.34 ± 0.40 1.31 ± 0.25 0.24 ± 0.03 0.2 ± 0.08 0.35 ± 0.09 0.16 ± 0.23 3.57 ± 0.36

10 65 4.4 ± 0.12 0.82 ± 0.37 0.00 ± 0.00 1.88 ± 1.35 0.83 ± 0.91 0.94 ± 1.56 1.49 ± 1.04 0.88 ± 1.59 6.84 ± 4.42

10 75 4.5 ± 0.06 1.41 ± 0.50 0.00 ± 0.01 1.32 ± 0.39 0.55 ± 0.58 0.82 ± 1.4 0.49 ± 0.59 0.18 ± 0.22 4.76 ± 3.20

15 25 4.26 ± 0.05 1.22 ± 0.20 0.00 ± 0.02 2.06 ± 0.15 0.28 ± 0.13 0.28 ± 0.04 0.37 ± 0.05 0.56 ± 0.47 4.76 ± 0.32

15 35 4.26 ± 0.09 1.19 ± 0.15 0.56 ± 0.65 2.14 ± 0.57 0.36 ± 0.11 0.35 ± 0.09 0.42 ± 0.15 0.62 ± 0.17 5.63 ± 1.44

15 45 4.23 ± 0.08 1.30 ± 0.27 0.28 ± 0.36 2.44 ± 0.34 0.31 ± 0.05 0.45 ± 0.29 0.37 ± 0.02 0.42 ± 0.22 5.58 ± 0.93

15 55 4.28 ± 0.05 1.29 ± 0.19 0.30 ± 0.34 2.04 ± 0.29 0.34 ± 0.03 0.25 ± 0.05 0.29 ± 0.06 0.58 ± 0.1 5.09 ± 0.87

15 65 4.28 ± 0.02 1.20 ± 0.57 0.00 ± 0.00 2.30 ± 0.24 0.40 ± 0.03 0.22 ± 0.04 0.15 ± 0.17 0.61 ± 0.04 4.86 ± 0.44

15 75 4.30 ± 0.03 1.39 ± 0.14 0.00 ± 0.01 2.29 ± 0.23 0.38 ± 0.02 0.22 ± 0.04 0.23 ± 0.03 0.56 ± 0.05 5.08 ± 0.32

287

Table D5: Organic acids and pH after pretreatment of unwashed and washed 37oC samples (ND = Not detected)

Storage duration


Nominal storage

moisture (%) pH

Acetic acid

(% DM) Lactic acid

(% DM)

Isobutyric acid

(% DM)

Formic acid

(% DM) Malic acid

(% DM)

Pyruvic acid

(% DM)

Tartaric acid

(% DM) Total acids

(% DM)

Day 220

5 25 4.50 ± 0.05 0.71 ± 0.33 1.73 ± 0.28 1.22 ± 0.99 0.21 ± 0.14 0.15 ± 0.14 0.23 ± 0.01 0.85 ± 0.83 5.11 ± 1.05

5 35 4.45 ± 0.06 0.44 ± 0.02 2.27 ± 0.72 0.80 ± 0.17 0.12 ± 0.02 0.16 ± 0.01 0.20 ± 0.01 0.26 ± 0.02 4.24 ± 0.86

5 45 4.30 ± 0.09 0.65 ± 0.38 3.08 ± 0.15 1.60 ± 0.72 0.21 ± 0.10 1.79 ± 2.42 0.33 ± 0.20 0.16 ± 0.22 7.82 ± 3.74

5 55 4.28 ± 0.01 0.23 ± 0.32 3.80 ± 0.53 1.13 ± 0.28 0.14 ± 0.04 1.18 ± 1.48 0.30 ± 0.17 0.09 ± 0.13 6.88 ± 0.35

5 65 4.16 ± 0.01 0.40 ± 0.04 2.89 ± 0.06 0.00 ± 0.00 0.11 ± 0.01 0.04 ± 0.00 0.12 ± 0.00 0.01 ± 0.01 3.56 ± 0.00

5 75 4.69 ± 0.03 0.53 ± 0.11 3.45 ± 0.48 0.00 ± 0.01 0.15 ± 0.04 0.09 ± 0.01 0.14 ± 0.05 0.04 ± 0.03 4.40 ± 0.72

10 25 4.34 ± 0.03 0.40 ± 0.57 1.91 ± 0.26 1.29 ± 0.01 0.21 ± 0.00 0.14 ± 0.03 0.22 ± 0.06 0.27 ± 0.38 4.46 ± 0.34

10 35 4.30 ± 0.01 0.74 ± 0.16 2.83 ± 0.59 1.65 ± 0.22 0.25 ± 0.05 0.19 ± 0.04 0.22 ± 0.03 0.27 ± 0.09 6.15 ± 0.94

10 45 4.18 ± 0.00 0.53 ± 0.74 3.52 ± 0.61 1.89 ± 0.48 0.27 ± 0.09 0.39 ± 0.12 0.24 ± 0.01 0.20 ± 0.01 7.04 ± 1.82

10 55 4.15 ± 0.04 0.37 ± 0.52 3.77 ± 0.14 1.42 ± 0.59 0.18 ± 0.09 1.33 ± 1.49 0.35 ± 0.28 0.00 ± 0.00 7.42 ± 3.12

10 65 4.03 ± 0.02 0.55 ± 0.08 2.96 ± 0.09 1.46 ± 0.07 0.10 ± 0.14 1.07 ± 1.18 0.26 ± 0.21 0.01 ± 0.01 6.41 ± 1.78

10 75 4.51 ± 0.03 0.78 ± 0.26 3.61 ± 0.86 2.15 ± 0.64 0.29 ± 0.16 0.62 ± 0.40 0.15 ± 0.03 0.01 ± 0.02 7.62 ± 1.56

15 25 4.23 ± 0.08 0.87 ± 0.06 1.63 ± 0.35 1.88 ± 0.39 0.28 ± 0.05 0.21 ± 0.02 0.23 ± 0.05 0.22 ± 0.21 5.32 ± 0.72

15 35 4.09 ± 0.01 0.40 ± 0.56 2.16 ± 0.50 1.31 ± 1.08 0.21 ± 0.13 0.29 ± 0.34 0.19 ± 0.02 0.13 ± 0.12 4.67 ± 2.75

15 45 3.96 ± 0.00 0.66 ± 0.10 2.72 ± 0.03 1.96 ± 0.07 0.24 ± 0.00 0.68 ± 0.53 0.17 ± 0.01 0.30 ± 0.19 6.72 ± 0.92

15 55 4.01 ± 0.01 0.43 ± 0.61 3.60 ± 0.20 1.66 ± 1.01 0.20 ± 0.13 0.75 ± 0.42 0.18 ± 0.05 0.10 ± 0.14 6.93 ± 1.73

15 65 3.90 ± 0.03 0.59 ± 0.21 3.05 ± 0.51 0.86 ± 1.22 0.22 ± 0.12 0.29 ± 0.17 0.16 ± 0.01 0.01 ± 0.01 5.18 ± 1.19

15 75 4.37 ± 0.08 0.87 ± 0.23 3.50 ± 0.28 1.32 ± 1.87 0.31 ± 0.02 0.69 ± 0.63 0.18 ± 0.03 0.00 ± 0.00 6.87 ± 2.49

WASHED

Day 220

15 25 4.00 ± 0.01 0.83 ± 0.08 0.29 ± 0.01 ND 0.38 ± 0.02 1.78 ± 2.10 0.23 ± 0.15 ND 3.51 ± 2.14

15 35 3.96 ± 0.01 0.89 ± 0.00 0.37 ± 0.09 ND 0.34 ± 0.01 1.93 ± 2.23 0.22 ± 0.17 ND 3.74 ± 2.51

15 45 3.95 ± 0.06 0.87 ± 0.01 0.38 ± 0.09 ND 0.38 ± 0.04 2.42 ± 2.83 0.08 ± 0.04 ND 4.13 ± 2.93

15 55 3.86 ± 0.01 0.91 ± 0.02 0.32 ± 0.01 ND 0.31 ± 0.00 2.90 ± 2.23 0.23 ± 0.11 ND 4.67 ± 2.31

15 65 3.82 ± 0.02 0.79 ± 0.04 0.41 ± 0.14 ND 0.28 ± 0.03 3.26 ± 0.10 0.30 ± 0.02 ND 5.04 ± 0.02

15 75 3.96 ± 0.00 0.93 ± 0.06 0.17 ± 0.07 ND 0.34 ± 0.03 0.37 ± 0.01 0.09 ± 0.00 ND 2 .00 ±0.10

288

Table D6: Concentration of potential inhibitors in pretreatment extract of unwashed samples

stored at 23oC†

† Includes data from replicates to be fermented with and without the pretreatment extract

Storage

duration

Pretreat

ment

retention

time

(min)

Nominal

storage

moisture

(%)

Lactic acid

(g/L)

Acetic acid

(g/L)

Isobutyric

acid (g/L) HMF (g/L) Furfural (g/L)

5 25 2.12 + 0.22 0.87 + 0.22 1.44 + 0.73 0.04 + 0.02 0.28 + 0.26

5 35 2.74 + 0.51 0.61 + 0.06 1.30 + 0.53 0.04 + 0.01 0.23 + 0.02

5 45 3.99 + 0.25 0.68 + 0.25 1.84 + 0.42 0.05 + 0.02 0.36 + 0.07

5 55 4.86 + 0.40 0.50 + 0.37 1.61 + 0.38 0.03 + 0.01 0.34 + 0.13

5 65 4.15 + 0.06 0.58 + 0.05 0.00 + 0.00 0.02 + 0.01 0.20 + 0.03

5 75 4.27 + 0.14 0.65 + 0.09 0.00 + 0.00 0.02 + 0.00 0.24 + 0.04

10 25 2.23 + 0.41 0.71 + 0.48 1.63 + 0.07 0.04 + 0.00 0.40 + 0.03

10 35 3.54 + 0.70 0.98 + 0.16 2.17 + 0.25 0.06 + 0.01 0.58 + 0.03

10 45 3.95 + 0.43 0.62 + 0.72 2.61 + 0.56 0.06 + 0.01 0.66 + 0.10

10 55 4.38 + 0.32 0.51 + 0.50 1.99 + 0.54 0.05 + 0.01 0.60 + 0.22

10 65 2.67 + 1.92 0.58 + 0.41 1.51 + 1.22 0.04 + 0.00 0.56 + 0.01

10 75 4.73 + 0.31 1.18 + 0.09 3.44 + 0.35 0.04 + 0.01 0.63 + 0.05

15 25 2.12 + 0.21 1.08 + 0.06 2.28 + 0.44 0.07 + 0.01 0.73 + 0.08

15 35 3.14 + 0.48 1.12 + 0.78 2.92 + 1.46 0.07 + 0.03 0.75 + 0.43

15 45 3.94 + 0.10 0.74 + 0.38 2.74 + 0.34 0.09 + 0.01 1.13 + 0.03

15 55 4.41 + 0.14 0.58 + 0.54 2.58 + 0.95 0.07 + 0.03 0.93 + 0.44

15 65 4.70 + 0.16 1.31 + 0.19 3.00 + 0.95 0.06 + 0.01 0.90 + 0.21

15 75 4.32 + 0.21 1.25 + 0.15 3.52 + 0.96 0.07 + 0.01 1.07 + 0.12

5 25 0.00 + 0.00 0.91 + 0.24 0.73 + 0.21 0.02 + 0.01 0.07 + 0.05

5 35 0.00 + 0.00 0.97 + 0.25 0.56 + 0.07 0.03 + 0.01 0.11 + 0.01

5 45 0.45 + 0.64 1.05 + 0.16 0.94 + 0.44 0.02 + 0.01 0.11 + 0.01

5 55 0.32 + 0.43 1.08 + 0.16 0.90 + 0.22 0.02 + 0.01 0.10 + 0.01

5 65 0.00 + 0.00 1.00 + 0.53 0.92 + 0.18 0.01 + 0.00 0.09 + 0.00

5 75 0.00 + 0.00 1.46 + 0.45 0.98 + 0.15 0.01 + 0.00 0.08 + 0.01

10 25 0.00 + 0.00 1.20 + 0.15 1.44 + 0.34 0.04 + 0.01 0.31 + 0.01

10 35 0.33 + 0.66 1.16 + 0.05 1.52 + 0.45 0.04 + 0.01 0.31 + 0.03

10 45 0.28 + 0.45 1.26 + 0.16 1.61 + 0.63 0.04 + 0.01 0.31 + 0.02

10 55 0.41 + 0.47 1.24 + 0.14 1.64 + 0.25 0.03 + 0.00 0.29 + 0.02

10 65 0.00 + 0.00 1.09 + 0.48 2.59 + 1.96 0.03 + 0.00 0.30 + 0.02

10 75 0.00 + 0.00 1.74 + 0.60 1.64 + 0.56 0.02 + 0.01 0.26 + 0.03

15 25 0.00 + 0.00 1.49 + 0.27 2.52 + 0.15 0.06 + 0.00 0.60 + 0.01

15 35 0.68 + 0.79 1.43 + 0.20 2.59 + 0.76 0.08 + 0.03 0.66 + 0.11

15 45 0.34 + 0.43 1.66 + 0.43 3.09 + 0.56 0.07 + 0.01 0.67 + 0.05

15 55 0.36 + 0.41 1.62 + 0.27 2.55 + 0.23 0.05 + 0.00 0.58 + 0.03

15 65 0.00 + 0.00 1.54 + 0.77 2.91 + 0.19 0.06 + 0.01 0.62 + 0.03

15 75 0.00 + 0.00 1.73 + 0.27 2.84 + 0.29 0.04 + 0.01 0.52 + 0.03

Day 220

Day 0

289

Table D7: Concentration of potential inhibitors in pretreatment extract of unwashed and washed

samples stored at 37oC

Storage

duration

Pretreat

ment

retention

time

(min)

Nominal

storage

moisture

(%)

Lactic acid

(g/L)

Acetic acid

(g/L)

Isobutyric

acid (g/L) HMF (g/L) Furfural (g/L)

5 25 2.28 + 0.25 0.93 + 0.38 1.58 + 1.22 0.04 + 0.03 0.40 + 0.40

5 35 2.93 + 0.66 0.58 + 0.08 1.04 + 0.12 0.03 + 0.00 0.21 + 0.04

5 45 3.79 + 0.20 0.78 + 0.38 1.93 + 0.68 0.06 + 0.02 0.39 + 0.12

5 55 4.53 + 0.65 0.28 + 0.39 1.35 + 0.34 0.03 + 0.00 0.26 + 0.13

5 65 4.03 + 0.00 0.56 + 0.06 0.00 + 0.00 0.02 + 0.01 0.17 + 0.03

5 75 4.46 + 0.26 0.68 + 0.08 0.00 + 0.00 0.02 + 0.00 0.24 + 0.03

10 25 2.55 + 0.52 0.56 + 0.79 1.71 + 0.11 0.04 + 0.00 0.43 + 0.01

10 35 3.90 + 0.78 1.03 + 0.22 2.28 + 0.29 0.07 + 0.01 0.58 + 0.02

10 45 4.17 + 0.69 0.62 + 0.88 2.24 + 0.56 0.07 + 0.01 0.64 + 0.18

10 55 4.49 + 0.30 0.45 + 0.63 1.71 + 0.76 0.04 + 0.01 0.46 + 0.27

10 65 4.32 + 0.51 0.81 + 0.19 2.13 + 0.28 0.03 + 0.00 0.48 + 0.04

10 75 4.61 + 0.78 0.99 + 0.27 2.74 + 0.64 0.04 + 0.01 0.52 + 0.11

15 25 2.09 + 0.28 1.13 + 0.02 2.42 + 0.31 0.07 + 0.01 0.76 + 0.10

15 35 3.13 + 0.83 0.59 + 0.83 1.91 + 1.63 0.06 + 0.04 0.52 + 0.59

15 45 3.73 + 0.04 0.90 + 0.13 2.68 + 0.08 0.09 + 0.01 1.07 + 0.01

15 55 4.31 + 0.20 0.52 + 0.73 1.99 + 1.20 0.06 + 0.04 0.64 + 0.52

15 65 4.34 + 0.43 0.86 + 0.35 1.29 + 1.82 0.04 + 0.02 0.44 + 0.36

15 75 4.39 + 0.07 1.11 + 0.36 1.74 + 2.45 0.05 + 0.02 0.64 + 0.29

Lactic acid

(g/L)

Acetic acid

(g/L)

Malic acid

(g/L) HMF (g/L) Furfural (g/L)

15 25 0.33 + 0.01 0.95 + 0.10 2.02 + 2.38 ND 1.06 + 0.07

15 35 0.42 + 0.10 1.01 + 0.01 2.17 + 2.51 ND 1.36 + 0.10

15 45 0.44 + 0.10 0.99 + 0.01 2.78 + 3.25 ND 1.53 + 0.06

15 55 0.36 + 0.02 1.03 + 0.02 3.30 + 2.54 ND 1.60 + 0.02

15 65 0.48 + 0.16 0.92 + 0.05 3.81 + 0.13 ND 1.39 + 0.07

15 75 0.20 + 0.08 1.08 + 0.05 0.43 + 0.00 ND 0.80 + 0.02

Day 220

WASHED

290

Table D8: Summary stats on concentration of potential inhibitors in unwashed samples stored at

23oC

Concentration of potential inhibitors

Storage Mean StDev Minimum Maximum

Lactic acid, g/L Ensiled 3.68 1.04 1.00 5.25

Unensiled 0.18 0.39 0.00 1.47

Acetic acid, g/L Ensiled 0.81 0.42 0.00 1.69

Unensiled 1.31 0.41 0.27 2.62

HMF, g/L Ensiled 0.05 0.02 0.01 0.10

Unensiled 0.04 0.02 0.00 0.11

Furfural, g/L Ensiled 0.59 0.33 0.10 1.26

Unensiled 0.33 0.22 0.00 0.76

291

Table D9: Differences in pH before and after pretreatment.

Pretreatment

time (mins)

Storage

moisture

content

pH before

pretreatment

pH after

pretreatment

(Unwashed)

pH before -

pH after

(Unwashed)

pH before

pretreatment

(Storage pH)

pH after

pretreatment

(Unwashed)

pH after

pretreatment

(Washed*)

storage pH -

Pretreatment

pH

(Unwashed)

storage pH -

Pretreatment

pH (Washed)

pH before

pretreatment

(Storage pH)

pH after

pretreatment

(Unwashed)

pH after

pretreatment

(Washed*)

storage pH -

Pretreatment

pH

(Unwashed)

storage pH -

Pretreatment

pH (Washed)

5 25 6.69 ± 0.02 4.64 ± 0.01 2.05 ± 0.01 4.95 ± 0.05 4.50 ± 0.05 4.41 ± 0.05 0.41 ± 0.05 0.50 ± 0.05 4.90 ± 0.05 4.50 ± 0.05 4.41 ± 0.05 0.41 ± 0.05 0.50 ± 0.05

5 35 6.69 ± 0.11 4.62 ± 0.01 2.08 ± 0.01 4.41 ± 0.03 4.45 ± 0.06 4.35 ± 0.04 0.01 ± 0.06 0.12 ± 0.04 4.46 ± 0.03 4.45 ± 0.06 4.35 ± 0.04 0.01 ± 0.06 0.12 ± 0.04

5 45 6.65 ± 0.02 4.62 ± 0.04 2.04 ± 0.04 4.18 ± 0.02 4.30 ± 0.09 4.24 ± 0.01 -0.06 ± 0.09 0.00 ± 0.01 4.24 ± 0.02 4.3 ± 0.09 4.24 ± 0.01 -0.06 ± 0.09 0.00 ± 0.01

5 55 6.71 ± 0.03 4.64 ± 0.03 2.07 ± 0.03 4.05 ± 0.03 4.28 ± 0.01 4.20 ± 0.01 -0.17 ± 0.01 -0.09 ± 0.01 4.11 ± 0.02 4.28 ± 0.01 4.2 ± 0.01 -0.17 ± 0.01 -0.09 ± 0.01

5 65 6.54 ± 0.08 4.62 ± 0.05 1.93 ± 0.05 4.23 ± 0.03 4.16 ± 0.01 4.11 ± 0.02 -0.08 ± 0.01 -0.03 ± 0.02 4.08 ± 0.01 4.16 ± 0.01 4.11 ± 0.02 -0.08 ± 0.01 -0.03 ± 0.02

5 75 6.73 ± 0.05 4.72 ± 0.02 2.02 ± 0.02 4.24 ± 0.01 4.69 ± 0.03 4.36 ± 0.06 0.06 ± 0.03 0.39 ± 0.06 4.75 ± 0.03 4.69 ± 0.03 4.36 ± 0.06 0.06 ± 0.03 0.39 ± 0.06

10 25 6.69 ± 0.02 4.43 ± 0.05 2.27 ± 0.05 4.95 ± 0.05 4.34 ± 0.03 4.15 ± 0.02 0.56 ± 0.03 0.76 ± 0.02 4.90 ± 0.05 4.34 ± 0.03 4.15 ± 0.02 0.56 ± 0.03 0.76 ± 0.02

10 35 6.69 ± 0.11 4.37 ± 0.06 2.32 ± 0.06 4.41 ± 0.03 4.30 ± 0.01 4.08 ± 0.04 0.16 ± 0.01 0.39 ± 0.04 4.46 ± 0.03 4.30 ± 0.01 4.08 ± 0.04 0.16 ± 0.01 0.39 ± 0.04

10 45 6.65 ± 0.02 4.40 ± 0.04 2.26 ± 0.04 4.18 ± 0.02 4.18 ± 0.00 4.05 ± 0.00 0.06 ± 0.00 0.19 ± 0.00 4.24 ± 0.02 4.18 ± 0.00 4.05 ± 0.00 0.06 ± 0.00 0.19 ± 0.00

10 55 6.71 ± 0.03 4.44 ± 0.02 2.28 ± 0.02 4.05 ± 0.03 4.15 ± 0.04 3.98 ± 0.02 -0.04 ± 0.04 0.14 ± 0.02 4.11 ± 0.02 4.15 ± 0.04 3.98 ± 0.02 -0.04 ± 0.04 0.14 ± 0.02

10 65 6.54 ± 0.08 4.41 ± 0.06 2.14 ± 0.06 4.23 ± 0.03 4.03 ± 0.02 3.95 ± 0.02 0.06 ± 0.02 0.14 ± 0.02 4.08 ± 0.01 4.03 ± 0.02 3.95 ± 0.02 0.06 ± 0.02 0.14 ± 0.02

10 75 6.73 ± 0.05 4.51 ± 0.02 2.23 ± 0.02 4.24 ± 0.01 4.51 ± 0.03 4.09 ± 0.05 0.24 ± 0.03 0.67 ± 0.05 4.75 ± 0.03 4.51 ± 0.03 4.09 ± 0.05 0.24 ± 0.03 0.67 ± 0.05

15 25 6.69 ± 0.02 4.26 ± 0.01 2.43 ± 0.01 4.95 ± 0.05 4.23 ± 0.08 4.00 ± 0.01 0.67 ± 0.08 0.91 ± 0.01 4.90 ± 0.05 4.23 ± 0.08 4.00 ± 0.01 0.67 ± 0.08 0.91 ± 0.01

15 35 6.69 ± 0.11 4.26 ± 0.01 2.43 ± 0.01 4.41 ± 0.03 4.09 ± 0.01 3.96 ± 0.01 0.38 ± 0.01 0.51 ± 0.01 4.46 ± 0.03 4.09 ± 0.01 3.96 ± 0.01 0.38 ± 0.01 0.51 ± 0.01

15 45 6.65 ± 0.02 4.24 ± 0.01 2.42 ± 0.01 4.18 ± 0.02 3.96 ± 0.00 3.95 ± 0.06 0.28 ± 0.00 0.30 ± 0.06 4.24 ± 0.02 3.96 ± 0.00 3.95 ± 0.06 0.28 ± 0.00 0.30 ± 0.06

15 55 6.71 ± 0.03 4.29 ± 0.05 2.43 ± 0.05 4.05 ± 0.03 4.01 ± 0.01 3.86 ± 0.01 0.10 ± 0.01 0.26 ± 0.01 4.11 ± 0.02 4.01 ± 0.01 3.86 ± 0.01 0.10 ± 0.01 0.26 ± 0.01

15 65 6.54 ± 0.08 4.28 ± 0.00 2.26 ± 0.00 4.23 ± 0.03 3.90 ± 0.03 3.82 ± 0.02 0.18 ± 0.03 0.27 ± 0.02 4.08 ± 0.01 3.90 ± 0.03 3.82 ± 0.02 0.18 ± 0.03 0.27 ± 0.02

15 75 6.73 ± 0.05 4.30 ± 0.03 2.43 ± 0.03 4.24 ± 0.01 4.37 ± 0.08 3.96 ± 0.00 0.39 ± 0.08 0.79 ± 0.00 4.75 ± 0.03 4.37 ± 0.08 3.96 ± 0.00 0.39 ± 0.08 0.79 ± 0.00

Unensiled (Day 0) Ensiled (Day 220 at 23oC) Ensiled (Day 220 at 37oC)

* Washed samples were washed before pretreatment to get rid of storage organic acids

292

Table D10: Ethanol yield and inhibitor concentrations† in pretreated unwashed stover fermented with pretreatment extract

† Concentrations are grams per Liter of fermentation broth Since ethanol yields are calculated on percentage theoretical basis (Equation D2), based on amount

of glucose present in feedstock at the time of fermentation, these yields can be fairly compared with fermentation without extract. The amount of glucose for

samples fermented with extract includes glucose in the pretreatment solid and in the extract.

5 25 41.36 ± 1.53 0.46 ± 0.00 1.21 ± 0.17 0.85 ± 0.68 0.09 ± 0.00 0.02 ± 0.00 48.73 ± 1.46 0.48 ± 0.16 0.00 ± 0.00 0.32 ± 0.10 0.06 ± 0.00 0.01 ± 0.00

5 35 47.80 ± 5.54 0.32 ± 0.02 1.97 ± 0.04 0.70 ± 0.06 0.13 ± 0.00 0.02 ± 0.00 46.35 ± 1.20 0.45 ± 0.02 0.00 ± 0.00 0.34 ± 0.07 0.07 ± 0.00 0.02 ± 0.00

5 45 43.61 ± 0.70 0.30 ± 0.01 2.14 ± 0.04 1.10 ± 0.43 0.17 ± 0.02 0.02 ± 0.00 44.64 ± 0.11 0.66 ± 0.01 0.48 ± 0.33 0.54 ± 0.26 0.06 ± 0.00 0.01 ± 0.00

5 55 45.81 ± 0.37 0.34 ± 0.08 2.51 ± 0.06 0.74 ± 0.10 0.20 ± 0.02 0.02 ± 0.00 49.05 ± 1.03 0.65 ± 0.06 0.36 ± 0.21 0.40 ± 0.03 0.06 ± 0.00 0.01 ± 0.00

5 65 42.03 ± 0.38 0.33 ± 0.01 2.27 ± 0.25 0.00 ± 0.00 0.12 ± 0.01 0.01 ± 0.00 45.64 ± 0.93 0.61 ± 0.05 0.00 ± 0.00 0.43 ± 0.06 0.05 ± 0.00 0.01 ± 0.00

5 75 43.43 ± 1.26 0.34 ± 0.03 2.34 ± 0.02 0.00 ± 0.00 0.12 ± 0.03 0.01 ± 0.00 45.50 ± 1.22 0.63 ± 0.06 0.00 ± 0.00 0.50 ± 0.04 0.05 ± 0.01 0.01 ± 0.00

10 25 49.05 ± 0.61 0.49 ± 0.01 1.30 ± 0.08 0.88 ± 0.07 0.21 ± 0.01 0.02 ± 0.00 50.13 ± 2.45 0.68 ± 0.09 0.00 ± 0.00 0.78 ± 0.22 0.18 ± 0.00 0.02 ± 0.00

10 35 51.86 ± 1.63 0.52 ± 0.05 2.15 ± 0.18 1.26 ± 0.01 0.32 ± 0.01 0.03 ± 0.00 49.87 ± 1.18 0.65 ± 0.05 0.38 ± 0.53 0.62 ± 0.04 0.19 ± 0.02 0.03 ± 0.00

10 45 50.52 ± 0.91 0.36 ± 0.51 2.15 ± 0.10 1.14 ± 0.04 0.39 ± 0.04 0.03 ± 0.00 51.75 ± 1.53 0.66 ± 0.01 0.28 ± 0.26 0.82 ± 0.18 0.17 ± 0.01 0.02 ± 0.00

10 55 53.26 ± 0.09 0.33 ± 0.33 2.42 ± 0.23 0.89 ± 0.26 0.42 ± 0.02 0.03 ± 0.00 52.93 ± 0.81 0.69 ± 0.12 0.47 ± 0.00 0.74 ± 0.02 0.16 ± 0.02 0.02 ± 0.00

10 65 43.43 ± 4.97 0.51 ± 0.08 0.55 ± 0.02 0.24 ± 0.15 0.31 ± 0.04 0.02 ± 0.00 47.92 ± 2.81 0.69 ± 0.07 0.00 ± 0.00 0.73 ± 0.02 0.15 ± 0.02 0.01 ± 0.00

10 75 48.48 ± 0.68 0.59 ± 0.02 2.43 ± 0.33 1.67 ± 0.04 0.34 ± 0.01 0.02 ± 0.00 50.15 ± 0.83 0.78 ± 0.03 0.00 ± 0.00 0.69 ± 0.13 0.15 ± 0.00 0.01 ± 0.00

15 25 53.57 ± 2.99 0.59 ± 0.04 1.14 ± 0.26 1.32 ± 0.29 0.40 ± 0.00 0.04 ± 0.00 55.53 ± 4.21 0.89 ± 0.02 0.00 ± 0.00 1.35 ± 0.05 0.34 ± 0.01 0.03 ± 0.00

15 35 53.09 ± 10.32 0.88 ± 0.07 1.79 ± 0.43 1.09 ± 0.91 0.52 ± 0.05 0.05 ± 0.01 54.27 ± 1.97 0.75 ± 0.03 0.77 ± 0.09 1.18 ± 0.15 0.43 ± 0.01 0.06 ± 0.01

15 45 60.05 ± 5.37 0.30 ± 0.26 2.28 ± 0.06 1.64 ± 0.08 0.64 ± 0.00 0.05 ± 0.00 55.44 ± 0.98 0.81 ± 0.01 0.38 ± 0.20 1.49 ± 0.20 0.36 ± 0.03 0.04 ± 0.00

15 55 63.79 ± 11.19 0.37 ± 0.33 2.63 ± 0.08 1.21 ± 0.71 0.70 ± 0.04 0.05 ± 0.00 55.58 ± 0.56 0.80 ± 0.09 0.40 ± 0.01 1.23 ± 0.11 0.34 ± 0.01 0.03 ± 0.00

15 65 59.67 ± 0.57 0.73 ± 0.02 2.56 ± 0.15 1.21 ± 0.18 0.53 ± 0.05 0.03 ± 0.00 54.61 ± 0.35 1.04 ± 0.03 0.00 ± 0.00 1.47 ± 0.18 0.36 ± 0.00 0.03 ± 0.00

15 75 55.42 ± 1.48 0.70 ± 0.04 2.52 ± 0.14 1.70 ± 0.67 0.61 ± 0.11 0.04 ± 0.00 56.18 ± 0.39 0.96 ± 0.17 0.00 ± 0.00 1.44 ± 0.08 0.31 ± 0.02 0.02 ± 0.00

HMF

( g/L)

Isobutyric

( g/L)

Furfural

( g/L)

Nominal

storage

moisture

(%)

Day 220, 23oC Day 0

Ethanol (%

theoretical)

Acetic acid

( g/L)

Lactic acid

( g/L)

Isobutyric

( g/L)

Furfural

( g/L)

HMF

( g/L)

Pretreatment

retention

time (min)

Ethanol (%

theoretical)

Acetic acid

( g/L)

Lactic acid

( g/L)

293

Table D11: Ethanol yield and inhibitor concentrations† in pretreated unwashed stover stored at 37oC fermented with pretreatment

extract

† Concentrations are grams per Liter of fermentation broth

*Data missing

5 25 37.52 ± 0.54 0.50 ± 0.23 1.21 ± 0.19 0.85 ± 0.69 0.22 ± 0.22 0.02 ± 0.02 31.33 ± 1.89 * * * * *

5 35 40.46 ± 0.92 0.30 ± 0.01 1.56 ± 0.50 0.55 ± 0.12 0.11 ± 0.03 0.02 ± 0.00 34.02 ± 1.34 * * * * *

5 45 39.30 ± 0.18 0.45 ± 0.26 2.13 ± 0.11 1.11 ± 0.50 0.22 ± 0.09 0.03 ± 0.01 36.43 ± 2.53 * * * * *

5 55 39.97 ± 4.66 0.16 ± 0.22 2.61 ± 0.37 0.78 ± 0.20 0.15 ± 0.08 0.02 ± 0.00 32.82 ± 1.02 * * * * *

5 65 38.54 ± 0.03 0.27 ± 0.03 1.99 ± 0.04 0.00 ± 0.00 0.08 ± 0.01 0.01 ± 0.00 35.55 ± 0.36 * * * * *

5 75 35.72 ± 1.97 0.36 ± 0.08 2.35 ± 0.34 0.00 ± 0.00 0.13 ± 0.03 0.01 ± 0.00 29.38 ± 0.93 * * * * *

10 25 41.85 ± 1.74 0.28 ± 0.40 1.35 ± 0.18 0.91 ± 0.01 0.23 ± 0.02 0.02 ± 0.00 50.19 ± 0.81 * * * * *

10 35 40.33 ± 3.97 0.51 ± 0.11 1.96 ± 0.39 1.14 ± 0.14 0.29 ± 0.01 0.03 ± 0.00 41.31 ± 1.17 * * * * *

10 45 45.38 ± 3.07 0.36 ± 0.52 2.43 ± 0.43 1.31 ± 0.34 0.37 ± 0.11 0.04 ± 0.00 43.48 ± 5.37 * * * * *

10 55 42.19 ± 3.95 0.25 ± 0.36 2.59 ± 0.10 0.98 ± 0.41 0.26 ± 0.15 0.02 ± 0.01 44.61 ± 1.48 * * * * *

10 65 43.68 ± 0.49 0.38 ± 0.06 2.05 ± 0.07 1.01 ± 0.05 0.23 ± 0.00 0.02 ± 0.00 45.14 ± 1.00 * * * * *

10 75 38.06 ± 0.93 0.53 ± 0.18 2.47 ± 0.59 1.48 ± 0.44 0.28 ± 0.07 0.02 ± 0.01 34.09 ± 0.08 * * * * *

15 25 44.62 ± 4.01 0.61 ± 0.03 1.14 ± 0.24 1.32 ± 0.26 0.42 ± 0.08 0.03 ± 0.01 42.76 ± 3.79 0.58 ± 0.06 0.20 ± 0.01 1.24 ± 1.47 0.68 ± 0.05 ND

15 35 50.24 ± 0.91 0.28 ± 0.39 1.50 ± 0.35 0.91 ± 0.75 0.24 ± 0.28 0.03 ± 0.02 44.29 ± 1.29 0.62 ± 0.00 0.26 ± 0.06 1.34 ± 1.55 0.87 ± 0.07 ND

15 45 57.91 ± 4.46 0.46 ± 0.07 1.89 ± 0.03 1.36 ± 0.05 0.54 ± 0.01 0.04 ± 0.00 49.33 ± 1.06 0.60 ± 0.01 0.26 ± 0.06 1.68 ± 1.97 0.97 ± 0.04 ND

15 55 52.30 ± 2.83 0.30 ± 0.42 2.45 ± 0.19 1.14 ± 0.71 0.37 ± 0.30 0.03 ± 0.02 32.46 ± 1.96 0.62 ± 0.02 0.22 ± 0.01 1.95 ± 1.48 1.02 ± 0.01 ND

15 65 51.22 ± 1.90 0.41 ± 0.14 2.11 ± 0.35 0.60 ± 0.84 0.21 ± 0.16 0.02 ± 0.01 41.08 ± 8.79 0.55 ± 0.03 0.29 ± 0.09 2.25 ± 0.07 0.86 ± 0.04 ND

15 75 24.86 ± 3.47 0.60 ± 0.16 2.40 ± 0.19 0.91 ± 1.28 0.34 ± 0.14 0.03 ± 0.01 39.44 ± 3.49 0.64 ± 0.04 0.12 ± 0.05 0.25 ± 0.00 0.50 ± 0.00 ND

HMF

(% g/L)

HMF

(% g/L)

Day 220, Washed

Ethanol (%

theoretical)

Acetic acid

(% g/L)

Lactic acid

(% g/L)

Malic (%

g/L)

Furfural

(% g/L)

Ethanol (%

theoretical)

Acetic acid

(% g/L)

Lactic acid

(% g/L)

Isobutyric

(% g/L)

Furfural

(% g/L)

Pretreatment

retention

time (min)

Nominal

storage

moisture

(%)

Day 220, Unwashed

294

Table D12: Ethanol yield from pretreated unwashed and washed stover fermented without pretreatment extract and potential ethanol

lost with extract†

† Ethanol yield for samples fermented without extract was calculated after accounting for glucan removed in the liquid extract during pretreatment. That is

amount of glucose used in calculating expected or theoretical yield includes glucose in the pretreatment solid fraction only.

Potential ethanol calculated from this extract provides an estimate of the increase in theoretical ethanol possible in a commercial facility where the extract would

likely be included. See Equation D3 used in calculating potential ethanol as percentage of broth total, that is theoretical ethanol yield if pretreatment solids and

extracts were used. *Not part of experimental design

5 25 46.57 ± 9.09 0.02 ± 0.00 0.01 ± 0.00 3.45 ± 0.17 38.52 ± 1.70 0.02 ± 0.00 0.01 ± 0.00 4.42 ± 0.04 * * * *

5 35 40.67 ± 0.98 0.01 ± 0.00 0.01 ± 0.00 3.06 ± 0.4 40.50 ± 1.77 0.02 ± 0.00 0.01 ± 0.00 4.30 ± 0.03 * * * *

5 45 49.12 ± 5.69 0.01 ± 0.00 0.01 ± 0.00 2.15 ± 0.03 38.95 ± 0.05 0.02 ± 0.00 0.01 ± 0.00 4.25 ± 0.46 * * * *

5 55 43.34 ± 0.06 0.01 ± 0.00 0.01 ± 0.00 2.12 ± 0.04 41.29 ± 0.96 0.02 ± 0.00 0.01 ± 0.00 3.95 ± 0.50 * * * *

5 65 37.77 ± 1.60 0.01 ± 0.00 0.01 ± 0.00 2.33 ± 0.11 38.62 ± 0.83 0.02 ± 0.00 0.01 ± 0.00 3.15 ± 0.46 * * * *

5 75 30.31 ± 16.98 0.01 ± 0.00 0.01 ± 0.00 2.53 ± 0.07 42.55 ± 1.25 0.02 ± 0.00 0.01 ± 0.00 5.01 ± 0.41 * * * *

10 25 47.09 ± 1.01 0.02 ± 0.00 0.01 ± 0.00 3.12 ± 0.37 43.62 ± 1.64 0.02 ± 0.00 0.01 ± 0.00 4.63 ± 0.01 * * * *

10 35 49.65 ± 2.86 0.02 ± 0.00 0.01 ± 0.00 3.17 ± 0.14 46.44 ± 2.56 0.02 ± 0.00 0.01 ± 0.00 4.87 ± 0.38 * * * *

10 45 49.55 ± 0.35 0.01 ± 0.00 0.01 ± 0.00 2.80 ± 0.23 47.01 ± 2.60 0.02 ± 0.00 0.01 ± 0.00 4.63 ± 0.74 * * * *

10 55 47.06 ± 2.13 0.01 ± 0.00 0.01 ± 0.00 2.75 ± 0.08 41.86 ± 8.64 0.02 ± 0.00 0.01 ± 0.00 4.35 ± 0.22 * * * *

10 65 47.13 ± 0.50 0.01 ± 0.00 0.01 ± 0.00 2.24 ± 0.42 47.47 ± 1.40 0.02 ± 0.00 0.01 ± 0.00 3.69 ± 0.19 * * * *

10 75 48.17 ± 2.95 0.01 ± 0.00 0.01 ± 0.00 2.41 ± 0.11 46.33 ± 0.48 0.02 ± 0.00 0.01 ± 0.00 4.96 ± 0.31 * * * *

15 25 51.71 ± 0.88 0.02 ± 0.00 0.01 ± 0.00 3.40 ± 0.17 48.89 ± 1.85 0.02 ± 0.00 0.01 ± 0.00 4.53 ± 0.97 45.52 ± 4.72 0.02 ± 0.00 0.01 ± 0.00 3.63 ± 0.30

15 35 50.30 ± 7.00 0.02 ± 0.00 0.01 ± 0.00 3.29 ± 0.30 50.17 ± 1.82 0.02 ± 0.00 0.01 ± 0.00 4.78 ± 0.01 50.55 ± 2.89 0.01 ± 0.00 0.01 ± 0.00 2.94 ± 0.49

15 45 51.68 ± 4.27 0.01 ± 0.00 0.01 ± 0.00 2.61 ± 0.04 51.53 ± 0.73 0.02 ± 0.00 0.01 ± 0.00 4.70 ± 0.36 42.79 ± 13.56 0.02 ± 0.00 0.01 ± 0.00 3.53 ± 0.06

15 55 46.44 ± 5.64 0.01 ± 0.00 0.01 ± 0.00 2.59 ± 0.13 50.03 ± 0.91 0.02 ± 0.00 0.01 ± 0.00 3.89 ± 0.25 50.18 ± 3.05 0.02 ± 0.00 0.01 ± 0.00 3.62 ± 0.15

15 65 48.50 ± 9.69 0.01 ± 0.01 0.01 ± 0.01 1.13 ± 1.37 44.21 ± 8.36 0.02 ± 0.00 0.01 ± 0.00 4.04 ± 0.78 44.12 ± 9.97 0.02 ± 0.00 0.01 ± 0.00 3.61 ± 0.37

15 75 53.22 ± 2.79 0.01 ± 0.00 0.01 ± 0.00 2.81 ± 0.09 49.82 ± 3.02 0.02 ± 0.00 0.02 ± 0.01 5.08 ± 0.93 40.76 ± 1.05 0.02 ± 0.00 0.01 ± 0.00 3.90 ± 0.02

Day 220, 37oC (Washed)Day 0

Ethanol (%

theoretical)

Glucan in

extract (g)

potential

ethanol from

extract (%

broth total)

Ethanol (%

theoretical)

potential

ethanol from

extract (%

broth total)

potential

ethanol from

extract (g)

Glucan in

extract (g)

potential

ethanol from

extract (g)

potential

ethanol from

extract (%

broth total)

Ethanol (%

theoretical)

Glucan in

extract (g)

potential

ethanol from

extract (g)

Pretreatment

retention

time (min)

Nominal

storage

moisture

(%)

Day 220, 23oC

295

Equation D2:

Where: EtOHactual = actual amount of ethanol in fermentation broth as measured using the YSI

2700 SELECT (g)

Gextract = Amount of glucan in pretreatment extract (g)

(When samples are fermented without extract, Gextract = 0)

Gsolid = Amount of glucan in pretreated dry matter that is fermented (g)

= [(% glucan before pretreatment ×dry mass of feedstock before

pretreatment (g)) - Amount of glucan in pretreatment extract (g)]

0.9 = (molar mass of glucan/molar mass of glucose) is to convert glucan to glucose

0.51 = conversion factor of 1 gram of glucose to ethanol from stoichiometric (used as

basis for theoretical yield)

Equation D3:

Where: Gextract and Gsolid are defined as in Equation D2

296


during pretreatment of unwashed unensiled stover†

Ethanol (%) Furfural, g/L HMF, g/L Acetic, g/L

Lactic, g/L

Furfural, g/L 0.852

0.000

HMF, g/L 0.657 0.871

0.000 0.000

Acetic, g/L 0.659 0.726 0.432

0.000 0.000 0.009

Lactic, g/L 0.163 0.293 0.511 -0.008

0.341 0.082 0.001 0.965

Isobutyric, g/L 0.826 0.916 0.680 0.824

0.164

0.000 0.000 0.000 0.000

0.340

Cell Contents: Pearson correlation

P-Value

† Concentration of potential inhibitors is grams per liter of fermentation broth

297


during pretreatment of unwashed ensiled stover stored for 220 days†

Ethanol (%) Furfural, g/L HMF, g/L Acetic,

g/L Lactic, g/L

Furfural, g/L 0.826

0.000

HMF, g/L 0.750 0.904

0.000 0.000

Acetic, g/L 0.247 0.343 0.339

0.146 0.040 0.043

Lactic, g/L 0.330 0.315 0.195 -0.115

0.049 0.062 0.255 0.505

Isobutyric, g/L 0.399 0.562 0.627 0.208

0.271

0.016 0.000 0.000 0.223

0.110

Cell Contents: Pearson correlation

P-Value

† Concentration of potential inhibitors is grams per liter of fermentation broth

298

Figure D2: Regression relationships between ethanol yield and concentration of potential inhibitors in the fermentation broth of

unensiled stover. Showing only potential inhibitors fromTable D12 with some correlation with ethanol.

299

Figure D3: Regression relationships between ethanol yield and concentration of potential inhibitors in the fermentation broth of

ensiled stover. Showing only potential inhibitors from Table D13 with some correlation with ethanol.

300

APPENDIX E: Post storage handling and processing of wet stored stover

(Chapter 6)

This appendix contains supplementary materials to Chapter 6. This includes an experimental

design and analytical process diagram, experimental data on pretreatment outcomes (organic

acids, pH, glucan and xylan removal, and inhibitors) as well as ethanol yields.

301

Figure E1: Experimental design and analysis for examining impact of drying on ethanol yield

302

Table E1: pH of corn stover before and after liquid hot water pretreatment at 190oC

Pretreatment

time (min) Moisture content Storage pH

Dry, Unwashed

Dry, Washed

"As is", Unwashed

"As is", Washed

Day 220

5 25 4.90 ± 0.05 4.50 ± 0.05 4.41 ± 0.05 4.47 ± 0.04 4.45 ± 0.17

5 35 4.46 ± 0.03 4.45 ± 0.06 4.35 ± 0.04 4.34 ± 0.00 4.23 ± 0.02

5 45 4.24 ± 0.02 4.30 ± 0.09 4.24 ± 0.01 4.24 ± 0.00 4.29 ± 0.18

5 55 4.11 ± 0.02 4.28 ± 0.01 4.20 ± 0.01 4.15 ± 0.06 4.14 ± 0.01

5 65 4.08 ± 0.01 4.16 ± 0.01 4.11 ± 0.02 4.06 ± 0.12 4.06 ± 0.03

5 75 4.75 ± 0.03 4.69 ± 0.03 4.36 ± 0.06 4.18 ± 0.01 4.18 ± 0.01

10 25 4.90 ± 0.05 4.34 ± 0.03 4.15 ± 0.02 4.28 ± 0.05 4.24 ± 0.10

10 35 4.46 ± 0.03 4.30 ± 0.01 4.08 ± 0.04 4.2 ± 0.01 4.05 ± 0.00

10 45 4.24 ± 0.02 4.18 ± 0.00 4.05 ± 0.00 4.13 ± 0.01 4.13 ± 0.07

10 55 4.11 ± 0.02 4.15 ± 0.04 3.98 ± 0.02 4.06 ± 0.02 4.01 ± 0.02

10 65 4.08 ± 0.01 4.03 ± 0.02 3.95 ± 0.02 4.05 ± 0.01 4.01 ± 0.02

10 75 4.75 ± 0.03 4.51 ± 0.03 4.09 ± 0.05 4.03 ± 0.01 4.05 ± 0.06

15 25 4.90 ± 0.05 4.23 ± 0.08 4.00 ± 0.01 4.13 ± 0.06 4.08 ± 0.08

15 35 4.46 ± 0.03 4.09 ± 0.01 3.96 ± 0.01 4.07 ± 0.00 3.94 ± 0.01

15 45 4.24 ± 0.02 3.96 ± 0.00 3.95 ± 0.06 4.08 ± 0.06 3.98 ± 0.08

15 55 4.11 ± 0.02 4.01 ± 0.01 3.86 ± 0.01 3.95 ± 0.00 3.90 ± 0.01

15 65 4.08 ± 0.01 3.90 ± 0.03 3.82 ± 0.02 3.96 ± 0.04 3.85 ± 0.01

15 75 4.75 ± 0.03 4.37 ± 0.08 3.96 ± 0.00 4.08 ± 0.03 3.93 ± 0.00

Day 0

5 25 6.69 ± 0.02 4.64 ± 0.01 ˣ 4.58 ± 0.05 4.62 ± 0.17

5 35 6.69 ± 0.11 4.62 ± 0.01 ˣ 4.59 ± 0.00 4.44 ± 0.01

5 45 6.65 ± 0.02 4.62 ± 0.04 ˣ 4.66 ± 0.28 4.50 ± 0.11

5 55 6.71 ± 0.03 4.64 ± 0.03 ˣ 4.76 ± 0.11 4.38 ± 0.04

5 65 6.54 ± 0.08 4.62 ± 0.05 ˣ 4.65 ± 0.08 4.35 ± 0.01

5 75 6.73 ± 0.05 4.72 ± 0.02 ˣ 4.40 ± 0.04 4.44 ± 0.06

10 25 6.69 ± 0.02 4.43 ± 0.05 ˣ 4.41 ± 0.01 4.41 ± 0.16

10 35 6.69 ± 0.11 4.37 ± 0.06 ˣ 4.37 ± 0.06 4.22 ± 0.04

10 45 6.65 ± 0.02 4.40 ± 0.04 ˣ 4.41 ± 0.23 4.25 ± 0.06

10 55 6.71 ± 0.03 4.44 ± 0.02 ˣ 4.53 ± 0.08 4.22 ± 0.01

10 65 6.54 ± 0.08 4.41 ± 0.06 ˣ 4.50 ± 0.04 4.18 ± 0.01

10 75 6.73 ± 0.05 4.51 ± 0.02 ˣ 4.20 ± 0.01 4.19 ± 0.03

15 25 6.69 ± 0.02 4.26 ± 0.01 ˣ 4.27 ± 0.04 4.25 ± 0.08

15 35 6.69 ± 0.11 4.26 ± 0.01 ˣ 4.28 ± 0.06 4.12 ± 0.01

15 45 6.65 ± 0.02 4.24 ± 0.01 ˣ 4.25 ± 0.21 4.11 ± 0.01

15 55 6.71 ± 0.03 4.29 ± 0.05 ˣ 4.41 ± 0.06 4.15 ± 0.04

15 65 6.54 ± 0.08 4.28 ± 0.00 ˣ 4.36 ± 0.11 4.06 ± 0.03

15 75 6.73 ± 0.05 4.30 ± 0.03 ˣ 4.11 ± 0.02 4.12 ± 0.03

303

Table E2: Organic acid profile of dried and “as is” stover before pretreatment

Lactic

(% DM)

Acetic

(% DM)

Isobutyric

(% DM)

Butyric

(% DM)

Tartaric

(% DM)

Malic

(% DM)

Pyruvic

(% DM)

Lactic

(% DM)

Acetic

(% DM)

Isobutyric

(% DM)

Butyric

(% DM)

Tartaric

(% DM)

Malic

(% DM)

Pyruvic

(% DM)

15 0.00 ± 0.00 0.00 ± 0.00 1.72 ± 0.22 0.00 ± 0.00 0.98 ± 0.01 0.91 ± 0.01 2.20 ± 0.03 0.00 ± 0.00 0.00 ± 0.00 1.80 ± 0.22 0.00 ± 0.00 0.86 ± 0.13 1.11 ± 0.23 2.21 ± 0.07

25 0.00 ± 0.00 0.00 ± 0.00 0.82 ± 0.05 0.00 ± 0.00 0.41 ± 0.02 0.00 ± 0.00 0.62 ± 0.62 0.00 ± 0.00 0.00 ± 0.00 0.79 ± 0.35 0.00 ± 0.00 1.88 ± 0.34 0.05 ± 0.05 2.8 ± 0.55

35 0.00 ± 0.00 0.00 ± 0.00 0.80 ± 0.03 0.00 ± 0.00 0.16 ± 0.02 0.30 ± 0.04 0.16 ± 0.16 0.00 ± 0.00 0.00 ± 0.00 0.93 ± 0.31 0.00 ± 0.00 2.54 ± 0.65 0.05 ± 0.05 2.35 ± 0.21

45 0.00 ± 0.00 0.00 ± 0.00 1.03 ± 0.08 0.00 ± 0.00 0.00 ± 0.00 0.38 ± 0.04 0.31 ± 0.31 0.00 ± 0.00 0.00 ± 0.00 0.63 ± 0.01 0.00 ± 0.00 1.73 ± 0.05 0.05 ± 0.05 2.11 ± 0.01

55 0.00 ± 0.00 0.00 ± 0.00 2.95 ± 2.14 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.57 ± 0.18 0.00 ± 0.00 1.13 ± 0.76 0.00 ± 0.00 1.99 ± 0.11

65 0.00 ± 0.00 0.00 ± 0.00 5.15 ± 0.47 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 2.61 ± 2.61 0.23 ± 0.23 0.00 ± 0.00 0.74 ± 0.64 0.00 ± 0.00 1.03 ± 0.24

75 0.00 ± 0.00 0.00 ± 0.00 6.83 ± 0.44 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.03 ± 0.00 0.00 ± 0.00 0.62 ± 0.14 0.16 ± 0.16 0.00 ± 0.00 0.08 ± 0.08 0.00 ± 0.00 0.80 ± 0.40

15 0.00 ± 0.00 0.00 ± 0.00 0.02 ± 0.03 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.05 ± 0.05 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00

25 0.26 ± 0.46 0.14 ± 0.24 0.04 ± 0.07 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00

35 1.94 ± 0.13 0.63 ± 0.02 0.2 ± 0.02 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00

45 2.9 ± 0.16 0.75 ± 0.06 0.27 ± 0.01 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.05 ± 0.09 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00

55 2.95 ± 0.4 1.14 ± 0.19 0.34 ± 0.22 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00

65 3.03 ± 1.76 1.24 ± 0.21 0.16 ± 0.27 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00

75 0.00 ± 0.00 2.74 ± 0.73 1.28 ± 1.06 2.57 ± 0.58 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00

Day 220 Day 0

Dried

"As is"

Moisture

content

(%)

304

Table E3: Organic acid profile of unwashed Day 220 samples after pretreatment

Pretreatment

time (min)

Storage

Moisture

(%)

Tartaric

(% DM)

Malic

(% DM)

Lactic

(% DM)

Acetic

(% DM)

Formic

(% DM)

Propionic

(% DM)

Isobutyric

(% DM)

Butyric

(% DM)

Pyruvic

(% DM)

5 25 0.85 ± 0.83 0.15 ± 0.14 1.73 ± 0.28 0.71 ± 0.33 0.21 ± 0.14 n.d 1.22 ± 0.99 n.d 0.23 ± 0.01

5 35 0.26 ± 0.02 0.16 ± 0.01 2.27 ± 0.72 0.44 ± 0.02 0.12 ± 0.02 n.d 0.80 ± 0.17 n.d 0.20 ± 0.01

5 45 0.16 ± 0.22 1.79 ± 2.42 3.08 ± 0.15 0.65 ± 0.38 0.21 ± 0.10 n.d 1.60 ± 0.72 n.d 0.33 ± 0.20

5 55 0.09 ± 0.13 1.18 ± 1.48 3.8 ± 0.53 0.23 ± 0.32 0.14 ± 0.04 n.d 1.13 ± 0.28 n.d 0.30 ± 0.17

5 65 0.01 ± 0.01 0.04 ± 0.00 2.89 ± 0.06 0.40 ± 0.04 0.11 ± 0.01 n.d n.d n.d 0.12 ± 0.00

5 75 0.04 ± 0.03 0.09 ± 0.01 3.45 ± 0.48 0.53 ± 0.11 0.15 ± 0.04 n.d n.d n.d 0.14 ± 0.05

10 25 0.27 ± 0.38 0.14 ± 0.03 1.91 ± 0.26 0.40 ± 0.57 0.21 ± 0.00 n.d 1.29 ± 0.01 n.d 0.22 ± 0.06

10 35 0.27 ± 0.09 0.19 ± 0.04 2.83 ± 0.59 0.74 ± 0.16 0.25 ± 0.05 n.d 1.65 ± 0.22 n.d 0.22 ± 0.03

10 45 0.20 ± 0.01 0.39 ± 0.12 3.52 ± 0.61 0.53 ± 0.74 0.27 ± 0.09 n.d 1.89 ± 0.48 n.d 0.24 ± 0.01

10 55 n.d 1.33 ± 1.49 3.77 ± 0.14 0.37 ± 0.52 0.18 ± 0.09 n.d 1.42 ± 0.59 n.d 0.35 ± 0.28

10 65 0.01 ± 0.01 1.07 ± 1.18 2.96 ± 0.09 0.55 ± 0.08 0.10 ± 0.14 n.d 1.46 ± 0.07 n.d 0.26 ± 0.21

10 75 0.01 ± 0.02 0.62 ± 0.40 3.61 ± 0.86 0.78 ± 0.26 0.29 ± 0.16 n.d 2.15 ± 0.64 n.d 0.15 ± 0.03

15 25 0.22 ± 0.21 0.21 ± 0.02 1.63 ± 0.35 0.87 ± 0.06 0.28 ± 0.05 n.d 1.88 ± 0.39 n.d 0.23 ± 0.05

15 35 0.13 ± 0.12 0.29 ± 0.34 2.16 ± 0.5 0.40 ± 0.56 0.21 ± 0.13 n.d 1.31 ± 1.08 n.d 0.19 ± 0.02

15 45 0.30 ± 0.19 0.68 ± 0.53 2.72 ± 0.03 0.66 ± 0.10 0.24 ± 0.00 n.d 1.96 ± 0.07 n.d 0.17 ± 0.01

15 55 0.10 ± 0.14 0.75 ± 0.42 3.60 ± 0.20 0.43 ± 0.61 0.20 ± 0.13 n.d 1.66 ± 1.01 n.d 0.18 ± 0.05

15 65 0.01 ± 0.01 0.29 ± 0.17 3.05 ± 0.51 0.59 ± 0.21 0.22 ± 0.12 n.d 0.86 ± 1.22 n.d 0.16 ± 0.01

15 75 n.d 0.69 ± 0.63 3.5 ± 0.28 0.87 ± 0.23 0.31 ± 0.02 n.d 1.32 ± 1.87 n.d 0.18 ± 0.03

5 25 n.d 0.10 ± 0.01 1.36 ± 0.14 1.50 ± 0.09 0.32 ± 0.02 n.d 1.39 ± 0.02 n.d 0.12 ± 0.01

5 35 n.d 0.27 ± 0.00 0.96 ± 1.36 1.92 ± 0.04 0.33 ± 0.00 n.d 2.44 ± 0.12 n.d 0.23 ± 0.03

5 45 n.d 0.21 ± 0.15 2.68 ± 0.40 2.38 ± 0.27 0.31 ± 0.01 n.d 2.69 ± 0.25 n.d 0.22 ± 0.05

5 55 n.d 0.10 ± 0.02 1.61 ± 2.28 2.98 ± 0.05 0.23 ± 0.01 n.d 2.05 ± 0.19 n.d 0.19 ± 0.00

5 65 n.d 0.08 ± 0.01 1.79 ± 2.53 2.93 ± 0.08 0.24 ± 0.01 n.d 1.87 ± 0.12 n.d 0.23 ± 0.00

5 75 n.d 0.01 ± 0.02 n.d 7.11 ± 0.03 0.31 ± 0.02 0.15 ± 0.04 n.d 4.55 ± 0.08 0.05 ± 0.06

10 25 n.d 0.22 ± 0.07 1.49 ± 0.18 1.84 ± 0.38 0.50 ± 0.02 n.d 2.45 ± 0.03 n.d 0.19 ± 0.01

10 35 n.d 0.24 ± 0.25 0.34 ± 0.48 1.53 ± 1.05 0.35 ± 0.23 n.d 2.58 ± 1.95 n.d 0.18 ± 0.14

10 45 n.d 0.28 ± 0.04 5.61 ± 3.10 3.75 ± 0.36 0.52 ± 0.10 n.d 4.10 ± 0.53 n.d 1.11 ± 1.19

10 55 n.d 0.19 ± 0.01 3.25 ± 0.04 3.55 ± 0.14 0.38 ± 0.05 n.d 2.90 ± 0.27 n.d 0.25 ± 0.01

10 65 n.d 0.15 ± 0.04 2.03 ± 2.87 3.35 ± 0.17 0.40 ± 0.01 n.d 2.76 ± 0.27 n.d 0.28 ± 0.01

10 75 n.d 0.15 ± 0.01 n.d 8.13 ± 0.03 0.47 ± 0.04 0.18 ± 0.07 n.d 4.22 ± 0.33 0.67 ± 0.72

15 25 n.d 0.23 ± 0.00 0.88 ± 1.24 1.4 ± 1.36 0.54 ± 0.18 n.d 2.91 ± 1.29 n.d 0.22 ± 0.01

15 35 n.d 0.31 ± 0.13 0.86 ± 1.22 2.84 ± 0.38 0.62 ± 0.17 n.d 4.99 ± 1.71 n.d 0.28 ± 0.05

15 45 n.d 2.38 ± 2.32 n.d 3.19 ± 0.08 1.33 ± 0.91 n.d 4.24 ± 2.31 n.d 0.69 ± 0.45

15 55 n.d 0.33 ± 0.01 n.d 4.16 ± 0.09 0.65 ± 0.01 n.d 5.02 ± 0.02 n.d 0.29 ± 0.00

15 65 n.d 0.21 ± 0.12 2.91 ± 1.26 2.82 ± 1.17 0.42 ± 0.19 n.d 2.80 ± 1.21 n.d 0.25 ± 0.13

15 75 n.d 0.17 ± 0.04 0.49 ± 0.47 2.52 ± 0.12 0.56 ± 0.06 n.d 2.58 ± 0.26 n.d 0.34 ± 0.03

Dried

"As is"

305

Table E4: Organic acid profiled of unwashed Day 0 samples after pretreatment

Pretreatment

time (min)

Storage

Moisture

(%)

Tartaric

(% DM)

Malic

(% DM)

Lactic

(% DM)

Acetic

(% DM)

Formic

(% DM)

Propionic

(% DM)

Isobutyric

(% DM)

Butyric

(% DM)

Pyruvic

(% DM)

5 25 1.09 ± 1.30 0.18 ± 0.01 n.d 0.82 ± 0.27 0.17 ± 0.00 n.d 0.75 ± 0.08 n.d 0.30 ± 0.42

5 35 0.92 ± 0.08 0.17 ± 0.01 n.d 1.00 ± 0.08 0.18 ± 0.00 n.d 0.48 ± 0.06 n.d 0.46 ± 0.01

5 45 0.12 ± 0.03 0.21 ± 0.01 0.70 ± 0.48 0.75 ± 0.10 0.19 ± 0.01 n.d 0.74 ± 0.43 n.d 0.30 ± 0.04

5 55 1.35 ± 0.04 0.12 ± 0.01 0.53 ± 0.31 0.85 ± 0.14 0.17 ± 0.01 n.d 0.89 ± 0.01 n.d 0.31 ± 0.09

5 65 3.20 ± 0.37 0.06 ± 0.01 n.d 0.68 ± 0.64 0.16 ± 0.01 n.d 0.83 ± 0.00 n.d 1.93 ± 0.17

5 75 3.27 ± 0.15 0.06 ± 0.01 n.d 1.49 ± 0.11 0.49 ± 0.45 n.d 0.91 ± 0.05 n.d 1.05 ± 0.04

10 25 0.56 ± 0.59 0.23 ± 0.01 n.d 0.99 ± 0.19 0.26 ± 0.00 n.d 1.21 ± 0.23 n.d 0.47 ± 0.14

10 35 0.68 ± 0.40 0.25 ± 0.03 0.55 ± 0.78 0.95 ± 0.02 0.26 ± 0.01 n.d 1.59 ± 0.12 n.d 0.47 ± 0.06

10 45 0.06 ± 0.08 0.22 ± 0.08 0.42 ± 0.39 1.07 ± 0.28 0.29 ± 0.01 n.d 1.34 ± 0.64 n.d 0.28 ± 0.04

10 55 n.d 0.19 ± 0.01 0.69 ± 0.01 0.94 ± 0.08 0.27 ± 0.01 n.d 1.53 ± 0.06 n.d 0.27 ± 0.01

10 65 1.67 ± 2.27 1.69 ± 2.26 n.d 0.64 ± 0.52 1.39 ± 1.09 n.d 2.71 ± 1.64 n.d 2.36 ± 0.37

10 75 0.29 ± 0.30 1.53 ± 1.96 n.d 1.68 ± 0.68 0.86 ± 0.78 n.d 1.63 ± 0.13 n.d 0.77 ± 0.85

15 25 0.68 ± 0.77 0.32 ± 0.01 n.d 1.11 ± 0.27 0.22 ± 0.18 n.d 2.14 ± 0.21 n.d 0.41 ± 0.01

15 35 0.54 ± 0.17 0.41 ± 0.02 1.12 ± 0.13 1.29 ± 0.16 0.45 ± 0.04 n.d 2.59 ± 0.39 n.d 0.50 ± 0.20

15 45 0.31 ± 0.26 0.64 ± 0.33 0.55 ± 0.28 1.43 ± 0.40 0.28 ± 0.02 n.d 2.70± 0.06 n.d 0.38 ± 0.00

15 55 0.60 ± 0.00 0.29 ± 0.01 0.59 ± 0.01 1.40 ± 0.20 0.37 ± 0.01 n.d 2.28 ± 0.04 n.d 0.3 ± 0.00

15 65 0.63 ± 0.01 0.19 ± 0.01 n.d 0.87 ± 0.74 0.40 ± 0.01 n.d 2.46 ± 0.06 n.d 0.29 ± 0.01

15 75 0.59 ± 0.01 0.20 ± 0.01 n.d 1.38 ± 0.03 0.4 ± 0.00 n.d 2.48 ± 0.04 n.d 0.24 ± 0.01

5 25 n.d 0.11 ± 0.01 0.67 ± 0.95 0.83 ± 1.17 0.33 ± 0.11 n.d 1.51 ± 0.16 n.d 0.14 ± 0.03

5 35 n.d 0.10 ± 0.01 n.d 0.11 ± 0.15 0.24 ± 0.02 n.d 1.04 ± 0.46 n.d 0.44 ± 0.39

5 45 n.d 0.58 ± 0.66 1.32 ± 1.87 1.98 ± 0.68 0.29 ± 0.06 n.d 1.94 ± 1.13 n.d 0.27 ± 0.12

5 55 n.d n.d 0.16 ± 0.23 1.40 ± 0.13 0.26 ± 0.07 n.d 1.05 ± 0.58 n.d 0.08 ± 0.03

5 65 n.d 0.05 ± 0.00 n.d 1.22 ± 0.00 0.17 ± 0.01 n.d 0.71 ± 0.05 n.d 0.11 ± 0.01

5 75 n.d n.d n.d 6.71 ± 0.68 0.31 ± 0.05 0.16 ± 0.00 n.d 4.36 ± 0.24 0.18 ± 0.13

10 25 n.d 0.15 ± 0.04 n.d 1.1 ± 0.09 0.37 ± 0.07 n.d 1.80 ± 0.04 n.d 0.20 ± 0.01

10 35 n.d 0.18 ± 0.09 n.d 1.27 ± 0.13 0.92 ± 0.48 n.d 2.08 ± 0.24 n.d 0.55 ± 0.34

10 45 n.d 1.42 ± 0.78 n.d 2.53 ± 0.91 1.04 ± 0.97 n.d 1.44 ± 0.02 n.d 0.59 ± 0.44

10 55 n.d 0.88 ± 0.15 n.d 1.78 ± 0.05 0.32 ± 0.01 n.d 1.42 ± 0.01 n.d 0.33 ± 0.03

10 65 n.d 0.31 ± 0.34 n.d 1.43 ± 0.09 0.26 ± 0 n.d 1.07 ± 0.03 n.d 0.19 ± 0.03

10 75 n.d 1.56 ± 1.87 0.04 ± 0.05 2.84 ± 0.77 1.28 ± 0.78 n.d 1.69 ± 0.23 n.d 0.81 ± 0.36

15 25 n.d 0.28 ± 0.02 n.d 1.43 ± 0.10 0.42 ± 0.13 n.d 2.61 ± 0.50 n.d 0.30 ± 0.05

15 35 n.d 0.26 ± 0.07 0.56 ± 0.79 1.60 ± 0.16 0.52 ± 0.02 n.d 2.99 ± 0.14 n.d 0.34 ± 0.04

15 45 n.d 0.88 ± 0.94 0.16 ± 0.23 2.36 ± 0.37 0.92 ± 0.63 n.d 2.52 ± 0.62 n.d 0.66 ± 0.53

15 55 n.d 0.1 ± 0.14 0.32 ± 0.01 2.48 ± 0.66 1.15 ± 0.91 n.d 2.63 ± 0.10 n.d n.d

15 65 n.d 0.11 ± 0.03 n.d 1.81 ± 0.40 0.36 ± 0 n.d 1.73 ± 0.13 n.d 0.24 ± 0.02

15 75 n.d 0.18 ± 0.01 n.d 2.64 ± 0.14 0.53 ± 0.01 n.d 2.29 ± 0.13 n.d 0.18 ± 0.25

Dried

"As is"

306

Table E5: Organic acid profile of washed “as is” samples after pretreatment

Storage

duration

(days)

Pretreatment

time (min)

Storage

Moisture

(%)

Tartaric

(% DM)

Malic

(% DM)

Lactic

(% DM)

Acetic

(% DM)

Formic

(% DM)

Propionic

(% DM)

Isobutyric

(% DM)

Butyric

(% DM)

Pyruvic

(% DM)

5 25 n.d 0.13 ± 0.08 n.d 0.69 ± 0.11 0.18 ± 0.04 n.d n.d n.d 0.09 ± 0.02

5 35 n.d 0.12 ± 0.09 n.d 0.79 ± 0.16 0.22 ± 0.14 n.d n.d n.d 0.18 ± 0.13

5 45 n.d 0.15 ± 0.03 n.d 0.77 ± 0.26 0.14 ± 0.02 n.d n.d n.d 0.08 ± 0.00

5 55 n.d 0.12 ± 0.03 n.d 0.64 ± 0.02 0.25 ± 0.04 n.d n.d n.d 0.09 ± 0.00

5 65 n.d 0.17 ± 0.04 n.d 0.66 ± 0.04 0.35 ± 0.01 n.d n.d n.d 0.11 ± 0.02

5 75 n.d 0.12 ± 0.03 n.d 0.76 ± 0.05 0.33 ± 0.05 n.d n.d n.d 0.12 ± 0.02

10 25 n.d 0.18 ± 0.01 n.d 0.99 ± 0.16 0.40 ± 0.15 n.d n.d n.d 0.13 ± 0.04

10 35 n.d 0.15 ± 0.08 n.d 1.06 ± 0.15 0.29 ± 0.25 n.d n.d n.d 0.11 ± 0.04

10 45 n.d 0.14 ± 0.11 n.d 0.93 ± 0.06 0.31 ± 0.11 n.d n.d n.d 0.10 ± 0.02

10 55 n.d 0.16 ± 0.12 n.d 1.04 ± 0.03 0.33 ± 0.13 n.d n.d n.d 0.11 ± 0.01

10 65 n.d 0.19 ± 0.05 n.d 1.24 ± 0.36 0.28 ± 0.17 n.d n.d n.d 0.23 ± 0.17

10 75 n.d 0.18 ± 0.19 n.d 1.10 ± 0.62 0.30 ± 0.28 n.d n.d n.d 0.10 ± 0.05

15 25 n.d 0.45 ± 0.11 n.d 1.41 ± 0.15 0.65 ± 0.17 n.d n.d n.d 0.20 ± 0.05

15 35 n.d 0.43 ± 0.01 n.d 1.55 ± 0.07 0.59 ± 0.01 n.d n.d n.d 0.14 ± 0.02

15 45 n.d 0.43 ± 0.00 n.d 1.28 ± 0.00 0.47 ± 0.00 n.d n.d n.d 0.13 ± 0.00

15 55 n.d 0.42 ± 0.02 n.d 1.43 ± 0.07 0.55 ± 0.07 n.d n.d n.d 0.14 ± 0.01

15 65 n.d 1.07 ± 0.10 n.d 1.62 ± 0.02 0.61 ± 0.09 n.d n.d n.d 0.19 ± 0.03

15 75 n.d 0.31 ± 0.13 n.d 1.63 ± 0.16 0.56 ± 0.14 n.d n.d n.d 0.14 ± 0.03

5 25 n.d 0.06 ± 0.02 n.d 0.74 ± 0.13 0.11 ± 0.01 n.d n.d n.d 0.11 ± 0.06

5 35 n.d 0.09 ± 0.00 n.d 0.70 ± 0.12 0.16 ± 0.09 n.d n.d n.d 0.08 ± 0.01

5 45 n.d 0.08 ± 0.02 n.d 0.69 ± 0.21 0.23 ± 0.05 n.d n.d n.d 0.18 ± 0.12

5 55 n.d 0.10 ± 0.06 n.d 0.88 ± 0.15 0.22 ± 0.03 n.d n.d n.d 0.08 ± 0.01

5 65 n.d 0.12 ± 0.05 n.d 0.99 ± 0.05 0.25 ± 0.05 n.d n.d n.d 0.10 ± 0.02

5 75 n.d 0.07 ± 0.02 n.d 0.81 ± 0.10 0.27 ± 0.03 n.d n.d n.d 0.09 ± 0.00

10 25 n.d 0.12 ± 0.06 n.d 1.04 ± 0.06 0.32 ± 0.06 n.d n.d n.d 0.14 ± 0.04

10 35 n.d 0.16 ± 0.09 n.d 0.99 ± 0.32 0.41 ± 0.00 n.d n.d n.d 0.13 ± 0.02

10 45 n.d 0.10 ± 0.00 n.d 0.92 ± 0.39 0.31 ± 0.11 n.d n.d n.d 0.11 ± 0.03

10 55 n.d 0.11 ± 0.02 n.d 0.93 ± 0.52 0.31 ± 0.09 n.d n.d n.d 0.12 ± 0.01

10 65 n.d 0.06 ± 0.01 n.d 0.82 ± 0.35 0.26 ± 0.03 n.d n.d n.d 0.10 ± 0.04

10 75 n.d 0.10 ± 0.03 n.d 1.10 ± 0.48 0.27 ± 0.15 n.d n.d n.d 0.12 ± 0.00

15 25 n.d 0.16 ± 0.05 n.d 1.12 ± 0.16 0.40 ± 0.14 n.d n.d n.d 0.25 ± 0.22

15 35 n.d 0.25 ± 0.06 n.d 1.53 ± 0.01 0.52 ± 0.04 n.d n.d n.d 0.14 ± 0.02

15 45 n.d 0.23 ± 0.03 n.d 1.61 ± 0.12 0.50 ± 0.08 n.d n.d n.d 0.12 ± 0.02

15 55 n.d 0.20 ± 0.02 n.d 1.38 ± 0.22 0.51 ± 0.15 n.d n.d n.d 0.14 ± 0.03

15 65 n.d 0.23 ± 0.01 n.d 1.54 ± 0.04 0.52 ± 0.03 n.d n.d n.d 0.13 ± 0.01

15 75 n.d 0.20 ± 0.01 n.d 1.43 ± 0.06 0.50 ± 0.08 n.d n.d n.d 0.21 ± 0.11

Day 220

Day 0

307

Table E6: Organic acid profile of Day 0 stover after pretreatment with acids listed by decreasing inhibitory potentialₒ

ₒ Sowing mean values

Dry samples were dried at 55oC before pretreatment; Washed samples were washed before pretreatment. See text for detailed

Protocol * lactic acid can be metabolized by most fungi

? Pyruvic acid provides energy for most living cells and is a key intermediate for most metabolic processes. Like most things,

excess pyruvic acid could have negative impact. Pyruvic acid inhibitors are usually derivative compounds

√ √ Higher antimicrobial activity relative to other microbial group

High (11.7% DM) Low (1.0% DM)High (6.7% DM) Low (0.1% DM)

25 35 45 55 65 75 25 35 45 55 65 75 25 35 45 55 65 75

√ √√ 5 0.00 0.00 0.00 0.00 0.00 0.16 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

10 0.00 0.00 0.00 0.00 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

15 0.00 0.00 0.00 0.00 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

√√ √ 5 0.33 0.24 0.29 0.26 0.17 0.31 0.11 0.16 0.23 0.22 0.25 0.27 0.17 0.18 0.19 0.17 0.16 0.49

10 0.37 0.92 1.04 0.32 0.26 1.28 0.32 0.41 0.31 0.31 0.26 0.27 0.26 0.26 0.29 0.27 1.39 0.86

15 0.42 0.52 0.92 1.15 0.36 0.53 0.40 0.52 0.50 0.51 0.52 0.50 0.22 0.45 0.28 0.37 0.40 0.40

√ √ 5 0.00 0.00 0.00 0.00 0.00 4.36 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

10 0.00 0.00 0.00 0.00 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

15 0.00 0.00 0.00 0.00 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

√ √ 5 1.51 1.04 1.94 1.05 0.71 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.75 0.48 0.74 0.89 0.83 0.91

10 1.80 2.08 1.44 1.42 1.07 1.69 0.0 0.0 0.0 0.0 0.0 0.0 1.21 1.59 1.34 1.53 2.71 1.63

15 2.61 2.99 2.52 2.63 1.73 2.29 0.0 0.0 0.0 0.0 0.0 0.0 2.14 2.59 2.70 2.28 2.46 2.48

√√ √ 5 0.83 0.11 1.98 1.40 1.22 6.71 0.74 0.70 0.69 0.88 0.99 0.81 0.82 1.00 0.75 0.85 0.68 1.49

10 1.10 1.27 2.53 1.78 1.43 2.84 1.04 0.99 0.92 0.93 0.82 1.10 0.99 0.95 1.07 0.94 0.64 1.68

15 1.43 1.60 2.36 2.48 1.81 2.64 1.12 1.53 1.61 1.38 1.54 1.43 1.11 1.29 1.43 1.40 0.87 1.38

√ * 5 0.67 0.00 1.32 0.16 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.00 0.00 0.70 0.53 0.00 0.00

10 0.00 0.00 0.00 0.00 0.00 0.04 0.0 0.0 0.0 0.0 0.0 0.0 0.00 0.55 0.42 0.69 0.00 0.00

15 0.00 0.56 0.16 0.32 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.00 1.12 0.55 0.59 0.00 0.00

√ √√ 5 0.11 0.10 0.58 0.00 0.05 0.00 0.06 0.09 0.08 0.10 0.12 0.07 0.18 0.17 0.21 0.12 0.06 0.06

10 0.15 0.18 1.42 0.88 0.31 1.56 0.12 0.16 0.10 0.11 0.06 0.10 0.23 0.25 0.22 0.19 1.69 1.53

15 0.28 0.26 0.88 0.10 0.11 0.18 0.16 0.25 0.23 0.20 0.23 0.20 0.32 0.41 0.64 0.29 0.19 0.20

√ 5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.09 0.92 0.12 1.35 3.20 3.27

10 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.56 0.68 0.06 0.00 1.67 0.29

15 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.68 0.54 0.31 0.60 0.63 0.59

? ? 5 0.14 0.44 0.27 0.08 0.11 0.18 0.11 0.08 0.18 0.08 0.10 0.09 0.30 0.46 0.30 0.31 1.93 1.05

10 0.20 0.55 0.59 0.33 0.19 0.81 0.14 0.13 0.11 0.12 0.10 0.12 0.47 0.47 0.28 0.27 2.36 0.77

15 0.30 0.34 0.66 0.00 0.24 0.18 0.25 0.14 0.12 0.14 0.13 0.21 0.41 0.50 0.38 0.30 0.29 0.24

5 3.59 1.93 6.38 2.95 2.26 11.72 1.02 1.03 1.18 1.28 1.46 1.24 3.31 3.21 3.01 4.22 6.86 7.27

10 3.62 5.00 7.02 4.73 3.26 8.22 1.62 1.69 1.44 1.47 1.24 1.59 3.72 4.75 3.68 3.89 10.46 6.76

15 5.04 6.27 7.50 6.68 4.25 5.82 1.93 2.44 2.46 2.23 2.42 2.34 4.88 6.90 6.29 5.83 4.84 5.29

Pretreatment

organic acids Bact

eria

Yeas

t &

mou

ld Pretreatment

time (min)

Average total acids

"As is", washed Dried, unwashed

Propionic (%)

Formic (%)

Butyric (%)

Isobutyric (%)

Acetic (%)

Lactic (%)

"As is", unwashed

Malic (%)

Tartaric

Pyruvic (%)

Total acids

Inhi

bito

ry p

oten

tial

5.35 1.67 5.29

308

Table E7: Amounts of Furfural and HMF generated in LHW pretreatment of dried and “as is”

stover

Storage

Duration

(days)

Pretreatment

time (min)

Storage

Moisture

(%)

HMF

(% DM)

Furfural

(% DM)

HMF

(% DM)

Furfural

(% DM)

HMF

(% DM)

Furfural

(% DM)

5 25 0.030 ±0.028 0.310 ±0.311 0.002 ±0.000 0.040 ±0.000 0.002 ±0.000 0.019 ±0.008

5 35 0.025 ±0.007 0.160 ±0.042 0.005 ±0.000 0.070 ±0.000 0.002 ±0.000 0.034 ±0.001

5 45 0.045 ±0.021 0.320 ±0.127 0.004 ±0.000 0.080 ±0.014 0.002 ±0.000 0.035 ±0.007

5 55 0.025 ±0.007 0.215 ±0.120 0.003 ±0.000 0.085 ±0.007 0.002 ±0.000 0.037 ±0.007

5 65 0.010 ±0.000 0.120 ±0.014 0.003 ±0.000 0.105 ±0.007 0.003 ±0.000 0.040 ±0.003

5 75 0.015 ±0.007 0.185 ±0.035 0.001 ±0.000 0.020 ±0.000 0.002 ±0.000 0.034 ±0.001

10 25 0.030 ±0.000 0.325 ±0.035 0.006 ±0.001 0.110 ±0.000 0.004 ±0.001 0.059 ±0.027

10 35 0.045 ±0.007 0.410 ±0.014 0.005 ±0.003 0.110 ±0.085 0.004 ±0.000 0.089 ±0.029

10 45 0.055 ±0.007 0.540 ±0.156 0.007 ±0.001 0.195 ±0.021 0.004 ±0.000 0.093 ±0.011

10 55 0.030 ±0.014 0.380 ±0.212 0.006 ±0.000 0.200 ±0.014 0.005 ±0.000 0.103 ±0.035

10 65 0.020 ±0.000 0.325 ±0.007 0.005 ±0.000 0.200 ±0.042 0.004 ±0.001 0.065 ±0.017

10 75 0.030 ±0.014 0.405 ±0.106 0.002 ±0.000 0.065 ±0.007 0.005 ±0.002 0.116 ±0.007

15 25 0.050 ±0.014 0.595 ±0.120 0.006 ±0.002 0.175 ±0.049 0.007 ±0.002 0.087 ±0.030

15 35 0.040 ±0.028 0.350 ±0.396 0.010 ±0.002 0.270 ±0.071 0.008 ±0.002 0.109 ±0.020

15 45 0.060 ±0.000 0.780 ±0.014 0.007 ±0.004 0.260 ±0.170 0.005 ±0.007 0.065 ±0.091

15 55 0.045 ±0.035 0.540 ±0.438 0.009 ±0.000 0.415 ±0.007 0.009 ±0.000 0.127 ±0.013

15 65 0.020 ±0.014 0.300 ±0.226 0.006 ±0.003 0.255 ±0.148 0.011 ±0.001 0.169 ±0.007

15 75 0.040 ±0.014 0.500 ±0.198 0.004 ±0.001 0.155 ±0.035 0.006 ±0.000 0.072 ±0.007

5 25 0.015 ±0.01 0.065 ±0.044 0.002 ±0.001 0.040 ±0.014 0.000 ±0.000 0.000 ±0.000

5 35 0.020 ±0.008 0.093 ±0.010 0.001 ±0.000 0.025 ±0.007 0.001 ±0.000 0.011 ±0.001

5 45 0.020 ±0.000 0.090 ±0.000 0.002 ±0.002 0.050 ±0.057 0.001 ±0.000 0.012 ±0.002

5 55 0.015 ±0.006 0.083 ±0.010 0.001 ±0.000 0.015 ±0.007 0.001 ±0.000 0.013 ±0.004

5 65 0.010 ±0.000 0.075 ±0.006 0.000 ±0.000 0.010 ±0.000 0.001 ±0.000 0.017 ±0.003

5 75 0.010 ±0.000 0.070 ±0.008 0.001 ±0.000 0.020 ±0.000 0.001 ±0.000 0.022 ±0.010

10 25 0.033 ±0.005 0.255 ±0.010 0.004 ±0.001 0.090 ±0.028 0.002 ±0.000 0.027 ±0.001

10 35 0.035 ±0.006 0.253 ±0.034 0.004 ±0.001 0.080 ±0.014 0.003 ±0.000 0.043 ±0.010

10 45 0.033 ±0.005 0.253 ±0.010 0.002 ±0.000 0.060 ±0.014 0.003 ±0.000 0.052 ±0.019

10 55 0.025 ±0.006 0.228 ±0.029 0.002 ±0.001 0.060 ±0.014 0.003 ±0.000 0.044 ±0.012

10 65 0.020 ±0.000 0.223 ±0.022 0.001 ±0.000 0.050 ±0.000 0.005 ±0.002 0.096 ±0.040

10 75 0.020 ±0.000 0.208 ±0.015 0.002 ±0.001 0.095 ±0.007 0.003 ±0.001 0.053 ±0.001

15 25 0.050 ±0.000 0.493 ±0.017 0.005 ±0.001 0.140 ±0.014 0.004 ±0.000 0.044 ±0.004

15 35 0.065 ±0.019 0.550 ±0.077 0.006 ±0.001 0.175 ±0.007 0.005 ±0.000 0.069 ±0.002

15 45 0.053 ±0.005 0.533 ±0.025 0.004 ±0.001 0.135 ±0.049 0.007 ±0.001 0.086 ±0.028

15 55 0.040 ±0.000 0.465 ±0.053 0.003 ±0.000 0.115 ±0.007 0.005 ±0.001 0.066 ±0.009

15 65 0.040 ±0.000 0.490 ±0.050 0.003 ±0.001 0.100 ±0.028 0.006 ±0.001 0.082 ±0.005

15 75 0.030 ±0.000 0.413 ±0.041 0.003 ±0.000 0.140 ±0.014 0.006 ±0.000 0.096 ±0.032

Day 220

Day 0

Unwashed, Dried Unwashed, "As is" Washed, "As is"

309

Table E8: Glucan removal (as % of initial amount present) after pretreatment of dried and “as is”

stover

Day 220 Day 0

Pretreatment

time (min)

Nominal

moisture

content

(%)

Dried,

Unwashed

"As is",

Unwashed

"As is",

Washed

Dried,

Unwashed

"As is",

Unwashed

"As is",

Washed

5 25 3.75 ± 0.50 3.81 ± 0.12 4.50 ± 0.16 4.50 ± 0.04 5.02 ± 1.00 2.79 ± 0.13

5 35 3.08 ± 0.61 4.11 ± 0.44 3.29 ± 0.01 4.38 ± 0.03 4.60 ± 1.45 3.67 ± 1.36

5 45 3.18 ± 0.20 3.26 ± 1.16 2.90 ± 0.19 4.33 ± 0.47 5.35 ± 2.51 3.53 ± 0.33

5 55 3.02 ± 0.07 3.48 ± 0.98 3.16 ± 0.10 4.02 ± 0.51 3.44 ± 0.20 3.17 ± 1.02

5 65 2.68 ± 0.14 2.61 ± 0.49 3.68 ± 0.11 3.21 ± 0.47 3.61 ± 0.11 3.93 ± 0.16

5 75 2.07 ± 0.21 2.24 ± 0.18 2.47 ± 0.44 5.11 ± 0.42 1.75 ± 0.38 2.41 ± 0.63

10 25 3.71 ± 0.05 3.81 ± 0.54 5.71 ± 0.50 4.72 ± 0.01 5.24 ± 1.14 2.66 ± 0.42

10 35 2.94 ± 0.20 3.11 ± 0.57 3.54 ± 0.04 4.97 ± 0.39 5.58 ± 0.04 3.70 ± 0.76

10 45 2.88 ± 0.14 3.75 ± 1.12 3.43 ± 0.23 4.71 ± 0.76 4.41 ± 0.56 4.04 ± 0.58

10 55 2.95 ± 0.01 3.37 ± 0.03 3.34 ± 0.39 4.43 ± 0.22 5.64 ± 0.63 3.92 ± 0.58

10 65 2.88 ± 0.33 3.16 ± 0.90 3.86 ± 0.16 3.76 ± 0.20 3.46 ± 0.19 4.48 ± 0.41

10 75 2.81 ± 0.20 2.31 ± 0.33 3.98 ± 0.03 5.06 ± 0.31 5.41 ± 0.86 3.88 ± 0.10

15 25 2.74 ± 1.23 4.86 ± 2.63 4.91 ± 0.45 4.62 ± 0.99 4.45 ± 0.57 3.87 ± 0.04

15 35 2.80 ± 0.03 3.75 ± 1.17 3.37 ± 0.41 4.87 ± 0.01 6.90 ± 1.16 5.52 ± 1.87

15 45 2.89 ± 0.04 4.11 ± 0.59 3.47 ± 0.30 4.79 ± 0.36 4.55 ± 2.28 4.54 ± 0.45

15 55 3.17 ± 0.16 2.81 ± 0.41 3.20 ± 0.06 3.96 ± 0.25 5.64 ± 1.04 3.65 ± 0.46

15 65 2.74 ± 0.01 2.53 ± 0.16 3.82 ± 0.17 4.11 ± 0.80 4.14 ± 0.95 4.82 ± 0.86

15 75 2.53 ± 0.18 4.15 ± 0.35 3.66 ± 0.38 5.18 ± 0.95 4.64 ± 0.17 3.76 ± 0.20

310

Table E9: Xylan removal (as % of initial amount present) after pretreatment of dried and “as is”

stover

Day 220 Day 0

Pretreatment

time (min)

Nominal

moisture

content

(%)

Dried,

Unwashed

"As is",

Unwashed

"As is",

Washed

Dried,

Unwashed

"As is",

Unwashed

"As is",

Washed

5 25 23.94 ± 2.75 22.33 ± 5.63 37.92 ± 5.70 22.27 ± 1.57 29.35 ± 2.80 25.93 ± 2.23

5 35 22.75 ± 2.65 30.62 ± 3.11 44.76 ± 1.09 23.51 ± 1.03 20.47 ± 0.62 26.62 ± 3.81

5 45 30.14 ± 2.86 34.61 ± 2.18 48.00 ± 3.13 21.64 ± 0.06 25.69 ± 14.65 30.76 ± 3.30

5 55 27.63 ± 0.73 33.31 ± 2.5 47.00 ± 0.47 22.84 ± 3.45 20.48 ± 1.04 30.56 ± 9.15

5 65 24.37 ± 1.18 26.64 ± 2.53 47.29 ± 2.94 19.10 ± 0.88 20.70 ± 3.42 37.37 ± 0.30

5 75 18.86 ± 1.25 20.17 ± 1.43 40.34 ± 2.89 22.29 ± 5.51 18.90 ± 2.56 23.07 ± 5.70

10 25 27.14 ± 0.33 32.86 ± 1.05 47.83 ± 0.53 28.76 ± 0.26 35.30 ± 0.58 28.54 ± 3.05

10 35 23.36 ± 0.95 33.83 ± 7.22 47.15 ± 0.85 28.64 ± 1.96 34.17 ± 5.11 35.18 ± 3.82

10 45 29.03 ± 1.42 39.69 ± 3.94 49.76 ± 1.64 26.79 ± 3.98 32.14 ± 2.65 39.67 ± 4.99

10 55 28.09 ± 0.84 34.59 ± 1.37 45.24 ± 4.55 29.46 ± 0.30 35.04 ± 1.12 41.45 ± 1.41

10 65 25.24 ± 1.85 31.07 ± 0.42 48.08 ± 1.86 23.76 ± 3.86 30.06 ± 1.23 41.16 ± 0.20

10 75 27.01 ± 1.39 29.04 ± 1.04 57.09 ± 0.82 25.99 ± 2.02 35.16 ± 3.34 38.63 ± 0.18

15 25 21.92 ± 5.78 34.89 ± 4.18 39.84 ± 4.17 27.69 ± 1.59 32.06 ± 6.20 33.89 ± 3.64

15 35 22.74 ± 1.54 33.24 ± 7.16 42.77 ± 6.11 27.25 ± 0.78 37.04 ± 3.10 38.83 ± 2.66

15 45 24.81 ± 1.57 37.37 ± 3.17 43.85 ± 3.63 26.88 ± 2.22 39.28 ± 3.46 40.59 ± 2.29

15 55 26.95 ± 1.01 30.3 ± 1.89 40.24 ± 0.66 27.71 ± 2.24 33.72 ± 4.15 41.22 ± 2.26

15 65 23.61 ± 2.34 24.22 ± 1.48 41.10 ± 2.16 26.71 ± 0.78 29.08 ± 1.05 40.42 ± 0.68

15 75 23.27 ± 2.36 31.26 ± 3.08 43.36 ± 4.19 26.34 ± 2.88 32.52 ± 0.51 37.41 ± 0.26

311

Table E10: Pretreatment products of washed dried stover at 15 minutes retention time

Moisture

content

(%)

Glucan

removed*

(%)

Xylan

removed*

(%)

Malic

(% DM)

Lactic

(% DM)

Acetic

(% DM)

Formic

(% DM)

Pyruvic

(% DM)

HMF

(% DM)

Furfural

(% DM)

25 3.43 ± 0.55 31.75 ± 1.95 1.78 ± 2.10 0.29 ± 0.01 0.83 ± 0.08 0.38 ± 0.02 0.23 ± 0.15 n.d 0.93 ± 0.05

35 3.41 ± 0.52 38.62 ± 6.75 1.93 ± 2.23 0.37 ± 0.09 0.89 ± 0.00 0.34 ± 0.01 0.22 ± 0.17 n.d 1.2 ± 0.100

45 3.32 ± 0.34 39.60 ± 5.13 2.42 ± 2.83 0.38 ± 0.09 0.87 ± 0.01 0.38 ± 0.04 0.08 ± 0.04 n.d 1.33 ± 0.06

55 3.24 ± 0.37 36.97 ± 2.72 2.90 ± 2.23 0.32 ± 0.01 0.91 ± 0.02 0.31 ± 0.00 0.23 ± 0.11 n.d 1.41 ± 0.02

65 2.67 ± 0.14 32.77 ± 0.88 3.26 ± 0.10 0.41 ± 0.14 0.79 ± 0.04 0.28 ± 0.03 0.30 ± 0.02 n.d 1.19 ± 0.06

75 2.74 ± 0.41 32.85 ± 1.53 0.37 ± 0.01 0.17 ± 0.07 0.93 ± 0.06 0.34 ± 0.03 0.09 ± 0.00 n.d 0.69 ± 0.01

* Glucan and xylan removed are percentage of original amount before pretreatment

n.d = not detected

312

Table E11: Pretreatment product and fermentation yield of dried ground “as received” stover*

* “As received” stover is stover that has not been adjusted for moisture and not ensiled. Moisture content is about 7%

Glucan and xylan removed are percentage of original amount before pretreatment

Glucan removal was not significantly different for unwashed (4.68 ± 1.16) and washed (4.84 ± 0.28) “as received” samples, p = 0.754

Xylan removal was also not significantly different: Unwashed = 30.27 ± 1.95, washed = 27.45 ± 3.44, p = 0.124

Glucan and xylan removal not significantly different across pretreatment times

pH and organic acids not significantly different for unwashed and washed “as received” samples

No significant difference in ethanol yield of unwashed (47.16 ± 3.13) and washed (46.39 ± 6.06) samples, p =0.787.

Ethanol yield at 5 minute retention time was significantly lower than 10 and 15 minutes (p = 0.02), both of which were not significantly different

from each other.

Pretreat-

ment

time

(min)

pH after

pretreatment

Glucan

removed

(%)

Xylan

removed

(%)

Tartaric

(% DM)

Malic

(% DM)

Acetic

(% DM)

Formic

(% DM)

Isobutyric

(% DM)

Pyruvic

(% DM)

5-HMF

(% DM)

Furfural

(% DM)

Ethanol

(%

theoretical)

5 4.52 ± 0.09 5.6 ± 0.35 31.57 ± 0.94 2.24 ± 0.05 0.32 ± 0.17 1.50 ± 0.18 0.35 ± 0.18 2.06 ± 1.65 0.44 ± 0.01 0.08 ± 0.06 0.29 ± 0.20 43.64 ± 0.74

10 4.30 ± 0.01 4.66 ± 0.38 26.09 ± 5.17 1.78 ± 0.91 0.32 ± 0.05 1.85 ± 0.27 0.33 ± 0.01 1.95 ± 0.25 0.27 ± 0.27 0.07 ± 0.00 0.36 ± 0.01 49.43 ± 1.30

15 4.15 ± 0.01 3.49 ± 1.34 29.79 ± 3.24 0.45 ± 0.49 0.54 ± 0.09 2.34 ± 0.08 0.46 ± 0.02 2.90 ± 0.45 0.37 ± 0.04 0.14 ± 0.01 0.69 ± 0.01 48.42 ± 2.90

5 4.30 ± 0.30 4.92 ± 0.21 30.51 ± 1.77 1.48 ± 0.91 0.43 ± 0.26 2.01 ± 0.63 0.37 ± 0.19 2.18 ± 1.94 0.43 ± 0.06 0.09 ± 0.06 0.44 ± 0.45 39.23 ± 1.34

10 4.26 ± 0.01 4.97 ± 0.32 29.46 ± 1.6 0.10 ± 0.01 0.30 ± 0.11 1.08 ± 1.52 0.23 ± 0.18 1.02 ± 1.27 0.17 ± 0.24 0.05 ± 0.05 0.20 ± 0.28 47.62 ± 1.34

15 4.33 ± 0.21 4.95 ± 0.11 25.76 ± 1.13 2.54 ± 0.62 0.24 ± 0.02 1.46 ± 0.08 0.21 ± 0.03 0.72 ± 0.03 0.38 ± 0.13 0.04 ± 0.01 0.13 ± 0.03 52.31 ± 1.73

Unwashed

Washed

313

Table E12: Ethanol yields (percentage of theoretical) of dried and “as is” stover

Day 220 Day 0

Pretreatment time

(min)

Moisture

content (%)

Dry,

Unwashed

Dry,

Washed

"As is",

Unwashed

"As is",

Washed

Dry,

Unwashed

"As is",

Unwashed

"As is",

Washed

5 25 35.85 ± 0.52 29.94 ± 1.80 65.67 ± 2.70 53.97 ± 1.41 46.26 ± 1.39 55.75 ± 0.72 43.84 ± 0.83

5 35 38.66 ± 0.88 32.50 ± 1.28 58.80 ± 0.94 61.02 ± 0.62 44.00 ± 1.13 52.66 ± 8.60 47.87 ± 10.66

5 45 37.55 ± 0.16 34.81 ± 2.42 56.58 ± 2.62 48.11 ± 7.50 42.38 ± 0.11 53.14 ± 1.78 50.45 ± 0.57

5 55 38.19 ± 4.45 31.36 ± 0.97 55.99 ± 5.35 55.68 ± 0.09 46.57 ± 0.98 45.90 ± 8.41 49.74 ± 3.50

5 65 36.82 ± 0.03 33.97 ± 0.35 47.29 ± 0.08 58.71 ± 5.18 43.33 ± 0.88 56.80 ± 1.65 56.37 ± 2.71

5 75 34.13 ± 1.87 28.07 ± 0.89 47.45 ± 9.22 55.58 ± 10.72 43.20 ± 1.15 26.28 ± 12.81 47.64 ± 5.60

10 25 39.99 ± 1.66 47.96 ± 0.77 53.55 ± 6.01 62.98 ± 4.84 47.59 ± 2.32 62.73 ± 7.04 44.38 ± 1.31

10 35 38.53 ± 3.79 39.47 ± 1.12 62.54 ± 2.64 67.56 ± 2.72 47.34 ± 1.12 54.57 ± 1.19 51.54 ± 4.02

10 45 43.36 ± 2.93 41.54 ± 5.13 57.73 ± 4.19 67.28 ± 9.04 49.14 ± 1.45 59.31 ± 8.79 50.32 ± 9.24

10 55 40.31 ± 3.78 42.63 ± 1.42 54.64 ± 4.06 63.01 ± 1.86 50.25 ± 0.77 53.67 ± 13.52 54.40 ± 3.97

10 65 41.73 ± 0.47 43.13 ± 0.96 56.36 ± 2.50 68.14 ± 2.11 45.49 ± 2.67 53.02 ± 0.26 58.43 ± 3.25

10 75 36.36 ± 0.88 32.57 ± 0.07 11.91 ± 1.05 59.40 ± 7.03 47.61 ± 0.79 64.74 ± 11.02 55.71 ± 1.66

15 25 42.63 ± 3.83 40.86 ± 3.63 59.07 ± 1.35 68.05 ± 1.15 52.72 ± 4.00 55.04 ± 13.67 48.98 ± 2.33

15 35 48.01 ± 0.87 42.31 ± 1.23 63.34 ± 2.84 68.75 ± 0.98 51.52 ± 1.87 60.05 ± 5.49 59.39 ± 1.51

15 45 55.33 ± 4.26 47.14 ± 1.01 66.82 ± 7.21 70.45 ± 3.09 52.63 ± 0.92 66.56 ± 4.58 63.98 ± 3.32

15 55 49.97 ± 2.70 31.01 ± 1.87 58.54 ± 2.99 68.92 ± 2.28 52.76 ± 0.54 52.67 ± 4.55 54.93 ± 1.72

15 65 48.94 ± 1.82 39.25 ± 8.39 55.47 ± 0.06 70.64 ± 1.84 51.85 ± 0.33 52.80 ± 1.77 60.04 ± 5.79

15 75 23.75 ± 3.32 37.68 ± 3.33 47.78 ± 2.07 43.43 ± 36.18 53.34 ± 0.38 57.14 ± 1.57 59.54 ± 4.00

314


unwashed “as is” samples. Top = yield distinguished by storage; Bottom = yield distinguished by

pretreatment time

Lactic (% DM)

Eth

an

ol (%

th

eo

reti

cal)

840

80

70

60

50

40

30

20

10

Acetic (% DM)

840

Isobutyric (% DM)

5.02.50.0

Butyric (% DM)

420

pretreat

time

5

10

15

Lactic (% DM)

Eth

an

ol (%

th

eo

reti

ca

l)

840

80

70

60

50

40

30

20

10

Acetic (% DM)

840

Isobutyric (% DM)

5.02.50.0

Butyric (% DM)

420

Storage

Ensiled

Unensiled

315


unwashed dried samples. Top = yield distinguished by storage; Bottom = yield distinguished by

pretreatment time

Lactic (% DM)

Eth

an

ol (%

th

eo

reti

ca

l)

420

60

50

40

30

20

Acetic (% DM)

210

Isobutyric (% DM)

420

PRT

time

5

10

15

Lactic (% DM)

Eth

an

ol (%

th

ere

tica

l)

420

60

50

40

30

20

Acetic (% DM)

210

Isobutyric (% DM)

420

store

Ensiled

Unensiled

316

APPENDIX F: Quality indices and model

(Chapter 7)

Tables F1 to F4 were used in defining the qualitative matrix relating organic acid to dry matter

loss, glucose yield and ethanol yields.

Table F1: Ethanol yield classification using pretreatment acids and storage acids

Pretreatment acids Storage acids

Lactic

(% DM)

Acetic

(% DM)

Isobutyric

(% DM)

EtoH

Yield

EtoH

Yield

Lactic

(% DM)

Acetic

(% DM)

Isobutyric

(% DM)

Butyric

(% DM)

MeanGrp1 0.90 2.40 3.25 62.87 61.09 1.97 0.66 0.20 0.00

MeanGrp2 1.94 3.26 2.53 53.23 50.95 1.94 1.51 0.51 0.73

MeanGrp3 0.00 8.13 0.00 11.90 11.90 0.00 2.74 1.28 2.57

Stand. Dev. Grp1 1.24 1.11 1.74 3.66 3.85 1.10 0.37 0.11 0.00

Stand. Dev. Grp2 1.96 1.46 1.13 4.85 4.37 1.46 0.86 0.51 1.20

Stand. Dev. Grp3 0.00 0.03 0.00 1.06 1.06 0.00 0.00 0.00 0.00

Minimum1 0.00 0.44 1.20 59.46 56.42 0.26 0.14 0.04 0.00

Minimum2 0.00 1.57 0.00 40.93 40.93 0.00 0.14 0.04 0.00

Minimum3 0.00 8.11 0.00 11.16 11.16 0.00 2.74 1.28 2.57

Maximum1 3.42 4.23 6.20 71.92 71.92 3.03 1.24 0.34 0.00

Maximum2 7.80 7.13 5.03 58.43 55.51 3.03 2.74 1.28 2.57

Maximum3 0.00 8.15 0.00 12.65 12.65 0.00 2.74 1.28 2.57

317

Table F2: Comparing pretreatment acids of dry and “as is” sample used in ethanol yield

classification.

Dry "As is"

Lactic (%DM)

Acetic (%DM)

Isobutyric (% DM)

EtOH yield, %

theoretical

EtOH yield, %

theoretical Lactic

(%DM) Acetic

(%DM) Isobutyric

(% DM)

MeanGrp1 2.89 0.56 1.20 38.37 19.09 0.00 7.42 0.00

MeanGrp2 1.00 0.84 1.23 47.84 53.19 1.00 2.34 2.06

MeanGrp3 0.89 1.09 2.13 54.82 64.19 0.58 2.24 2.85

Stand. Dev. Grp1 0.81 0.30 0.81 4.88 11.13 0.00 0.91 0.00

Stand. Dev. Grp2 1.37 0.48 0.77 2.28 4.70 1.61 1.41 0.99

Stand. Dev. Grp3 1.14 0.35 0.51 2.24 3.88 1.06 0.98 1.51

Minimum1 1.38 0.00 0.00 22.40 11.16 0.00 6.24 0.00

Minimum2 0.00 0.00 0.00 44.03 39.95 0.00 0.00 0.00

Minimum3 0.00 0.35 0.89 51.86 59.46 0.00 0.44 1.20

Maximum1 4.22 1.03 2.64 43.33 35.33 0.00 8.15 0.00

Maximum2 3.95 2.16 3.87 50.88 58.74 7.80 7.13 5.03

Maximum3 3.75 1.71 2.86 61.06 72.53 3.42 4.23 6.20

Table F3: Dry matter loss grouping using storage organic acids

Dry matter loss (%)

Lactic (% DM)

Acetic (% DM)

Isobutyric (% DM)

Butyric (% DM)

MeanGrp1 0.24 1.49 0.51 0.26 0.00

MeanGrp2 3.32 2.47 0.86 0.27 0.03

MeanGrp3 4.16 0.26 2.28 1.70 1.57

Stand. Dev. Grp1 0.95 0.78 0.44 0.49 0.02

Stand. Dev. Grp2 0.99 0.65 0.40 0.37 0.11

Stand. Dev. Grp3 1.57 0.54 0.46 0.92 1.24

Minimum1 -1.67 0.11 0.00 0.00 0.00

Minimum2 1.54 0.90 0.21 0.00 0.00

Minimum3 1.60 0.00 1.60 0.54 0.10

Maximum1 1.77 2.90 1.86 1.90 0.10

Maximum2 5.56 3.20 1.78 1.46 0.49

Maximum3 5.92 1.36 2.74 3.22 3.51

318

Table F4: Glucose yield, grouping based on storage organic acids

Glucose (%

theoretical) Lactic

(% DM) Acetic

(% DM) Isobutyric

(% DM) Butyric (% DM)

MeanGrp1 16.50 0.39 2.55 1.61 1.21

MeanGrp2 22.82 2.44 0.83 0.22 0.00

MeanGrp3 16.97 0.54 0.16 0.07 0.00

Stand. Dev. Grp1 3.93 0.61 0.78 0.81 1.06

Stand. Dev. Grp2 3.09 0.87 0.34 0.14 0.00

Stand. Dev. Grp3 10.39 0.52 0.19 0.07 0.00

Minimum1 9.14 0.00 1.49 0.00 0.00

Minimum2 16.24 1.33 0.20 0.00 0.00

Minimum3 5.02 0.00 0.00 0.00 0.00

Maximum1 19.96 1.42 4.17 2.41 3.14

Maximum2 27.91 4.95 1.37 0.59 0.00

Maximum3 28.65 1.16 0.42 0.15 0.00

319

Table F5: General observation in amounts of organic acids after pretreatment of “As is”

samples* and corresponding storage acids

Percent point Difference (Pretreatment acids –

storage acids) Storage acid amount (% DM) Storage Moisture (%) proportion of samples

Lactic

(-3.03) – (-0.02)a 1.94 - 3.03 35 -65% (Ensiled) 0.22

0.00 0.00 25 – 75% (Unensiled), 75%

(Ensiled)b 0.44

0.07 – 1.00 0, 2.90 – 3.03c Mostly 55% and 75%d 0.21

1.02 – 4.90

Most (78% of samples) have less than 0.30% lactic. 22% of

samples have 2.90 – 3.03% lactic

Mostly 25% 0.13

Acetice

≤ 1%f Mixed (0.00 – 2.74) 25, 35, 65 and 75 0.11

1.00 – 1.99 0 ≤ 1.24; about 51% of

samples have 0.00% acetic Mostly 25%, 35% and 55% 0.49

2.00 – 2.99 0 ≤ 1.24; about 32% of

samples have 0.00% acetic Mostly 45% and 55% 0.26

3.00 - 7.00 50% samples have > 1.20% acetic; 50% have < 1.20%

Mostly 75% 0.14

Isobutyric

(-1.28) - 0.00g 1.28 – 0.00 75% (mostly ensiled) 0.08

0.00 ≤ 2.00 0.00– 1.28; 62% of samples

have 0.00% isobutyric Mainly 55% and 25% (but 25

-75%) 0.46

2.00 – 6.00% 0.00 – 0.34; 33% of samples

have 0.00% isobutyric Mainly 35% and 45% 0.46

*”As is” samples are samples that were not processed (either by drying or size reduction) before pretreatment. Negative

values imply a decrease in storage amount after pretreatment. a Generally, lactic acid was not detected in about 60% of pretreated samples. Within this group, lactic acid was not

detected except in 5 samples. Lactic acid disappearance was observed in only ensiled samples. About half of the ensiled

samples were within this group and where associated with high storage lactic. 75% ensiled samples generally had no

lactic acid. Less than half the samples showed an increase in acid over storage amount. Of these, some had high storage

lactic and these samples generally had lesser increase of storage amount.

b This does not include all unensiled samples although all moisture levels are embraced c Few samples with no lactic acid during storage generated lactic acid during pretreatment (almost half the samples in

this group had 0%). Generally, lactic acid in these samples was generated at 15 minutes pretreatment retention time.

Two samples at 5 minutes also generated lactic acid d Two samples had moisture of 45% and two had 65% e Found in all pretreated samples; All 0 percent acetic during storage = unensiled samples f Only two samples decreased in acetic acid content ( i.e. are negative: -0.14 and -0.31) g Within this group, no isobutyric was detected in pretreated sample. And in fact the only samples without any isobutyric

in all pretreated samples

320

Figure F1: Model input interface (Part 1. Process cost modeling ending with feedstock delivery

cost)

And impact on Feedstock delivery cost

Farm size 500 Acres Corn yield 123 Bushels/acre

Storage type Dry storage

Harvest time (month/day; up

to mid December) 11/2or

Days after grain

Harvest 0Up to 35 days

Or specifyheight below

Height of cut 2 Plant height 140

Harvesting and collection operations (tick all that apply)

TRUE

TRUE

TRUE

TRUE

TRUE 3

TRUE Large round bale

TRUEChoose this option only if contract

is for complete harvest FALSE Choose forage harvester type blower

TRUE 1 1

FALSE

FALSEChoose if contract is for complete

wet storage harvest TRUE

Moisture content entering into storage (%) 30

Storage structures/configurations

All bales are assumed to be tied with twine or wire

Dry storage Wet storage

Structure 1 1

Under roof, enclosed barn Vertical si lo (Tower)

Wrapping 1 If no wrapping, leave blank. Under roof, enclosed barn does not require wrapping

Wrapping materials 3 If using "under roof, enclosed barn", leave this option blank

Also for "On field, covered with plastic or tarp", wrapping material, if applicable should be net

Base 3 If using "under roof, enclosed barn", leave this option blank. But must select an option if using on-field storage

Stacking 2

End stacking - vertically

Bale diameter/width (inches) 50 Bale length (inches) 30

Bale density (lbs/cu ft) 25 Silage density (lbs/cu ft)


Transportation to biorefinery

Transportation distance

(up to 125 miles) 25 Transportation mode Road

Container size

Go to Outputs

Mower conditioner

Windrowing

Baling

Rakng

Shredding

Hauling

Use forage harvester (Silage)

Staking bales

Chopping (Silage)

Cutting, raking, baling, hauling, and stacking

Hauling (Silage)

Chopping, Hauling and filling silo

DRY MATTER LOSSES UNDER VARIOUS STORAGE SYSTEMS

321

Figure F2: Model input interface – truncated. (Part 2. Ethanol prediction and cost modeling

ending with ethanol production cost)

Storage type Dry storage

Storage configuration Under roof, enclosed barn

Storage starting moisture (%) 30

Moisture of feedstock delivered (%) 5


Tons delivered 1086

Feedstock purchase price ($/ton) 34.51

Feedstock composition 1 Enter estimates Glucan: 34 %

Use my estimates Xylan: 21 %

%

%

Storage overseen by 2

Biorefinery

Estimate process Dry matter loss (%) 10

Number of farmers contracted 600

Preprocesing

FALSE No washing

FALSE No drying

TRUE Ground 6 mm

0.2362206

Fermentation 2

Glucose and Xylose

LHW Pretreatment time (min) 10

Go to output

Nameplate Capacity 70,000,000 gal./yr. Construction Cost

CAPITAL COST Total

Equipments Equity

Feed Handling 7,500,000.00 $ Debt

Pretreatment 18,900,000.00 $ Per Gallon Nameplate Cap.

Neutralization/Conditioning [7800000] $ Per Gallon Operating Cap.

Saccharification & Fermentation 9,400,000.00 $ Per Bushel Operating Cap.

Distil lation and Solids Recovery 21,800,000.00 $ Depreciation

Wastewater Treatment 3,300,000.00 $ Ethanol Production

Storage 2,000,000.00 $ Nameplate Capacity

Boiler/Turbogenerator 38,300,000.00 $ Operating Capacity

Util ities 4,700,000.00 $

Total Equipment cost 105,900,000.00 $ Corn stover Usage

Warehouse 1,588,500.00 $ Natural Gas Usage

Site Development 5,893,751.75 $ Electricity Usage

Total Installed Cost (TIC) 113,382,251.75 $ Electricity Cost

Indirect Costs Water Usage

Field Expenses 22,676,450.35 $ Water Cost

Home Office & Construction Fee 28,345,562.94 $ Number of Employees

Project Contingency 3,401,467.55 $ Labor & Management Cost

Total Capital Investment (TCI) 167,805,732.59 $ Interest Cost

Other Costs Cellulolytic enzymes

(Startup, Permits, etc.) 16,780,573.26 $ Fermenting microbe (Yeasts)

Working Capital 14,512,928.22 $ Chemical: process & antibiotics

$ Chemical: waste treatment

Total Project Investment 199,099,234.07 $ Boiler cooling tower

per year

70,000,000 gallons per year

26,777,265 gallons per year

$9,954,961.70

Inputs Internal Output

Facility Construction Annual Production and Resource Usage

$7.44

$99,549,617

$99,549,617

Reduction size

$8,212,843

77

$199,099,234

$2.84

$4.93

619,123

24,100

$120,498

$2,054,000

Economic Model of an Ethanol Production Facility

You can change the contents of input category below if you have data fitting your system

per year

per year

per year

803,318

$10,711

$64,265

$2,704,504

937,204

$602,488

$3,280

bushels per year

1,000 cubic feet/year

per year

per year

per year

per year

$669,432

employees

per year

kilowatt hours/year

gallons per year

Washing

Drying

Size reduction (grinding)

ETHANOL YIELD and PRODUCTION COST

322

Table F6: Dry matter loss relationship for dry storage used in the model

Storage condition

Under roof, enclosed Relationship from this study

Under roof, open sides, elevated = 1.75 x “Under roof, enclosed”

Under roof, open sides, on ground=1.38 x “Under roof, open

sides, elevated”= 2.40 x “Under roof, enclosed”

Under roof, open sides, net wrap, elevated= 0.47 x “Under roof, open

sides, on ground”= 1.13 x “Under roof, enclosed”

Under roof, open sides, net wrap, on ground= 0.65 x “Under roof, open

sides, on ground”= 1.56 x “Under roof, enclosed”

Under roof, open sides, plastic wrap, elevated= 0.35 x “Under roof, open

sides, net wrap, elevated”= 0.40 x “Under roof, enclosed”

Under roof, open sides, plastic wrap, on ground

= 0.35 x “Under roof, open

sides, net wrap, on

ground”

= 0.55 x “Under roof, enclosed”

On field, covered with plastic or tarp, on ground= 2.0 x “Under roof, open

sides”=3.5 x “Under roof, enclosed”

On field, uncovered, on ground

= 2 x “On field, covered

with plastic or tarp, on

ground”

= 7.0 x “Under roof, enclosed”

On field, covered with plastic or tarp, elevated = 1.5 x “Under roof, enclosed”

On field, uncovered, elevated= 3 x “On field, covered,

elevated”= 4.5 x “Under roof, enclosed”

= 0.72 x “On field, covered,

on ground”

On field, covered with plastic or tarp, net wrap, on

ground

0.65 x “On field, covered


ground”

=2.28 x “Under roof, enclosed”

On field, covered with plastic or tarp, net wrap,

elevated

0.47 x “On field, covered


ground”


On field, uncovered, net wrap, elevated= 0.47 x “On field,

uncovered, on ground”=3.30 x “Under roof, enclosed”

On field, uncovered, net wrap, on ground=0.65 x “On field,

uncovered, on ground”=4.55 x “Under roof, enclosed”

On field, uncovered, plastic wrap, elevated

= 0.35 x “On field,

uncovered, net wrap,

elevated”


On field, uncovered, plastic wrap, on ground

= 0.35 x “On field,

uncovered, net wrap, on

ground”


Dry matter loss (factor)

323

Table F7: Dry matter loss relationship for wet storage used in the model

Storage structure Dry matter loss (factor)

Vertical (tower) silo Relationship from this study

Silage bags (tubes) = same as Vertical silo Relationship from this study

Silage bags (tubes), (Moisture level > 70%) =1.3 x “Silage bags (tubes)” =1.3 x loss in vertical silo

Wrapped silage (baleage) = 2 x “Silage bags (tubes)” =2 x loss in vertical silo

Wrapped silage (baleage), (Moisture level > 70%) =2 x loss in vertical silo

Horizontal (bunker) silo= 1.4 x “Wrapped silage

(baleage)” =2.8 x loss in vertical silo

Horizontal (bunker) silo, (Moisture level > 70%) =2.8 x loss in vertical silo

[Drive over] Pile= “Horizontal (bunker)

silo”=2.8 x loss in vertical silo

324

Table F8: Regression equations developed from Chapter 3 and from literature used in process cost estimation

Parameter Equation R-sq Comment Source

Stover yield (dry tons/acre)[corn yield (bushels/acre)*56 pounds/bushel]/2000

pounds/tonDry weight basis

Relative stover biomass remaining in

the field1.08*Relative cutting height + 4.51 0.93

Relative height and biomass to normalize

data. Cutting height up to 70cm? At

physiological maturity, plant height ranged

from 185 cm to 276 cm at study sites.

Wilhelm et al., 2011.

Moisture content at time of Harvest

3.63676 + 0.19474*H -0.29771*D+ 0.00446*H^2 +

0.01043*D^2 - 0.011365*H*D; (where H = percentage

harvested; D = days after grain harvest)

0.93Remodelled using derived vaiables. See main

chapter

Igathinathane et al.

(2006)

Moisture content at time of Harvest -0.5798*date of harvest + 24147 0.87Using date of harvest. Not considering

propoertion harvestedRichard lab data

Dry matter loss

Aerobic (Dry storage) 21 days 0.2856*Moisture - 2.292 0.79 Using mean PA values Chapter 3

Aerobic (Dry storage) 21 days, LCL 0.2843*Moisture - 4.336 0.89 Using mean PA values Chapter 3

Aerobic (Dry storage) 21 days, UCL 0.2869*Moisture - 0.2444 0.62 Using mean PA values Chapter 3

Aerobic (Dry storage) 90 days 0.7439*Moisture - 14.313 0.98 Using mean PA values Chapter 3

Anaerobic (Wet storage) 21 days 0.1078*Moisture - 3.9251 0.83 Using mean IA values Chapter 3

Anaerobic (Wet storage) 21 days, LCL 0.0789*Moisture - 4.5811 0.46 Using mean IA values Chapter 3

Anaerobic (Wet storage) 21 days, UCL 0.1369*Moisture - 3.2779 0.93 Using mean IA values Chapter 3

Anaerobic (Wet storage) 220 days 0.097*Moisture - 2.5692 0.75 Using mean IA values Chapter 3

Chapter 3

Aerobic (Dry storage) less than or

equal to 21 days, meanDuration*(0.2856*Moisture - 2.293)/21 Chapter 3


equal to 21 days, Lower Confidence levelDuration*(0.2843*Moisture - 4.336)/21 Chapter 3


equal to 21 days Upper Confidence levelDuration*(0.2869*Moisture - 0.2444)/21 Chapter 3

Anaerobic (wet storage) less than or

equal to 21 days, meanDuration*(0.1078*Moisture - 3.9251)/21 Chapter 3


equal to 21 days Lower Confidence levelDuration*(0.0789*Moisture - 4.5811)/21 Chapter 3


equal to 21 days Upper Confidence levelDuration*(0.1369*Moisture - 3.2779)/21 Chapter 3

Aerobic (Dry storage) greater than 21

days, mean

= (0.2843*Moisture - 4.336)+ (0.0065*Moisture -

0.1666)*(Duration-21)Chapter 3


days, LCL




days, UCL



Anaerobic (wet storage) greater than

21 days

=(0.1078*Moisture - 3.9251)+ (-0.00005*Moisture +



21 days




21 days



325

Table F8 cont:

Change in moisture after storage


equal to 21 days=(-0.0022*Moisture + 0.0947)*Duration 0.73 Chapter 3


21 days= (6E-06*Moisture^2 - 0.0007*Moisture + 0.016)*Duration 0.67 Chapter 3


equal to 21 days=(-0.0063*Moisture + 0.4716)*Duration 0.93 Chapter 3


days

=(-0.0002*Moisture^2 + 0.0183*Moisture -

0.1266)*Duration0.94 Chapter 3

Transportation ($/ton) = 2.0493*ln(distance to refinery (miles)) - 2.2822 From literature

Silo cost

Vertical silo cost, structural =31.216*Capacity + 17054 ISA

Vertical silo operating cost/year =(145.68*Capacity + 335919)/20

Bunker silo cost, structural =12.603*Capacity + 14576 ISA

Bunker silo operating cost/year '= (236.24*Capacity + 422811)/20

Stoichiometry

Glucose to ethanol C6H12O6 = 2C2H5OH + 2CO2 ≡ 0.511 kg of Ethanol/Kg glucose General knowledge

Xylose to ethanol 3C5H1oO5 = 5C2H5OH + 5CO2 ≡ 0.511 kg of Ethanol/Kg xylose General knowledge

Structural sugar (Anaerobic)

Glucan (%) = 0.0009*Moisture^2- 0.1099*Moisture + 36.825 0.69 Chapter 3

Glucan by duration (%)

=(0.0009*Moisture^2 - 0.1099*Moisture +

36.826)+(0.000003*Moisture^2 - 0.0004*Moisture +

0.0079)*Duration

0.67 Chapter 3

xylan (%) = 0.0014*Moisture^2 - 0.1526*Moisture + 25.66 0.51 Chapter 3

Xylan by duration (%)

= (0.0014*Moisture^2 - 0.1526*Moisture +

25.67)+(0.000003*Moisture^2 - 0.0004*Moisture +

0.0079)*Duration

0.50 Chapter 3

Structural sugar (Aerobic)

Glucan (%)≤ 21days =(-0.0727*Moisture + 35.723)-( -0.0039*Moisture + 0.1624)*(21-duration)0.94 Chapter 3

Glucan (%)> 21days (<35% MC)= (-0.0727*Moisture + 35.724)+(0.0013*Moisture -


Glucan (%)> 21days (>35% MC)=(-0.0727*Moisture + 35.723)+( -0.0039*Moisture +


xylan (%) ≤ 21days

=(-0.0545*Moisture+ 21.754)-(-0.0024*Moisture +

0.0906)*(21-duration)0.92 Chapter 3

Xylan (%)> 21days= (-0.0545*Moisture + 21.755)+(-0.0024*Moisture +

0.0906)*(Duration-21)0.89 Chapter 3

Storage acid

Lactic ≤ 21 days =(-0.0012*Moisture^2 + 0.155*Moisture - 2.7767)/21 0.88 Using mean IA values Chapter 3

Lactic > 21 days=(-0.0012*Moisture^2 + 0.155*Moisture - 2.7767)+(-1E-

05*Moisture^2 + 0.001*Moisture - 0.0177)*(Duration-21)0.74 Using mean IA values Chapter 3

Acetic ≤ 21 days =( -0.0002*Moisture^2 + 0.0364*Moisture - 0.6546)/21 0.82 Using mean IA values Chapter 3

Acetic > 21 days= (-0.0002*Moisture^2 + 0.0364*Moisture - 0.6546)+(4E-

06*Moisture^2 - 0.0003*Moisture + 0.0056)*(Duration -21)0.82 Using mean IA values Chapter 3

Isobutyric ≤ 21 days =( 0.0004*Moisture^2 - 0.0323*Moisture + 0.6745)/21 0.73 Using mean IA values Chapter 3

Isobutyric > 21 days=( 0.0004*Moisture^2 - 0.0323*Moisture + 0.6745)+(2E-

06*Moisture^2 - 0.0001*Moisture+ 0.002)*(Duration-21)0.98 Using mean IA values Chapter 3

326

Table F9: Regression equations developed from this study and used ethanol cost and prediction model


Pretreatment acid (unensiled feedstock)-direct estimation from storage moisture

Acetic at 5 mins = 0.0041*Moisture^2 - 0.316*Moisture + 6.4542 0.75 Chapters 5 & 6

Acetic at 10 mins = -4E-05*Moisture^2 + 0.0284*Moisture + 0.524 0.41 Chapters 5 & 6

Acetic at 15 mins = -0.0004*Moisture^2 + 0.0633*Moisture + 0.1127 0.58 Chapters 5 & 6

Isobutyric at 5 mins = -0.0011*Moisture^2 + 0.084*Moisture - 0.0647 0.77 Chapters 5 & 6

Isobutyric at 10 mins = 0.0005*Moisture^2 - 0.0618*Moisture + 3.2344 0.46 Chapters 5 & 6

Isobutyric at 15 mins = -0.0002*Moisture^2 - 9E-06*Moisture + 2.8808 0.45 Chapters 5 & 6

Pretreatment acid (Ensiled feedstock)-direct estimation from storage moisture



Acetic at 15 mins = -0.0028*Moisture^2 + 0.2947*Moisture - 4.2078 0.85 Chapters 5 & 6



Isobutyric at 15 mins '= -0.0031*Moisture^2 + 0.2888*Moisture - 2.0295 0.69 Chapters 5 & 6

Ratio of pretreatment acid: storage acid (Ensiled feedstock) used in predicting pretreatment acid from storage acid







Ethanol yield from Organic acid =58.6 + 3.21*Isobutyric -3.94*Acetic 0.67 Chapters 5 & 6

Yield correction factor, unensile

At 5 mins pretreatment time = 0.0007*Moisture^2 - 0.0614*Moisture + 2.3649 0.50 Asis_220day/Asis_0day Chapters 5 & 6

At 10 mins pretreatment time = -0.0009*Moisture^2 + 0.0789*Moisture - 0.5861 0.77 Asis_220day/Asis_0day Chapters 5 & 6

At 15 mins pretreatment time = -0.0002*Moisture^2 + 0.0151*Moisture + 0.7752 0.62 Asis_220day/Asis_0day Chapters 5 & 6

Drying impact factor

At 5 mins pretreatment time = -0.0004*Moisture^2 + 0.0328*Moisture + 0.5344 0.55 Asis_day0/ dry_day0 Chapters 5 & 6

At 10 mins pretreatment time = 0.0003*Moisture^2 - 0.0331*Moisture + 1.8736 0.78 Asis_day0/ dry_day0 Chapters 5 & 6

At 15 mins pretreatment time = -0.0001*Moisture^2 + 0.0096*Moisture + 0.8692 0.19 Asis_day0/ dry_day0 Chapters 5 & 6

Drying silage impact

At 5 mins pretreatment time = -0.0001*Moisture^2 + 0.0127*Moisture + 0.5537 0.60 EnsiledDry_220day/UnensiledDry_0day Chapters 5 & 6

At 10 mins pretreatment time = -8E-05*Moisture^2 + 0.0076*Moisture + 0.6764 0.17 EnsiledDry_220day/UnensiledDry_0day Chapters 5 & 6

At 15 mins pretreatment time '= -0.0006*Moisture^2 + 0.0585*Moisture - 0.2937 0.89 EnsiledDry_220day/UnensiledDry_0day Chapters 5 & 6

Washing impact

At 5 mins pretreatment time = 5E-05*Moisture^2 + 0.0017*Moisture + 0.7832 0.64 AsisWash_220day/AsisUnwash_220day Chapters 5 & 6

At 10 mins pretreatment time = 0.0034*Moisture^2 - 0.2885*Moisture + 6.6158 0.80 AsisWash_220day/AsisUnwash_220day Chapters 5 & 6

At 15 mins pretreatment time = -0.0002*Moisture^2 + 0.016*Moisture + 0.7976 0.20 AsisWash_220day/AsisUnwash_220day Chapters 5 & 6

327

Table F9 cont:


Aerobic storage PH Chapters 3

At less or equal to 21 days = 0.0007*Moisture^2 - 0.0578*Moisture + 9.4244 0.91 Chapters 3

At >21 days storage = -0.0009*Moisture^2 + 0.0949*Moisture + 6.1017 0.64 Chapters 3

Pretreatment pH "As is" -unwashed, unensiled

At 5 min = -0.0004*Moisture^2 + 0.0343*Moisture + 3.8987 0.75 Chapters 5 & 6



Relating pH to acid content after pretreatment

Acetic at 5 mins = 92.471*pH^2 - 859.94*pH + 2000.1 0.89 for pH between 4.0 and 4.8 (regression range) Chapters 5 & 6



Isobutyric at 5 mins = -21.631*pH^2 + 201.09*pH - 466.01 0.60 for pH between 4.0 and 4.8 (regression range) Chapters 5 & 6



328

Table F10: Field operation custom rates and losses used in model

Operation Mean Range Unit SourceHarvesting Source

Mower conditioner 17.4 12.00 - 22.00 $/acre Pike (2013)

Willowing $/acre Pike (2013)

shredding 16.6 8:00 - 23:00 $/acre Pike (2013)

Raking 9.55 5.75 - 14.75 $/acre Pike (2013)

Pick up/Baling

Type of bale

Small: Square with wire 0.91 0.45 - 2.00 $/bale Twete et al. (2009)

Square with Twine 0.92 0.45 - 2.00 $/bale Twete et al. (2009)

Large: Round (under 1500 lbs.) Without net 10.45 6.00 - 19.00 $/bale Twete et al. (2009)

Round (under 1500 lbs.) with net 10.71 7.00 - 16.00 $/bale Twete et al. (2009)

Round (over 1500 lbs.) Without net 10.56 7.00 - 17.00 $/bale Twete et al. (2009)

Round (over 1500 lbs.) with net 10.86 7.00 - 20.00 $/bale Twete et al. (2009)

Square (approx. 1 ton) 13.58 7.00 - 25.00 $/bale Twete et al. (2009)

Small Square 0.92 0.45 - 1.75 $/bale Pike (2013)

Large Round 7.7 5.50 - 10.00 $/bale Pike (2013)

Large Square 8.35 6.50 - 11.00 $/bale Pike (2013)

Wrapping bales 6.85 4.25 - 9.25 $/bale Pike (2013)

Hauling

Small bales 0.84 0.20 - 2.50 $/bale Twete et al. (2009)

Large bales 4.35 1.00 - 15.00 $/bale Twete et al. (2009)

Stacking 58.33 35.00 - 80.00 $/stack Twete et al. (2008)

Entire Process contracted: cutting,

conditioning, raking, baling,

hauling, and stacking

Small bales 1.77 0.65 - 2.25 $/bale Twete et al. (2009)

Large bales 19.1 11.00 - 28.00 $/bale Twete et al. (2009)

Use forage harvester (Silage)

Pull type chopper and tractor 1.9 acres/hr 92 70.00 - 100.00 $/hour Pike (2013)

(Schnitkey and Lattz, 2008)

Self-propelled chopper 4.5 acres/hr 247 150.00 - 350.00 $/hour Pike (2013)

(Schnitkey and Lattz, 2008)

self-propelled for bunker 50 tons/hr 371 $/hour

Blower 100 tons/hr

1 man, 2 wagons, 1 tractor (CSI, 2013) 80.6 45.00 - 100.00 $/hour Pike (2013)

2 men, 2 wagons, 2 tractors 134 85.00 - 168.00 $/hour Pike (2013)

1 man, 1 truck 74.7 57.90 - 80.00 $/hour Pike (2013)

Chopping, hauling and filling silo 7.39 4.50 - 17.00 $/ton Twete et al. (2009)

Chopping only 4.68 1.50 - 7.00 $/ton Twete et al. (2009)

Hauling only 2.18 0.20 - 4.50 $/ton Twete et al. (2009)

Chopping and hauling 6.54 4.80 - 8.50 $/ton Twete et al. (2009)

Bagging silage

< 9.0 ft 1 ton/ft 4.55 2.00 -7.50 $/foot Pike (2013)

(R.B. Family Farms, 2007)

≥ 9.0 ft 1.5 ton/ft 5.15 4.00 -7.51 $/foot Pike (2013)

(R.B. Family Farms, 2007)

Wrapping

Twine 0.54 per ton Brechbill & Wallace (2008)

Net wrap 1.97 per ton Brechbill & Wallace (2008)

Twine and plastic wrap 2.75 per ton Brechbill & Wallace (2008)

Losses

Harvest method Amount harvested (%)Baler chamber [&

ejection]losses1.60% Shinners et al. (1996)

Shredding and raking 80 Lang (2002)Pick-up losses

(Small bale)0.40% Shinners et al. (1996)

Raking only 65 Lang (2002) (Large bale) 2.60% Shinners et al. (1996)

Combine windrow only 50 Lang (2002)

Drift losses 3 0.50 - 5.00 % Mickan and Piltz (2003)

329

Table F11: Breakdown of project investment cost used in model

Equipment Cost

Feed Handling $7,500,000.00

Pretreatment $18,900,000.00

Saccharification & Fermentation $9,400,000.00

Distillation and Solids Recovery $21,800,000.00

Wastewater Treatment $3,300,000.00

Storage $2,000,000.00

Boiler/Turbogenerator $38,300,000.00

Utilities $4,700,000.00

Total Equipment cost $105,900,000.00

Warehouse $1,588,500.00

Site Development $5,893,751.75

Total Installed Cost (TIC) $113,382,251.75

Indirect Costs Field Expenses $22,676,450.35

Home Office & Construction Fee $28,345,562.94

Project Contingency $3,401,467.55

Total Capital Investment (TCI) $167,805,732.59

Other Costs (Startup, Permits, etc.) $16,780,573.26

Working Capital $14,512,928.22

Total Project Investment $199,099,234.07

330

Table F12: Breakdown of project operating cost used in model

Fixed Operating Cost

Labor & Management Number Salary

Plant Manager 1 $80,000.00

Plant Engineer 1 $65,000.00

Maintenance Supr 1 $60,000.00

Lab Manager 1 $50,000.00

Shift Supervisor 5 $37,000.00

Lab Technician 2 $25,000.00

Maintenance Tech 8 $28,000.00

Shift Operators 20 $25,000.00

Yard Employees 32 $20,000.00

General Manager 1 $100,000.00

Clerks & Secretaries 5 $20,000.00

Total Salaries 77 $2,054,000.00

Overhead/Maintenance 1,232,400.00

Maintenance 2,118,000.00

Insurance & Taxes 17,007,337.76

Total fix operating cost 22,411,737.76

Variable Operating Cost

Prices

Electricity 5.00 c/KwH

Water 0.35 ¢/gal.

Bioreagents/chemicals Cellulolytic enzymes 10.10 ¢/gal.

Fermenting microbe (Yeasts) 2.50 ¢/gal.

Chemical: process & antibiotics 2.25 ¢/gal.

Chemical: waste treatment 0.24 ¢/gal.

Boiler cooling tower 0.04 ¢/gal.

Denaturants 5.55 ¢/gal.

Other Direct Costs Repairs & Maintenance 1.50 ¢/gal.

Transportation 0.75 ¢/gal.

Other 2.00 ¢/gal.

331

Table F13: Evaluating test error by comparing with a prediction error of zero using the t-test

Table F14:Predicted and observed ethanol yield values of “as is” stover with corresponding test

errors (predicted –observed)

N Mean StDev SE Mean 95% Confidence interval T P

Without yield factors 72 0.844 7.588 0.894 (-0.939438, 2.626660) 0.94 0.349

With yield factors* 72 -3.161 9.880 1.164 (-5.48231, -0.83907) -2.71 0.008

Mean yield factorˣ 72 -3.744 8.449 0.996 (-5.72968, -1.75893) -3.76 0.000

Without yield factors 72 13.465 7.244 0.854 (11.7627, 15.1673) 15.77 0.000

With yield factors 72 10.385 9.404 1.108 (8.1747, 12.5945) 9.37 0.000

Mean yield factorsˣ 72 -1.791 6.597 0.778 (-3.34155, -0.24095) -2.30 0.024

* Yield factors applied only to unensiled

ˣ Using mean yield factors instead of yield factor equation, which accounts for moisture content

"As is"

Dried

feedstock

Storage

duration

pretreat

time

(min)

Storage

moisture

content

(%) Observed Predicted

With

yield

factor

Apply

mean

yield

factor

Without

yield

factor

With

yield

factor

with mean

yield factor

Without

yield

factor

With

yield

factor

with

mean

yield

factor

63.76 56.83 56.83 56.83 -6.93 -6.93 -6.93 TRUE TRUE TRUE

67.58 57.42 57.42 57.42 -10.15 -10.15 -10.15 TRUE TRUE TRUE

58.13 58.67 58.67 58.67 0.54 0.54 0.54 FALSE FALSE FALSE

59.46 59.01 59.01 59.01 -0.46 -0.46 -0.46 TRUE TRUE TRUE


58.43 57.64 57.64 57.64 -0.79 -0.79 -0.79 TRUE TRUE TRUE

59.77 53.13 53.13 53.13 -6.64 -6.64 -6.64 TRUE TRUE TRUE




40.93 30.65 30.65 30.65 -10.28 -10.28 -10.28 TRUE TRUE TRUE

53.97 30.50 30.50 30.50 -23.47 -23.47 -23.47 TRUE TRUE TRUE



60.67 59.33 59.33 59.33 -1.34 -1.34 -1.34 TRUE TRUE TRUE

64.40 62.35 62.35 62.35 -2.05 -2.05 -2.05 TRUE TRUE TRUE

60.69 54.78 54.78 54.78 -5.92 -5.92 -5.92 TRUE TRUE TRUE


57.51 53.72 53.72 53.72 -3.79 -3.79 -3.79 TRUE TRUE TRUE


54.59 54.08 54.08 54.08 -0.51 -0.51 -0.51 TRUE TRUE TRUE

58.12 54.40 54.40 54.40 -3.72 -3.72 -3.72 TRUE TRUE TRUE



65

75

10

25

35

45

55

55

65

75

Day 220

5

25

35

45

Ethanol yield Predicted yield - Observed yield Predicted > Observed

332

Table F14 cont.

Storage

duration

pretreat

time

(min)

Storage

moisture

content


With

yield

factor

Apply

mean

yield

factor

Without

yield

factor

With

yield

factor

with mean

yield factor

Without

yield

factor

With

yield

factor

with

mean

yield

factor

56.26 63.07 49.77 52.35 6.81 -6.49 -3.91 FALSE TRUE TRUE

55.24 57.27 45.19 47.54 2.04 -10.05 -7.70 FALSE TRUE TRUE


58.74 62.12 57.87 51.56 3.38 -0.87 -7.18 FALSE TRUE TRUE

54.40 56.36 55.29 46.78 1.96 0.88 -7.62 FALSE FALSE TRUE

51.88 57.68 56.58 47.87 5.80 4.70 -4.01 FALSE FALSE TRUE

51.84 55.50 50.21 46.07 3.66 -1.63 -5.77 FALSE TRUE TRUE


55.63 56.19 50.84 46.64 0.57 -4.79 -8.98 FALSE TRUE TRUE

57.96 57.43 51.96 47.67 -0.53 -6.01 -10.30 TRUE TRUE TRUE

35.33 34.03 20.05 28.24 -1.30 -15.28 -7.08 TRUE TRUE TRUE


67.71 59.71 47.12 49.56 -7.99 -20.59 -18.15 TRUE TRUE TRUE

57.75 60.37 47.63 50.11 2.62 -10.12 -7.64 FALSE TRUE TRUE

53.73 60.09 55.98 49.87 6.36 2.25 -3.86 FALSE FALSE TRUE

55.41 60.44 56.31 50.17 5.03 0.90 -5.24 FALSE FALSE TRUE

53.09 55.77 54.71 46.29 2.69 1.62 -6.80 FALSE FALSE TRUE

65.52 50.77 49.81 42.14 -14.75 -15.72 -23.38 TRUE TRUE TRUE


63.23 56.71 51.30 47.07 -6.52 -11.93 -16.16 TRUE TRUE TRUE

52.84 55.96 50.63 46.45 3.12 -2.21 -6.39 FALSE TRUE TRUE

53.20 56.29 50.92 46.72 3.08 -2.28 -6.49 FALSE TRUE TRUE

72.53 54.46 32.08 45.20 -18.07 -40.44 -27.33 TRUE TRUE TRUE

56.95 51.24 30.19 42.53 -5.72 -26.77 -14.43 TRUE TRUE TRUE


64.70 62.18 49.06 52.85 -2.52 -15.64 -11.85 TRUE TRUE TRUE

63.93 61.12 56.94 51.95 -2.81 -6.99 -11.97 TRUE TRUE TRUE

56.17 62.67 58.39 53.27 6.51 2.22 -2.89 FALSE FALSE TRUE

63.32 57.01 55.92 48.46 -6.32 -7.40 -14.87 TRUE TRUE TRUE

69.80 57.75 56.65 49.08 -12.06 -13.15 -20.72 TRUE TRUE TRUE

55.88 57.82 52.31 49.15 1.94 -3.57 -6.73 FALSE TRUE TRUE

49.45 56.22 50.86 47.78 6.77 1.41 -1.66 FALSE FALSE TRUE

51.55 55.20 49.93 46.92 3.65 -1.61 -4.63 FALSE TRUE TRUE

54.05 59.31 53.66 50.42 5.26 -0.40 -3.64 FALSE TRUE TRUE

58.25 54.87 32.32 46.64 -3.38 -25.93 -11.61 TRUE TRUE TRUE

56.03 56.22 33.12 47.79 0.20 -22.90 -8.24 FALSE TRUE TRUE

55.35 55.35 51.35 50.76 0.80 3.16 3.74

7.99 11.06 9.80

6.07 7.63 7.74

65

75

Mean

Root mean squared error

Maximum absolute error

15

25

35

45

55

55

65

75

75

10

25

35

45

45

55

65

Day 0

5

25

35

Ethanol yield Predicted yield - Observed yield Predicted > Observed

333

Table F15: Predicted and observed ethanol yield values of dried stover with corresponding test

errors (predicted –observed)

Storage

duration

pretreat

time

(min)

Storage

moisture

content


With

yield

factor

Apply

mean

yield

factor

Without

yield

factor

With

yield

factor

with mean

yield factor

Without

yield

factor

With

yield

factor

with

mean

yield

factor



39.81 59.00 43.33 39.67 19.19 3.52 -0.14 FALSE FALSE TRUE

41.11 59.88 43.97 40.26 18.77 2.86 -0.85 FALSE FALSE TRUE




43.26 61.07 51.40 41.07 17.81 8.14 -2.19 FALSE FALSE TRUE

38.52 56.94 55.79 38.28 18.42 17.27 -0.24 FALSE FALSE TRUE

38.56 57.14 55.98 38.42 18.58 17.42 -0.14 FALSE FALSE TRUE



40.62 59.53 38.31 44.63 18.91 -2.31 4.01 FALSE TRUE FALSE

43.08 62.77 40.39 47.06 19.69 -2.69 3.98 FALSE TRUE FALSE



47.55 61.60 69.81 46.18 14.05 22.26 -1.37 FALSE FALSE TRUE








47.45 61.95 52.94 45.47 14.50 5.49 -1.98 FALSE FALSE TRUE


50.88 60.35 55.17 44.30 9.47 4.29 -6.58 FALSE FALSE TRUE

49.60 62.09 56.77 45.58 12.49 7.17 -4.02 FALSE FALSE TRUE

61.06 62.17 58.50 45.63 1.11 -2.56 -15.43 FALSE TRUE TRUE

54.75 62.41 58.73 45.81 7.66 3.98 -8.94 FALSE FALSE TRUE

50.30 61.64 57.52 45.24 11.34 7.22 -5.06 FALSE FALSE TRUE

54.30 62.84 58.63 46.12 8.54 4.33 -8.18 FALSE FALSE TRUE

49.87 56.83 50.65 41.72 6.96 0.78 -8.15 FALSE FALSE TRUE

52.56 61.22 54.56 44.94 8.66 2.00 -7.62 FALSE FALSE TRUE


27.31 63.01 51.52 46.25 35.70 24.21 18.94 FALSE FALSE FALSE75

35

45

55

65

65

75

15

25

10

25

35

45

55

45

55

65

75

Predicted > Observed

Day 220

5

25

35

Ethanol yield Predicted yield - Observed yield

334

Table F15 cont.

Storage

duration

pretreat

time

(min)

Storage

moisture

content


With

yield

factor

Apply

mean

yield

factor

Without

yield

factor

With

yield

factor

with mean

yield factor

Without

yield

factor

With

yield

factor

with

mean

yield

factor

49.76 58.33 52.82 40.79 8.57 3.06 -8.97 FALSE FALSE TRUE

47.70 57.22 51.81 40.01 9.52 4.11 -7.69 FALSE FALSE TRUE

47.19 56.28 47.20 39.35 9.09 0.01 -7.84 FALSE FALSE TRUE

45.50 56.09 47.04 39.23 10.59 1.54 -6.27 FALSE FALSE TRUE

44.71 58.71 48.91 41.05 14.00 4.20 -3.66 FALSE FALSE TRUE

44.56 57.30 47.74 40.07 12.74 3.18 -4.49 FALSE FALSE TRUE

48.32 58.53 51.87 40.93 10.21 3.55 -7.39 FALSE FALSE TRUE

49.78 57.68 51.12 40.34 7.90 1.34 -9.44 FALSE FALSE TRUE

46.30 56.81 58.19 39.73 10.51 11.89 -6.57 FALSE FALSE TRUE

44.98 60.36 61.82 42.21 15.38 16.84 -2.77 FALSE FALSE TRUE

46.36 55.25 74.22 38.63 8.89 27.86 -7.73 FALSE FALSE TRUE

44.64 56.06 75.31 39.20 11.42 30.67 -5.44 FALSE FALSE TRUE

48.40 57.53 46.63 49.17 9.13 -1.77 0.77 FALSE TRUE FALSE

51.86 59.65 48.35 50.98 7.79 -3.51 -0.88 FALSE TRUE TRUE




52.83 56.49 56.97 48.28 3.66 4.14 -4.55 FALSE FALSE TRUE

53.50 59.41 61.85 50.78 5.91 8.35 -2.72 FALSE FALSE TRUE

52.36 60.17 62.64 51.43 7.81 10.28 -0.93 FALSE FALSE TRUE



50.74 55.03 51.02 47.04 4.29 0.28 -3.70 FALSE FALSE TRUE


52.55 59.87 62.66 45.70 7.32 10.11 -6.85 FALSE FALSE TRUE

58.51 62.29 65.20 47.55 3.78 6.69 -10.96 FALSE FALSE TRUE

55.66 60.50 65.50 46.18 4.84 9.84 -9.48 FALSE FALSE TRUE

52.88 63.17 68.40 48.22 10.29 15.52 -4.66 FALSE FALSE TRUE

54.75 62.58 68.75 47.77 7.83 14.00 -6.98 FALSE FALSE TRUE

56.13 60.66 66.64 46.30 4.53 10.51 -9.83 FALSE FALSE TRUE

55.18 60.86 66.62 46.46 5.68 11.44 -8.72 FALSE FALSE TRUE

55.97 59.92 65.59 45.74 3.95 9.62 -10.23 FALSE FALSE TRUE

54.36 60.86 65.16 46.46 6.50 10.80 -7.90 FALSE FALSE TRUE

54.86 65.25 69.86 49.81 10.39 15.00 -5.05 FALSE FALSE TRUE

56.45 61.11 62.74 46.65 4.66 6.29 -9.80 FALSE FALSE TRUE

55.90 61.11 62.74 46.65 5.21 6.84 -9.25 FALSE FALSE TRUE

50.79 60.09 57.01 46.08 13.46 10.38 1.79

15.27 13.97 6.79

13.46 10.74 5.48

Mean

Root mean squared error

Maximum absolute error

35

45

55

65

75

65

75

15

25

10

25

35

45

55

45

55

65

75

Day 0

5

25

35

Predicted > ObservedEthanol yield Predicted yield - Observed yield

IRENE DZIDZOR DARKU

571 723 6879

Email: [email protected]

EDUCATION

2007 – 2013 PhD, Agricultural and Biological Engineering, Pennsylvania State University, USA

2003 – 2004 MPhil in Engineering for Sustainable Development, University of Cambridge, UK

1998 – 2002 B.Sc. in Agricultural Engineering with First-Class Honors, Kwame Nkrumah University

of Science & Technology (KNUST), Ghana

1989 – 1994 Senior School Certificate (S.S.C), United Faith Tabernacle College. Jarawan-Kogi,

Plateau State, Nigeria

PUBLICATIONS

Darku, I. D and T. L. Richard. Biofuels: ethanol producers. 2011. Encyclopedia of life sciences. John

Wiley & Sons, Ltd. Chichester. Available at http://www.els.net/WileyCDA/ElsArticle/refId-

a0020373.html

Darku, I. D, M. N. Marshall and T. L. Richard. 2010. Implications of organic acids in wet storage and

bioconversion of corn stover to ethanol. ASABE Paper No. 1008748. St. Joseph, Mich.: ASABE

Schwartz J. L., I. D. Darku, T. L. Smith, M. N. Marshall, T. L. Richard. 2010. From fields to fuels: A

student workshop on conversion of biomass to ethanol. ASABE Paper No. 1008874. St. Joseph, Mich.:

ASABE

Darku, I. D and K. Essien. 2009. Book review: Oil, Democracy, and the Promise of True Federalism in

Nigeria. African and Asian Studies, Vol. 8 (3): 338-340

Darku, I.D. 2005. Exploring the potential of oil palm fiber as a clean solid fuel to meet local energy

needs in Ghana. In Hunger without Frontiers; Proceedings of the 2nd West Africa Society of Agricultural

Engineering International Conference on Agricultural Engineering: 447 – 461. E.Y.H. Bobobee and A.

Bart-Plange, eds. Design Press: KNUST, Ghana

MANUSCRIPT REVIEW

BioResources Journal - Enzymatic hydrolysis of switchgrass and coastal bermuda grass pretreated using

different chemical methods

ORAL AND POSTER PRESENTATIONS

“Implications of organic acids in wet storage and bioconversion of corn stover to ethanol.” Oral

presentation at the ASABE Int'l Meeting, June 20 -23, 2010. Pittsburgh, PA.

"Effect of wet storage on the value of corn stover as a biofuel feedstock." Poster presentation at the 31st

Symposium on Biotechnology for Fuels and Chemicals, May 3 - 6, 2009, San Francisco, CA.

“Lignocellulosic feedstock value after wet storage: correlating dry matter loss, structural composition,

and environmental variables.” Poster presentation at the Northeast Renewable Energy Conference,

August 25-28, 2008, State College, PA.

“Exploring the Potential of Oil Palm Fiber as a Clean Solid Fuel to Meet Local Energy Needs in Ghana.”

Oral presentation at the International Conference on Agricultural Engineering, September 20 -24, 2004,

Kumasi, Ghana.

“Small scale irrigation projects in Ghana -evaluating sustainability principles.” Poster presentation at the

Center for Sustainable Development, February, 2004, University of Cambridge, UK.

http://www.els.net/WileyCDA/ElsArticle/refId-a0020373.html

http://www.els.net/WileyCDA/ElsArticle/refId-a0020373.html

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