Combinatorial Life Cycle Analysis (Environmental Impact Assessment) of Microalgae-based Biodiesel...

Combinatorial Life Cycle Analysis (Environmental

Impact Assessment) of Microalgae-based

Biodiesel Production Chains

CN4118R – Final Year Project

THE NATIONAL UNIVERSITY OF SINGAPORE

Department of Chemical & Biomolecular Engineering

Final Report

15 Jan 2014

Submitted by: Tan Wei Han, Denis (A0072022Y)

Mentor: Yu Nan

Supervisor: Dr. Lee Dong-Yup

I am immensely grateful for the support and assistance provided by my project

supervisor, Dr. Lee Dong-Yup, and my project mentor, Ms. Yu Nan. They accorded me

with utmost flexibility to determine the direction of my research endeavour. The

project has taught me, amongst other things, the importance of perseverance and

endeavour.

Table of Contents

Abstract ................................................................................................................. I

List of Figures ....................................................................................................... III

List of Tables ........................................................................................................ IV

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

2 Scope .............................................................................................................. 3

3 Literature Review ............................................................................................ 5 3.1 LCAs of Microalgae-based Biodiesel ........................................................................ 5

3.2 LCAs of Petroleum Diesel and Other Biodiesel ......................................................... 6

4 Methodology .................................................................................................. 7 4.1 General ............................................................................................................... 7

4.2 Stage 1: Cultivation ........................................................................................... 10

4.3 Stage 2: Harvesting............................................................................................ 11

4.4 Stage 3: Extraction ............................................................................................ 12

4.5 Stage 4: Conversion ........................................................................................... 13

4.6 Biodiesel Transport ........................................................................................... 14

5 Results and Discussion .................................................................................. 15 5.1 Stage 1: Cultivation ........................................................................................... 15

5.2 Stage 2: Harvesting............................................................................................ 18

5.3 Stage 3: Extraction ............................................................................................ 20

5.4 Stage 4: Conversion ........................................................................................... 23

5.5 Biodiesel Transport ........................................................................................... 25

5.6 Global Optimisation .......................................................................................... 25

5.7 Analysis of Environmentally Optimal Production Chain ...................................... 31

6 Conclusion and Future Work ......................................................................... 36

7 Supplementary Information .......................................................................... 37

References .......................................................................................................... 37

Annexes ............................................................................................................. 1-1 Annex 1 – Definition of Terms ................................................................................... 1-1

Annex 2 – Key Modelling Parameters Obtained from the Literature ........................... 2-1

Annex 3 – Options Considered for Harvesting Production Step ................................... 3-1

Annex 4 – Options Considered for Extraction Production Step .................................... 4-1

Annex 5 – Options Considered for Conversion Production Step .................................. 5-1

Annex 6 – Project Timeline ........................................................................................ 6-1

I

Abstract

Global energy demand is expected to increase by more than 35% from 2010 to 2035

of which 80% continues to be supplied by fossil fuels. This brings forth concerns of

environmental sustainability. Third-generation microalgae-based biodiesel appears

to hold the answer to some of these issues through its reduction of fossil fuel usage

while avoiding problems that plagued previous generation biodiesel. However, policy

makers are yet to be fully convinced. This study puts forth the following questions: Is

third-generation biodiesel environmentally benign? Are all methods of producing

third-generation biodiesel equally environmentally sustainable? If not, which

methods are more sustainable?

A cradle-to-pump environmental impact assessment (EIA) was undertaken to

determine the net energy ratio (NER), life cycle water footprint (WF), and global

warming potential (GWP) of various production methods of microalgae-based

biodiesel. A functional unit of fatty acid methyl ester (FAME) biodiesel produced

from 100000 kg of cultivated algal biomass was used. 11 cultivation methods, 45

harvesting methods, 144 lipid extraction methods, and 4 conversion methods were

considered. 16 optimistic scenarios were then selected for further study. The

environmental impacts of the 16 scenarios varied widely, indicating that not all

methods of producing biodiesel are equally sustainable. Through optimisation, this

study finds that the environmentally optimal production chain involves open thin-

layer photo-bioreactor cultivation, single-step chitosan harvesting, belt drying, and

dry direct in-situ trans-esterification. This production chain is calculated to have a

WF of 2.7 tonnes of water and GWP of 0.9 tonnes of CO2-equivalent greenhouse

gases (GHG) per gigajoule (GJ) of FAME biodiesel, well above that for the production

of an equivalent GJ of petroleum diesel (0.0098 tonnes of water and 0.24 tonnes of

CO2-equivalent GHG). The NER exceeds that of petroleum diesel (0.83) but is some

distance from that of corn biodiesel (3.22).

II

Hence, this study finds that microalgae-based biodiesel does not constitute an

unambiguous improvement over the diesel fuel currently in use, leading to the

conclusion that third-generation biodiesel today is not green. However, the

identified environmentally optimal production chain will assist to optimise R&D and

policy development efforts so that the much-vaunted environmental potential of

microalgae-based biodiesel can be attained sooner rather than later.

This study is the first life cycle study on biofuels involving optimisation across

multiple environmental criteria and further augments the literature by expanding

the scope of production methods considered.

III

List of Figures

Figure 1: Biodiesel Production Chain Schematic……………………………………………….. 07

Figure 2: System Boundary and Superstructure of Microalgae-based Biodiesel

Production Chain……………………………………………………………………………………………… 08

Figure 3: Cultivation Schematic………………………………………………………………………… 10

Figure 4: Harvesting Schematic………………………………………………………………………… 11

Figure 5: Extraction Schematic…………………………………………………………………………. 12

Figure 6: Conversion Schematic……………………………………………………………………….. 13

Figure 7: Delivery Schematic…………………………………………………………………………….. 14

Figure 8: Net Energy Ratio of various cultivation methods……………………………….. 17

Figure 9: Water Footprint of various cultivation methods………………………….…….. 17

Figure 10: GWP-100 of various cultivation methods…………………………………………. 17

Figure 11: Net Energy Ratio for various harvesting methods.……….….….….….….… 19

Figure 12: Water Footprint for various harvesting methods.……………………..…..… 19

Figure 13: GWP-100 of various harvesting methods…………………………………………. 19

Figure 14: Net Energy Ratio for 144 Extraction Combinations………………………….. 22

Figure 15: Water Footprint for 144 Extraction Combinations…………………………… 22

Figure 16: GWP-100 for 144 Extraction Combinations……………………………………… 22

Figure 17: Net Energy Ratio for Conversion Stage……………………………………..……… 24

Figure 18: GWP-100 for Conversion Stage………………………………………………………… 24

Figure 19: Procedure for Selection of 4 Options for Aggregated Extraction-

Conversion Process………………………………………..………………………………………………… 25

Figure 20: Schematic for Global Optimisation………………………………………..………… 26

Figure 21: Summary of EIA for 16 Scenarios……………………………………………………… 27

Figure 22: Net Energy Ratio for 16 Scenarios……………………………………………………. 28

Figure 23: Water Footprint for 16 Scenarios…………………………………………………….. 28

Figure 24: Global Warming Potential for 16 Scenarios……………………………………… 28

Figure 25: Environmentally Optimal Microalgae-based Biodiesel Production

Chain………………………………………………………………………..………………………………..……. 31

Figure 26: Life Cycle Energy Footprint for Environmentally Optimal Production

Chain………………………………………………………………………..………………………………..……. 32

Figure 27: Life Cycle Water Footprint for Environmentally Optimal Production

Chain………………………………..………………………………..……………………………………………. 33

Figure 28: Life Cycle GWP for Environmentally Optimal Production Chain……….. 34

Figure 29: Summary of Contribution of Each Stage to the 3 Environmental

Dimensions………………………………..………………………………..…………………………………… 35

IV

List of Tables

Table 1: Summary of Results of Local Optimisation for Cultivation Stage…………. 16

Table 2: Summary of 16 Scenarios for Global Optimisation……..………………………. 26

Table 3: Key Modelling Parameters Extracted or Calculated from Literature for

Cultivation Section……..…………………………………………..……………………………………….. 2-1


Harvesting Section……..…………………………………………..……………………………………….. 2-2


Cell Disruption Sub-section of Extraction Section…..………………………………………… 2-3


Drying Sub-section of Extraction Section…..………………………………………..…..………. 2-3


Lipid Extraction Sub-section of Extraction Section………………………………..………….. 2-3


Conversion Section…..………………………………………..…..……………………………………….. 2-4

Table 9: Options Considered for Harvesting Segment of Production Chain………. 3-1

Table 10: Options in Disruption Sub-Stage…..……………..……………..…………………….. 4-1

Table 11: Options in Drying Sub-Stage…..……………..……………..………….…..………….. 4-1

Table 12: Options in Lipid Extraction Sub-Stage…..……………..……………..…………….. 4-1

Table 13: Options in Conversion Sub-Stage…..……………..……………..…………………… 5-1

Table 14: Milestone Dates…..……………..……………..……………………..……………………… 6-1

1

1 Introduction

Global energy demand is expected to increase by more than 35% from 12,380

Million Tonnes of Oil Equivalent (MTOE) in 2010 to 16,730 MTOE in 2035 [1] due to

growing populations and emerging affluence [2]. Moreover, it is forecasted that with

business-as-usual conditions, fossil fuels will continue to supply almost 80% of the

world’s energy demand through 2040 [3]. This is clearly unsustainable as

consumption of fossil fuels accounts for the majority of global anthropogenic

greenhouse gas (GHG) emissions [4]. These emissions are well accepted by the

scientific community to be the foremost cause of climate change [5].

Biofuels, fuels produced from biomass [6], have emerged as renewable alternatives

to fossil-based sources of energy and have been much discussed in academia in the

past decade [4, 6-35]. They are believed to be (1) environmentally friendly, (2)

producible in a sustainable manner, and (3) biodegradable [21]. Of the various forms

of biofuels, biodiesel and bioethanol have been most aggressively pursued as

potential replacements for diesel and gasoline respectively [19]. In particular,

biodiesel has been the subject of much research as it is a potential direct

replacement for petroleum diesel which will surpass gasoline as the number one

global transportation fuel by 2020 [2].

Biodiesel can be produced from various types of feedstock. To date, there are three-

generations of biodiesel. First-generation biodiesel is produced primarily from edible

oils derived from rapeseed, palm oil, soybean, and sunflower [4, 19, 21, 36]. The

fundamental drawback of first-generation biodiesel is the threat they impose on

global food supply as their production creates direct competition for resources with

production of food since it requires the use of food crops [4, 17, 19, 21, 36-39].

Second-generation biodiesel is produced primarily from oils derived from either non-

edible parts of current food crops, or non-food crops like miscanthus and jatropha

[4, 19, 21, 26, 29, 33, 36]. They are an improvement over first-generation biodiesel

as there is no direct competition for food crop demand. However, land competition

2

is still significant [4, 19, 28, 39]. Third-generation biodiesel, thus, offers the most

promise as it can potentially avoid competition for food crops and minimize land

competition. Third-generation biodiesel feedstock is primarily made up of

microalgae [4, 12, 17, 21, 31, 32, 36] that grows at a time scale several orders of

magnitude below that of plant crops. It is important to note that in spite of its

apparent potential, large-scale microalgae-based biodiesel production is still nascent

and generally not commercially viable with production cost well in excess of

petroleum diesel sale price [40, 41]. There are, fortunately, a few burgeoning

commercial applications to date [42].

Despite the promises of biodiesel and biofuels in general, one cannot simply assume

that they are sustainable or green. In fact, the European Environment Agency

Scientific Committee has expressed concern and scepticism with regards to

bioenergy’s environmental sustainability, particularly its ability to reduce GHG

emissions [43]. To date, numerous life cycle assessments have been conducted on

biofuels [11, 13, 18, 20, 27, 32, 33, 37-39, 44-51] to determine their environmental

sustainability to mixed results. This study seeks to add to the growing literature on

this topic by answering the following questions: Is third-generation biodiesel truly

environmentally benign? Are all methods of producing third-generation biodiesel

equally environmentally sustainable? If not, which methods are more sustainable?

3

2 Scope

This project, conducted over 5 months, involves a cradle-to-pump environmental

impact assessment (EIA) on various methods of biodiesel production from

microalgae. The project milestones are listed in Annex 6.

Specifically, the net energy ratio (also known as energy returns on investment), life

cycle water footprint (WF), and global warming potential (GWP) of the various steps

in microalgae-based biodiesel production were quantified. These 3 criteria were

chosen as they were deemed to be the most significant environmental dimensions of

the present time [2-4, 52]. Definition of the nomenclature is provided in Annex 1.

Numerous options were considered at each of the four production process steps of

(1) cultivation, (2) harvesting, (3) extraction, and (4) conversion. The options

considered are not meant to be exhaustive but to be diverse and representative of

the different technologies available. Some novel technology was also included. A

review of the options at each process step is not the intention of this study so the

interested reader is referred to Brennan et. al [17] and Mata et. al [19] for two of the

many available in the literature.

All in all, 11 cultivation methods from, 45 harvesting methods from, 144 lipid

extraction methods, and 4 conversion methods were considered. The functional unit

for the entire production chain was chosen as fatty acid methyl ester biodiesel

(FAME) produced from 100000 kg cultivated algal biomass. Due to the enormous

number of production combinations possible, an exhaustive assessment of all1

production chains would be highly time-consuming. Instead, promising methods

were isolated from each of the production steps through local optimisation.

Optimisation aims to balance the trade-offs between possibly conflicting

environmental criteria of NER, WF, and GWP. In this study, a linear multi-objective

function involving the three environmental dimensions (NER, WF, GWP) was applied

1 In principle, a total of 11 x 45 x 144 x 4 = 285120 production chains are possible by combining the options considered at each of the 4 process steps.

4

for the optimisation process. The promising methods of each process step were

combined to create 16 scenarios for further analysis to determine the global

optimum based on NER, WF, and GWP.

Upon determining the environmentally optimal production chain, the NER, WF, and

GWP of that production chain was compared to their equivalent for petroleum diesel

and corn biodiesel. These diesels are currently used in the market and serve as a

sound basis to judge if microalgae-based biodiesel indeed constitute an

environmental improvement to the status quo.

This project not only adds to but also augments the growing literature of life cycle

assessments (LCA) conducted on biofuels [11, 13, 18, 20, 27, 33, 37-39, 44-51, 53].

Surprisingly, little has been written on the comparison between life cycle

environmental impact assessments of different microalgae-based biodiesel

production chains. To my knowledge, the only prior literature in this area was by

Yang et. al [51], Delrue et. al [53], Lardon et. al [37], Xu et. al [54], and Bretner et. al

[55]. This study applies the same combinatorial approach of Bretner et. al [55] albeit

with different modelling assumptions. Moreover, while Bretner et. al [55] focuses

solely on energetics to determine process optimality, this study expands the scope of

scenarios considered and is, to my knowledge, the first to simultaneously consider

and optimise across multiple environmental criteria.

5

3 Literature Review

3.1 LCAs of Microalgae-based Biodiesel

Various life cycle assessments on third-generation biodiesel have been conducted in

recent times to a wide range of disparate results. These include substantial prior

work on the energetics of microalgae-based biodiesel production. The life cycle NER

of biodiesel has been reported to range from 0.13 [55] to 5.9 [20] for open raceway-

based production and 0.23 [46] to 1.1 [56] for photobioreactor-based production.

Some of the variation can be explained by locational differences with studies

conducted in Asia [57], the United States [45], and across Europe [37] while the rest

accrue from differences in system boundary and modelling assumptions. Despite the

divergence, there is widespread consensus that open raceway production consumes

less energy than photobioreactor production resulting in the former having a higher

average NER than the latter.

For life cycle water footprint of microalgae-based biodiesel, there has been not

much prior work in the literature. The most notable findings were by Delrue et. al

[53] and Yang et. al [51] who listed the water footprint at 9.8 tonnes per GJ and 16

tonnes per GJ respectively. There is general agreement that the methods of

cultivation and the target recycle ratio have significant impact on the life cycle water

consumption.

The GWP of microalgae-based biodiesel production has had substantial prior work

though researchers typically focus their attention on carbon emissions and neglect

other greenhouse gases. There has also been significant variation on the GWP

findings ranging from -75.3 kg per GJ [56] to 0.534 tonnes per GJ [55].

The range of results on all 3 environmental dimensions attests to the uncertainty

inherent in life cycle studies. This does not negate the effectiveness of LCAs but

merely serves to highlight that LCAs are an important starting point for further, more

precise environmental impact research. It is useful to note that much of the detailed

6

studies undertaken are based on the open raceway cultivation method. More work

could be done on non-raceway cultivation of third-generation biodiesel and this

study answers the call by including other photobioreactor cultivation methods.

3.2 LCAs of Petroleum Diesel and Other Biodiesel

Sheehan et. al [58] undertook a holistic study on biodiesel and petroleum diesel life

cycles in 1998 and found that petroleum diesel had an NER of 0.83 while first-

generation biodiesel, in this case derived from corn feedstock, had an NER of 3.22.

Consequently, corn biodiesel is clearly more environmentally amendable than

petroleum diesel on the energy front. They also quantified the life cycle fossil carbon

emission for petroleum diesel and corn biodiesel. Sheehan et. al [58] found that

petroleum diesel has a cradle-to-grave life cycle CO2 emission of 0.240 tonnes per GJ

of diesel while corn biodiesel has a cradle-to-grave CO2 emission of 0.257 tonnes per

GJ of biodiesel. The figures fall to 0.032 tonnes and 0.04 tonnes respectively when

tailpipe emissions are omitted to quantify the cradle-to-pump carbon footprint.

Finally, the authors reported the life cycle water footprint of petroleum diesel and

corn biodiesel to be 0.0098 tonnes and 32.2 tonnes. The work of Sheehan et. al [58]

will serve as an evaluation basis for the results arising from this study.

7

4 Methodology

4.1 General

There are 5 stages in a typical microalgae-based biodiesel production process: strain

selection, cultivation, harvesting, extraction, and lipid conversion into biodiesel. This

is followed by biodiesel delivery to the demand source to complete the cradle-to-

pump framework. The biodiesel production schematic used in this study is shown in

Figure 1.

Figure 1: Biodiesel Production Chain Schematic

The latter 4 of the 5 stages are the focus of this study. While strain selection has

significant impact on lipid content and, hence, the energy producible from the

biomass cultivated, it is omitted from this study to broaden the available cultivation

data. The interested reader is pointed to Rodolfi et. al [15] for a concise treatise on

the impact of strain selection on biodiesel production. This study assumes an

average lipid content from the strains chosen by the authors from which the

cultivation stage is based.

The overall superstructure of the production chain is defined in Figure 2. The system

boundary of the functional unit used in this study is represented by the dotted line.

The green, red, and brown arrows represent inflow, outflow, and recycle streams

8

respectively. Water is assumed to be the only material recycled. This study also

omits from consideration any energy coupling process or co-location credits to

ensure a fair comparison with LCA of petroleum diesel and corn biodiesel conducted

by Sheehan et. al [58].

Figure 2: System Boundary and Superstructure of Microalgae-based Biodiesel Production Chain

Each stage, with its own functional basis, was modelled in Excel and/or OpenLCA

based on parameters obtained from the literature (see Annex 2). LCA allocation was

done based on energy content. The NER, life cycle water footprint, and GWP were

then computed for the numerous options considered at each of the 4 stages. Local

optimisation was done at each of the 4 stages to identify 2 of the most

environmentally optimistic options. The intention was for the 2 options from each

stage to be combined to obtain 16 (24) scenarios for further analysis. However, this

procedure was altered due to the interacting nature of the extraction and

9

conversion stages2. This will be elaborated further in Section 5.6. Global optimisation

was then pursued based on the 16 scenarios.

The linear multi-objective function, henceforth referred to as Equation 1, used for

both local and global optimisation (minimisation) in this study was:

1𝑁𝐸𝑅𝑖

⁄

(1𝑁⁄ ) ∑ 1

𝑁𝐸𝑅𝑖⁄𝑁

1

+𝑊𝐹𝑖

(1𝑁⁄ ) ∑ 𝑊𝐹𝑖

𝑁1

+𝐺𝑊𝑃𝑖

(1𝑁⁄ ) ∑ 𝐺𝑊𝑃𝑖

𝑁1

𝑤ℎ𝑒𝑟𝑒 𝑖 ∈ [1, 𝑁] 𝑎𝑛𝑑 𝑁 𝑖𝑠 𝑡ℎ𝑒 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑜𝑝𝑡𝑖𝑜𝑛𝑠 𝑢𝑛𝑑𝑒𝑟 𝑐𝑜𝑛𝑠𝑖𝑑𝑒𝑟𝑎𝑡𝑖𝑜𝑛

Equation 1: Optimisation Equation

The function is a weighted sum of the 3 environmental criteria of this study. Each

criterion was given an equal weight, as there is no sound basis to tilt the balance

towards any particular criterion in any particular way. While WF and GWP are clearly

criteria that, upon minimisation, reflect environmental optimality, NER acts in

reverse. Hence, the reciprocal of NER was included in the objective function rather

than NER.

For local optimisation at each stage, only options with NER > 1 were considered as

production steps that already consume more energy than maximally producible are

not only at odds with environmental feasibility but also with economic realities. This

study would be less useful if the environmentally optimal production chain identified

is so far at odds with economic realities that it will never be utilised in a meaningful

way. Hence, options with NER < 1 were omitted.

Key modelling parameters obtained from the literature for this study are provided in

Annex 2 while detailed documentation is available in the supporting documentation.

2 Due to the interactive nature of the extraction and conversion stages (i.e. selection of conversion method affects choice of extraction method significantly and vice versa), the 16 scenarios were formed from 4 optimistic options in the aggregated extraction-conversion stage rather than the 2 scenarios from each of extraction and conversion stages. This is discussed in greater detail in Section 5.6.

10

4.2 Stage 1: Cultivation

The cultivation stage schematic is shown in Figure 3. The functional basis for this

stage is 100000 kg of dry biomass production. 11 different cultivation methods from

6 categories published by 10 different authors [18, 59-67] were considered. The 6

categories of cultivation methods considered were: raceway pond, tubular

photobioreactor, open thin-layer photobioreactor, flat-plate photobioreactor,

enclosed photobioreactor, and heterotrophic bioreactor. Both conventional

methods (raceway) and novel ones (tubular solarised airlift reactor) were

considered. Modelling parameters of all 11 cultivation methods can be found in

Table 3, Annex 2.

Figure 3: Cultivation Schematic

The EIA for 9 of the reactors were newly modelled from empirical data. The EIA for

the remaining 2 reactors were replicated from the life cycle assessment conducted

by Jorquera et. al [18], albeit with minor changes to the system boundary and

process parameters.

The cultivation stage forms the foundation for biodiesel production and is probably

the most significant step in the EIA. The cultivated biomass determines the

subsequent amount of lipid extractable and, hence, biodiesel producible.

11

4.3 Stage 2: Harvesting

The harvesting stage, shown in Figure 4, is primarily modelled based on the

dewatering methods reviewed by Uduman et. al [68]. The functional basis for this

stage is 1 m3 of 0.923%3 total suspended solids (TSS) cultured microalgae from the

cultivation process and the harvested output was taken to be 20% TSS microalgae

slurry.

Figure 4: Harvesting Schematic

45 options of both single-step and double-step dewatering were considered [10, 69-

79]. For brevity, the options are summarised in Annex 3 while the modelling

parameters can be found in Table 4. Annex 2. Both conventional methods

(gravitational sedimentation) and novel ones (ultrasonic aggregation) were included.

The harvesting stage is likely to be the least significant stage in the overall EIA as it is

not as energetically or resource intensive as the others.

3 An average output TSS from the 11 cultivation methods considered.

12

4.4 Stage 3: Extraction

The extraction schematic of this study is shown in Figure 5. The functional basis for

this stage is 1 m3 of 20% total suspended solids (TSS) microalgae slurry from the

harvesting stage.

Figure 5: Extraction Schematic

Extraction can be categorised into wet or dry extraction (with additional drying step).

Pre-extraction preparation can also include cellular disruption (not shown on

schematic). Hence, 3 sub-stages (cell disruption, drying, and lipid extraction) were

considered in this stage. Cell disruption data was obtained from Lee et. al [80] and

Bunge et. al [81], drying data was referenced from Baker & McKenzie [82],

Hassebrauck & Ermel [83], and Mujumdar [84], and lipid extraction data was based

on Lee et. al [85], Halim et. al [86], Andrich et. al [87], and Mendes et. al [88].

All in all, 6 cell disruption options, 6 drying options, and 4 lipid extraction options

were considered, giving rise to 144 (6 x 6 x 4) combinations of both wet and dry

extraction. For all 3 sub-stages, conventional and novel methods were included. For

instance, novel methods such as ultrasonic cell disruption and supercritical carbon

dioxide extraction were considered. For brevity, the different options are

summarised in Annex 4 while modelling parameters can be found in Tables 5 to 7,

Annex 2.

13

4.5 Stage 4: Conversion

The schematic of the conversion stage is reflected in Figure 6. The functional basis

for this stage is 1 kg of FAME biodiesel produced by lipid conversion.

Figure 6: Conversion Schematic

All in all, 4 options were considered. The 4 options arise from the 2 possible

dimensions of the lipid to biodiesel conversion processes: (1) extraction-

transesterification versus direct in-situ conversion and (2) dry versus wet. The

former distinguishes the conversion process by whether a separate (stage 3)

extraction step is required.

Direct conversion processes are believed to be more commercially feasible as less

process steps are required. The options are summarised in Annex 5 while modelling

parameters can be found in Table 8, Annex 2 with the data primarily referenced

from Batan et. al [56] and Johnson et. al [89].

14

4.6 Biodiesel Transport

Figure 7 shows the schematic of the biodiesel transport segment.

Figure 7: Delivery Schematic

A cradle-to-pump EIA for the production of microalgae-based biodiesel has to

include transport and delivery of the biodiesel product to the demand source. In this

study, the average delivery distance was taken to be 100 km and carried out by a

diesel-powered combination truck. Consequently, the functional basis for the

transport stage was the transport of 1 tonne of biodiesel for 100 km.

15

5 Results and Discussion

5.1 Stage 1: Cultivation

The results of the LCA-EIA (NER, water footprint, and GWP) for the cultivation step

are summarised in Figures 8 to 10.

The NER (0.07 to 8), water footprint (2 x 106 to 75 x 106 kg of water), and GWP (-1.7 x

105 kg to 3.7 x 105 kg) of the 11 cultivation methods varied significantly across

options, indicating the wide variation of environmental impacts accruing from

different cultivation methods.

The NER of the cultivation step is crucial in determining if a given microalgae-based

biodiesel production chain is green. Any cultivation process that has NER below 1

would imply that more energy, here assumed to be entirely from fossil-based

sources, is required in materials and operation of the cultivation method than is

maximally producible from the cultivated biomass. Moreover, the subsequent

downstream steps add to the input energy requirement, making the NER even lower

after accounting for the entire biodiesel production chain.

In this study, only 5 of the 11 cultivation methods were found to have an NER in

excess of 1: 1 of the 2 flat plate photobioreactor, all 3 open thin-layer

photobioreactors, and the raceway pond. The environmental impact findings of the

various cultivation methods are in general agreement with those of Jorquera et. al

[18], Husemann & Benemann [90], and Rodolfi et. al [15] in that raceway cultivation

remains the most energetically feasible cultivation method with an NER of 7.91.

The various cultivation methods considered all had environmental trade-offs.

Raceways offered the highest potential for energetic feasibility but at the expense of

high water footprint and GWP. On the other hand, the other cultivation methods

had lower WF at the expense of NER barely crossing 1.

16

Local optimisation was done in terms of cultivation categories (6) instead of

cultivation methods (11) in recognition of the potential significant variation in

environmental impact within each cultivation category4. Local optimisation identified

the open thin-layer cultivation method as the most environmentally optimal

followed by the raceway pond. Though the open thin-layer cultivation method had a

significantly lower NER than raceway cultivation, it made up for it through its much

lower WF and GWP. The values of the objective function (based on Equation 1) for

the 6 cultivation categories are summarised in Table 1.

S/N Cultivation Methods (11) Cultivation Category (6) Value of Objective Function (lower is better)

1 Yoo et. al, 2013 Open Thin-Layer -13.8

Doucha et. al, 2005

Doucha et. al, 2009

2 Jorquera et. al, 2010 Raceway 38.7

3 Jorquera et. al, 2010 Flat Plate -*

Cheng-Wu et. al, 2001

4 Molina et. al, 2000 Tubular -*

Richmond et. al, 1992

Bahadur et. al, 2013

5 Olaizola, 2000 Enclosed -*

6 Li, 2007 Heterotrophic -* Table 1: Summary of Results of Local Optimisation for Cultivation Stage

*Some or all of the cultivation methods in this category have NER < 1 and hence are omitted from

consideration

More details are available in the supporting documentation.

4 For instance, the 2 flat plate photobioreactors considered have relatively different environmental impacts with only 1 of them having an NER > 1.

17

0 1 2 3 4 5 6 7 8 9

Raceway (Jorquera)

Tubular (Molina)Tubular (Richmond)

Tubular (Bahadur)

Thin Layer (Doucha)Thin Layer (Doucha, updated)

Thin Layer (Yoo)

Flat Plate (Jorquera)Flate Plate (Cheng-Wu)

Enclosed (Olaizola)

Heterotrophic (Li)

NER per 100 ton Dry Biomass Cultivated

0 1 2 3 4 5 6 7 8

Raceway (Jorquera)

Tubular (Molina)Tubular (Richmond)

Tubular (Bahadur)

Thin Layer (Doucha)Thin Layer (Doucha, updated)

Thin Layer (Yoo)

Flat Plate (Jorquera)Flate Plate (Cheng-Wu)

Enclosed (Olaizola)

Heterotrophic (Li)

10^7 kg

Water Footprint per 100 ton Dry Biomass Cultivated

-2 -1 0 1 2 3 4

10^5 kg CO2 equivalent GHG

GWP-100 per 100 ton Dry Biomass Cultivated

Optimal

Legend

Figure 8: Net Energy Ratio of various cultivation methods

Figure 9: Water Footprint of various cultivation methods

Figure 10: GWP-100 of various cultivation methods

18

5.2 Stage 2: Harvesting

The NER (3 to 575) and GWP (0.12 kg to 13.6 kg) of the 45 options considered in the

harvesting section varied significantly, indicating the wide range of environmental

impacts accruing from different harvesting options. All harvesting techniques had

NER > 1, indicating that less energy is required for harvesting than the energy

producible from the harvested biomass. This is expected as harvesting is not a

resource and energetically intensive step along the production chain. However,

some harvesting methods required significantly more energy to put into effect than

others. This also contributed to their much higher GWP as energy consumption was

assumed to be from fossil sources that contribute to high levels of greenhouse gas

emission.

Water footprint (47.8 to 48.5 kg of water) of all 45 options was relatively similar due

to the modelling assumption of 95% recycle ratio.

Through local optimisation, single-step chitosan flocculation harvesting from

0.923% TSS to 20% TSS algal suspension was found to be the environmentally

optimal harvesting technique with the highest NER, lowest water footprint, and

lowest GWP. Two-step magnetophoretic harvesting followed by chitosan

flocculation was a close second. The values of the objective function were 1.038 and

1.041 respectively.

The summary of results (NER, water footprint, and GWP) for the harvesting step is

reflected in Figures 11 to 13.


19

47.4

47.6

47.8

48

48.2

48.4

48.6

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45

kg

Water Footprint per m3 of 0.923% TSS Algal Slurry

0

2

4

6

8

10

12

14

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45kg

CO

2 e

qu

ival

en

t G

HG

GWP-100 from Biodiesel Producible from 1m3 of 0.923% TSS Algal Slurry

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45

NER per m3 of 0.923% TSS Algal Slurry Optimal

Legend

Figure 11: Net Energy Ratio for various harvesting methods

Figure 12: Water Footprint for various harvesting methods

Figure 13: GWP-100 of various harvesting methods

20

5.3 Stage 3: Extraction

The summary of results (NER, water footprint, and GWP) for the extraction step is

reflected in Figures 14 to 16.

From Figures 14 and 16, the NER and GWP results take the form of 6 similar clusters,

reflecting the 6 different drying methods (from no drying, spray drying, freeze

drying, thermal drying, drum drying to belt drying). Within each cluster, the results

primarily reflect the differences arising from the 4 different lipid extraction methods

considered (2 solvent and 2 supercritical CO2 methods). The differences stemming

from the different cell disruption techniques (from no disruption to osmotic shock)

are much more nuanced and cannot be distinguished with the naked eye. This is

because the environmental impact arising from different disruption techniques is

several magnitudes less than the effects from the various drying and lipid extraction

methods.

As is the case with the previous two stages, the NER (0.026 to 1.64) and GWP (2200

kg to 66600 kg) of the 144 options considered in the extraction section varied

significantly, indicating the wide range of environmental impacts accruing from

different extraction combinations. Prior to conducting the EIA, it was expected that

wet extraction methods, as indicated by the blue portions of Figures 11 to 13, would

have higher NER, lower WF, and lower GWP as environmental impact accruing to the

resource intensive drying process was avoided. However, these predictions are not

reflected in the findings.

This is primarily because while wet extraction methods had lower energy

consumption due to the omission of the drying sub-stage, they required more

energy during the lipid extraction sub-stage due to the larger volume of solution

mixture handled (solvent + slurry). The effects happen to cancel.

Water footprint (750 kg or 0 kg) differed between the 144 combinations only

depending on whether the extraction method was wet or dry. Water that had not

21

been evaporated due to the drying process was assumed to be recycled and already

accounted for in the 95% recycle ratio previously assumed.

Through local optimisation, two extraction combinations from 20% TSS algal

suspension were found to be environmentally optimal. They are: solvent extraction

without cell disruption and drying and solvent extraction with osmotic shock cell

disruption without drying. The values of the objective function were both 0.569.


22

a Optimal

Wet

Dry

Legend

0

10000

20000

30000

40000

50000

60000

70000

1 5 9

13

17

21

25

29

33

37

41

45

49

53

57

61

65

69

73

77

81

85

89

93

97

10

1

10

5

10

9

11

3

11

7

12

1

12

5

12

9

13

3

13

7

14

1

GW

P-1

00

a (k

g C

O2

eq

uiv

ale

nt)

GWP-100a (kg CO2 equivalent)

Figure 16: GWP-100 for 144 Extraction Combinations

0

100

200

300

400

500

600

700

800

1 5 9

13

17

21

25

29

33

37

41

45

49

53

57

61

65

69

73

77

81

85

89

93

97

10

1

10

5

10

9

11

3

11

7

12

1

12

5

12

9

13

3

13

7

14

1

Wat

er

Foo

tpri

nt

(kg)

Water Footprint (kg)

Figure 15: Water Footprint for 144 Extraction Combinations

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

1 5 9

13

17

21

25

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33

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41

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49

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57

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97

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11

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11

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12

1

12

5

12

9

13

3

13

7

14

1

NER

Figure 14: Net Energy Ratio for 144 Extraction Combinations

23

5.4 Stage 4: Conversion

The summary of results (NER and GWP) for the conversion stage is reflected in

Figures 17 and 18. WF was not considered for this production stage as most water

was assumed to be recycled. This has been accounted for by the 95% recycle ratio

previously assumed.

The dry conversion methods, as indicated by the red portions of Figures 17 and 18,

was found to have higher NER and lower GWP than their wet counterparts. This is

intuitive, as the volume of material handled during dry conversion was lower.

All in all, the NER (0.12 to 8.17) and GWP (0.91 kg to 151.4 kg) of the 4 options

considered in the conversion section varied widely, indicating the wide range of

environmental impacts possible from different conversion methods.

Through local optimisation, dry post-extraction transesterification to produce 1 kg

of FAME biodiesel was found to be the environmentally optimal conversion method.

Wet post-extraction transesterification was a distant second. The values of the

objective function were 0.080 and 0.119 respectively. More details are available in

the supporting documentation.

The findings, at first glance, seem to contradict prior belief that direct conversion

processes are more feasible as less process steps are involved. However, this is

erroneous. The EIA for this stage does not constitute a fair comparison between the

two categories of conversion methods as extraction was omitted (and separately

modelled in the previous stage). Consequently, the extraction and conversion stages

can be understood to be interactive and should not be considered in isolation of

each other. This will be expanded upon in the global optimisation portion of the

results and analysis.

24

a Optimal

Wet

Dry

Legend

0

1

2

3

4

5

6

7

8

9

Extraction - TransE (Wet) Extraction - TransE (Dry) Direct In Situ (Wet) Direct In Situ (Dry)

NER

Figure 17: Net Energy Ratio for Conversion Stage

0

20

40

60

80

100

120

140

160

Extraction - TransE (Wet) Extraction - TransE (Dry) Direct In Situ (Wet) Direct In Situ (Dry)

GW

P-1

00

a (k

g C

O2

eq

uiv

ale

nt)

GWP-100a (kg CO2 equivalent)

Figure 18: GWP-100 for Conversion Stage

25

5.5 Biodiesel Transport

From OpenLCA, transport of 1 tonne of FAME biodiesel by a diesel-powered

combination truck for a distance of 100km consumed 0.10 GJ of energy from fossil

sources and produced 8.0 kg of GHG equivalent CO2. More details are available in

the supporting documentation.

5.6 Global Optimisation

The optimal microalgae-based biodiesel production chain was obtained by global

optimisation. As previously discussed, the 16 scenarios were intended to be

assembled from the 2 most environmentally amendable options arising from local

optimisation of the 4 production stages. This remained the case for the cultivation

and harvesting stages. However, as the extraction and conversion stages were

interactive, simple combination of the 2 optimal methods from each of the

extraction and conversion stage might not give rise to the production chains that

included the global optimum. The interactive nature of the extraction and

conversion stages can be understood through a simple example. Consider direct

conversion methods: the lipid extraction sub-stage would, by definition, be rendered

obsolete so the choice of the conversion stage affects the choice of the previous

extraction stage, constituting interactive process steps.

Hence, extraction-conversion was treated as a single aggregated stage and 4 options

were chosen for it. The procedure for the selection of the 4 options is summarised in

Figure 19. The 16 options formed by combinatorial aggregation is summarised in

Table 2.

Figure 19: Procedure for Selection of 4 Options for Aggregated Extraction-Conversion Process

Choose 1 of the 4 conversion methods

Vary the extraction methods

Calculate NER, WF, and GWP for entire extraction-conversion process

Find extraction-conversion option that minimises Equation 1 – This is 1 of the 4

options

Change conversion method until all 4 are

exhausted

1 2 3

4 5

Repeat to Step 2

6

26

Consequently, the 16 scenarios can be simplified into the following schematic in

Figure 20. The interaction between the extraction and conversion stages are

apparent.

Table 2: Summary of 16 Scenarios for Global Optimisation

Figure 20: Schematic for Global Optimisation

27

Figure 21 summarises the results for the EIA of the 16 scenarios while more granular

breakdown of the EIA for the 16 scenarios are found in Figures 22 to 24. The optimal

production chain is also highlighted while its analysis is deferred to Section 5.7.

Figure 21: Summary of EIA for 16 Scenarios

28

0

2000000

4000000

6000000

8000000

10000000

12000000

14000000

16000000

18000000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Wat

er

Foo

tpri

nt

(kg)

Water Footprint

Figure 23: Water Footprint for 16 Scenarios

0

200000

400000

600000

800000

1000000

1200000

1400000

1600000

1800000

2000000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

GW

P-1

00

a (k

g C

O2

eq

uiv

ale

nt)

GWP-100a

Figure 24: Global Warming Potential for 16 Scenarios

Optimal

Legend

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

NER

Figure 22: Net Energy Ratio for 16 Scenarios

29

From Figure 22, it can be seen that only production chains 3, 7, 11, and 15

(production chains with the same dry direct in-situ extraction-conversion method

but differing in cultivation and harvesting methods) had NER in excess of 1. These

production chains, given the chosen basis of FAME biodiesel produced from 100000

kg of cultivated biomass, are surprisingly not the ones with the lowest life cycle

energy consumption. They have life cycle energy footprints of around 680 GJ.

Instead, options 1, 5, 9, and 13 (production chains that differ from 3, 7, 11, and 15

respectively only by wet direct in-situ extraction-conversion method) have the

lowest total life cycle energy consumption of around 180 GJ due to the absence of

the energetically significant drying step. However, options 3, 7, 11, and 15, have

emerged as the only production chains with NER > 1 due to the much higher

conversion efficiency arising from dry direct conversion as compared with wet direct

conversion given today’s technology[89]. This meant that although wet direct

conversion consumes only a fraction of the energy of dry conversion, its conversion

efficiency is so much lower that the amount of FAME biodiesel produced by the

latter more than offset the energetic requirement, resulting in a much higher NER

for the latter. The wet direct conversion options (1, 5, 9, and 13) have NER of around

0.5.

The fact that NER is relatively invariant amongst options 3, 7, 11, and 15 highlights

that stages 3 and 4 (conversion-extraction) are more energetically important than

stages 1 and 2 for production methods involving dry direct in-situ transesterification.

Hence, it implies that the energetic bottleneck today lies in stages 3 and 4 and future

innovations to dry direct in-situ transesterification can be a big step to improving the

NER and energetic feasibility of microalgae-based biodiesel.

Option 11 emerges as the environmentally optimal production chain primarily by

distinguishing itself from 3, 7, and 15 on WF and GWP. This can be seen in Figures 23

and 24. The primary difference between the option set 3 & 7 and the option set 11 &

15 is the cultivation method. 3 & 7 involved the conventional raceway pond

cultivation method that has seen more commercial application while 11 & 15

involved the relatively novel open thin-layer photobioreactor that, to my knowledge,

30

has not seen large-scale commercial application. The latter has a higher life cycle

energy footprint but consumes much less water, required less material for

construction, and achieved much higher algal biomass productivity. Consequently,

11 & 15 were favoured on the WF and GWP dimension.

Finally, option 11 edges 15 as the environmentally optimal production chain

amongst those considered in this study due to the minor advantages (NER and WF)

single stage chitosan flocculation had over two stage magnetophoretic harvesting-

chitosan flocculation. Consequently, open thin-layer photo-bioreactor cultivation,

single-step chitosan harvesting, belt drying, and dry direct in-situ trans-

esterification is found to be the environmentally optimal production chain in this

study. All in all, for FAME biodiesel produced from 100000 kg cultivated algal

biomass in the most environmentally optimal production chain, the WF and GWP are

found to be 4350 tonnes and 1470 tonnes respectively while the NER is 1.22.

31

5.7 Analysis of Environmentally Optimal Production Chain

As discussed in Section 5.6, the identified environmentally optimal production chain

(Figure 25) involves the following production stages:

Stage 1: open thin-layer photo-bioreactor cultivation,

Stage 2: single-step chitosan harvesting,

Stage 3: belt drying, and

Stage 4: direct in-situ trans-esterification

The NER for the optimal production chain is found to be 1.22. This meant that 0.82

GJ of fossil energy is required to produce every 1 GJ of microalgae-based biodiesel.

The findings are relatively consistent with those in the literature with

photobioreactors generally reported to have NER between 0.23 and 1.1 (see

Literature Review Section). The slightly higher NER at 1.22 could be attributed to the

global optimisation that is the crux this study.

The breakdown of the life cycle energy footprint for this production chain is shown

in Figure 26. As highlighted in the previous section, stages 3 and 4 are the more

energetically significant stages of this production chain, accounting for 76% of total

energy consumption on aggregate. More specifically, stage 3 (belt drying) accounted

for almost half of all energy used. This finding is consistent with similar EIAs involving

dry extraction production stages, such as Lardon et. al [37] and Xu et. al [54] where

Figure 25: Environmentally Optimal Microalgae-based Biodiesel Production Chain

32

the drying stage was found to be the most energetically significant segment of the

production chain. This finding implies that gains from innovation in drying

technology seem to be the quickest path to commercial viability for third-generation

biodiesel as wet conversion techniques have extremely low conversion efficiency at

this point.

On the WF front, the WF of 4350 tonnes of water for the assumed basis of biodiesel

produced from 100000 kg of algal biomass works out to 2.7 tonnes per GJ of

biodiesel. The findings have the same order of magnitude as those in the literature

(see Literature Review Section). The slightly smaller figure compared to those of

Yang et. al (16 tonnes per GJ) [51] and Delrue et. al (9.8 tonnes per GJ) [53] could

again be attributed to the optimisation process used in this study.

It is also interesting to note that the cultivation stage accounts for the overwhelming

majority of water use (97%). This is reflected in Figure 27. On first glance, this result

may seem contradictory to the findings from the Figure 26 where the drying step has

been shown to be the biggest energy consumer in the production chain. One would

infer that the drying stage would also be a large source of water outlay. However,

close scrutiny unveils the source of this apparent conflict. In stage 2, a 95% recycle

ratio was assumed that accounts for the recycle of water from the harvesting step

Figure 26: Life Cycle Energy Footprint for Environmentally Optimal Production Chain

Stage 124%

Stage 20%

Stage 349%

Stage 427%

Delivery0%

Total Energy

Stage 1

Stage 2

Stage 3

Stage 4

Delivery

33

and all steps downstream of it. Adjusting the assumption would drastically change

the proportion of WF borne by the cultivation stage. Again, this finding is consistent

with the EIAs in the literature. For instance, Yang et. al [51] attributes 100% of

freshwater usage to the cultivation stage when recycle ratio is 100%.

For GWP, the findings of 1470 tonnes of CO2-equivalent GHG for the assumed basis

of biodiesel produced from 100000 kg algal biomass work out to 0.9 tonnes of CO2-

equivalent GHG per GJ of biodiesel.

The stage-by-stage breakdown of the GWP is reflected in Figure 28. Only the

cultivation stage has a negative GWP, indicating that the net emission of CO2

equivalent GHG is negative. This is intuitive because cultivation is the only stage that

consumes CO2 (CO2 is a feedstock for algal growth) while all other stages emit CO2

and other greenhouse gases. Aggregating all stages, the net GWP is still significantly

positive.

Figure 27: Life Cycle Water Footprint for Environmentally Optimal Production Chain

97%

Water Footprint

Stage 1

Stage 2

Stage 3

Stage 4

Delivery

34

It is clear from Figure 28 that stage 4 (conversion) accounts for most of the GHG

emissions. This can be attributed to the life cycle emissions accruing from the

production of solvents and chemicals used in the conversion stage. More specifically,

the direct in-situ transesterification method in this study uses significant quanities of

methanol, sulphuric acid, and hexane that results in high GHG emissions from their

production. This is also the reason why the GWP findings of this study exceed that

found in the literature which peaks at 0.5 tonnes of CO2-equivalent GHG per GJ of

biodiesel (see Literature Review Section).

Finally, Figure 29 summarises the relative contribution of each stage to the 3

environmental dimensions under study. It is clear from Figure 29 that stage 2

(harvesting) is relatively insignificant and underpins the importance that research

attention is placed on stages 1, 3, and 4 to improve the environmental sustainabilty

of third-generation biodiesel. Specifically, research priority should be placed on

reducing energy used in biomass drying, reducing the water footprint of open thin-

layer cultivation methods, and reducing the use of GWP-heavy solvents in lipid

conversion. This is not to say that effort should not be spent on promising

technology such as wet conversion methods but that comparatively more effort

Stage 1 Stage 2 Stage 3 Stage 4 Delivery-200000

0

200000

400000

600000

800000

1000000

1200000

1400000

1600000

GW

P-1

00

a (k

g C

O2

eq

uiv

ale

nt)

GWP-100a

Figure 28: Life Cycle GWP for Environmentally Optimal Production Chain

35

should be placed on methods today that are in closer proximity to environmental

goals.

Comparing with the NER, WF, and GWP of petroleum diesel and soybean biodiesel

that have been discussed in the literature review segment, it is clear that third-

generation biodiesel still has some way to go before it can truly claim to be

environmentally sustainable. The NER of 1.22 clearly lags behind that of corn

biodiesel (3.22) although they do prove slightly less environmentally malignant than

petroleum diesel (0.83). The WF of 2.7 tonnes per GJ is in significant excess of that of

petroleum diesel (0.0098 tonnes per GJ) although it uses much less water than first-

generation biodiesel (32.2 tonnes). Finally, the GWP of 0.9 tonnes per GJ is far in

excess of both petroleum diesel (0.032 tonnes per GJ) and corn biodiesel (0.04

tonnes per GJ). Hence, today’s microalgae-based biodiesel production is some way

off its claims on environmental sustainability and definitely not an unambiguous

improvement over the status quo.

However, there is still much optimism with regards to third-generation biodiesel. For

instance, this study has pinpointed 3 aforementioned specific areas for researchers

and environmental policy makers to focus on to unlock the full potential of third-

generation biodiesel sooner rather than later.

-20%

0%

20%

40%

60%

80%

100%

Total Energy Water Footprint GWP-100a

Delivery

Stage 4

Stage 3

Stage 2

Stage 1

Figure 29: Summary of Contribution of Each Stage to the 3 Environmental Dimensions

36

6 Conclusion and Future Work

From the wide variation in NER, WF, and GWP findings across different options

considered at different production process steps, it is clear that some production

chains are much more environmentally benign than others.

Of all the production chains considered in this study, the environmentally optional

one involves open thin-layer photo-bioreactor cultivation, single-step chitosan

harvesting, belt drying, and direct in-situ trans-esterification. Using this production

chain, biodiesel produced from 100000 kg cultivated algal biomass has a WF and

GWP of 4350 tonnes of water and 1470 tonnes of CO2-equivalent GHG respectively

while the NER is 1.22. This works out to 2.7 tonnes of water used and 0.9 tonnes of

CO2-equivalent GHG produced per GJ of biodiesel.

With NER just over unity, large-scale microalgae-based biodiesel production remains

barely energetically feasible today despite its NER exceeding that of petroleum

diesel (0.83) [58]. Moreover, WF and GWP are well above that of an equivalent GJ of

petroleum diesel (0.0098 tonnes of water and 0.032 tonnes of GHG equivalent CO2

[58]). Consequently, large-scale microalgae-based biodiesel production today is

some distance from environmentally sustainable, contrary to conventional wisdom.

However, it is important to note that this study does not include novel industrial

solutions to reduce the environmental footprint of microalgae-based biodiesel

production. Localised solutions such as process integration [91, 92] and introduction

of novel additives [93] can further mitigate the environmental impact.

All in all, this project has accomplished the multiple objectives of:

Determining if third-generation biodiesel is indeed environmentally benign

given today’s technology;

Determining if all microalgae-based biodiesel production chains are equally

green; and

37

Identifying the production chain that is environmentally optimal if not all

methods are equally green.

This study pinpoints the areas in which future work in research and policy

development in biodiesel production should focus on to align with the objectives of

energy and environmental sustainability. This will allow the much-championed

environmental potential of microalgae-based biodiesel to be attained sooner rather

than later. The specific areas are:

Reducing energy used in biomass drying,

Reducing the water footprint of open thin-layer cultivation methods, and

Reducing the use of GWP-heavy solvents in lipid conversion

7 Supplementary Information

Working files, supporting documentation and modelling assumptions are available at

http://bit.ly/lca_eia.

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1 - 1

Annexes

Annex 1 – Definition of Terms

Net Energy Ratio: The ratio of the total energy produced over life cycle energy

required for the unit plant operation. Here, the total energy produced is taken as the

energy of biomass that is maximally producible.

NER = ∑ 𝐸𝑛𝑒𝑟𝑔𝑦 𝑃𝑟𝑜𝑑𝑢𝑐𝑒𝑑

∑ 𝐸𝑛𝑒𝑟𝑔𝑦 𝑟𝑒𝑞𝑢𝑖𝑟𝑒𝑚𝑒𝑛𝑡𝑠

Water Footprint: Total water required for and exhausted during the unit plant

operation.

Global Warming Potential: Total emissions from the unit plant operation that has

been converted to that of emission of an equivalent mass of CO2. The definition

used in the LCAs are based on the 2000 Intergovernmental Panel on Climate Change

Revision over a time horizon of 100 years. GWP is calculated through OpenLCA.

FAME: Fatty Acid Methyl Esters.

2 - 1

Annex 2 – Key Modelling Parameters Obtained from the Literature

Variables Raceway (Jorquera)

Flat Plate (Jorquera)

Tubular (Molina)

Tubular (Bahadur)

Thin Layer (Doucha)

Thin Layer (Doucha, Updated)

Thin Layer (Yoo)

Flat Plate (Cheng-Wu)

Enclosed (Olaizola)

Tubular (Richmond)

Heterotrophic (Li)

No. of Units in Entire System to produce 100 ton Dry Biomass

23 113 917 21076 241 39 182649 1015 250 1301 15

Volume of 1 unit of reactor (m3)

350 9.02 0.20 0.02 0.73 2.00 0.025 1.00 25.0 0.140 11.0

Volumetric productivity (kg/(m3d))

0.0350 0.270 1.50 0.650 1.56 3.57 0.0600 0.270 0.0440 1.50 1.70

Illuminated area/volume ratio (m-1)

3.08 20.1 75.4 76.4 75.3 112 60.3 20.4 4.00 115 0

Occupied area/volume ratio (m-1)

2.86 11.0 0.600 57.5 75.3 112 60.3 3.00 4.00 70.6 1.32

Biomass concentration (kg/m3)

0.350 2.70 3.96 3.96 30.0 40.0 3.84 0.245 0.268 2.00 14.2

Energy consumption (W/m3)

3.72 53.0 2000 2000 500 500 12.9 479 68.6 2000 7490

Table 3: Key Modelling Parameters Extracted or Calculated from Literature for Cultivation Section

2 - 2

Basis: Uduman et. al, 2010 Lower Bound of Output TSS (%)

Upper Bound of Output TSS (%)

Lower Bound of Yield (%)

Upper Bound of Yield (%)

Lower Bound of Concentration Factor

Upper Bound of Concentration Factor

Energy Requirement in kWh/m3

Chemical Requirements

Centrifugation

12.0 40.0 95.0

120 8.00

Filtration

Natural filtration

1.00 6.00

89 15.0 60.0 0.400

Pressure filtration

5.00 27.0

89 50.0 245 0.880

Tangential flow filtration

70.0 89.0 5.00 40.0 2.06

Gravity Sedimentation 0.500 1.50

95

16.0 0.100

Flocculation-flotation 1.00 6.00 80.0 90.0

15.0 Alum, Ferric sulphate

Electrophoresis techniques

Electrolytic coagulation

99.5

95.0

1.15 Aluminium electrodes

Electrolytic flocculation

90.0

0.331

Basis: Danquah et. al, 2009 Lower Bound of Output TSS (%)








Vacuum Filtration 0.500 18.0

89

5.90

Polymer Flocculation 0.042 15.0

95.0

14.8 Zetag and Al

Basis: Beach et. al, 2012 Lower Bound of Output TSS (%)








Chitosan Flocculation

22.0

95.0

0.142 Chitosan

Basis: Lee et. al, 2013 Lower Bound of Output TSS (%)








Magnetophoretic Harvesting 0.042 15.0

99.0

0.142 Chitosan and Fe3O4

Basis: Bosma et. al, 2003 Lower Bound of Output TSS (%)








Ultrasonic Aggregation 0.0450 0.900

92.0

20.0 19.2

Table 4: Key Modelling Parameters Extracted or Calculated from Literature for Harvesting Section

2 - 3

No Disruption Autoclave Bead-beating Microwave Ultrasound Osmotic Shock

Basis Lee et. al, 2010 Lee et. al, 2010 Bunge, 1992 Lee et. al, 2010 Lee et. al, 2010 Lee et. al, 2010

Energy Use (kWh/m3) 0 6.37 27.8 3.33 6.25 0

Lipid Extraction Efficiency (%) 5.00 9.00 15.6 16.3 8.33 9.33

Normalised Energy Use (kWh/% change in m3) 0 1.59 2.62 0.294 1.88 0

Normalised Energy Use (MJ/% change in m3) 0 5.74 9.43 1.06 6.76 0

Normalised Energy Use based on Assumed Target Extraction Efficiency (MJ) 0 125 3.22 26.2 147 0

Chemicals - - - - - 10% NaCl solution used

Chemical requirement - - - - - 100

Table 5: Key Modelling Parameters Extracted or Calculated from Literature for Cell Disruption Sub-section of Extraction Section

No Drying Spray Drying Freeze Drying Thermal Drying Drum Dryer Belt Dryer

Energy Required (kJ/kg) 0 4870 5400 4850 3916.8 3340.8

Source - Baker, 2005 Mujumdar, 2006 Mujumdar, 2006 Hassebrauck & Ermel, 1996 Hassebrauck & Ermel, 1996

Table 6: Key Modelling Parameters Extracted or Calculated from Literature for Drying Sub-section of Extraction Section

Type Solvent Solvent Supercritical CO2 Supercritical CO2

Source Lee et. al, 1998 Halim et. al, 2011 Andrich et. al, 2005 Mendes et. al, 1995

Duration 50.0 450 360 500

Agitation 800 800

Energy Requirement (GJ/tonne biomass) 23.6 99.4 27.0 52.0

Energy Requirement with non-Ambient Consideration (GJ/tonne biomass) 23.6 99.4 40.3 1200

Solvent Requirement (mL/g dried biomass) 250 75.0 - -

Solvents Chloroform to Methanol Hexane to isopropanol CO2 CO2

Table 7: Key Modelling Parameters Extracted or Calculated from Literature for Lipid Extraction Sub-section of Extraction Section

2 - 4

Description Solvents Data Source Solvent Ratio (in order, wrt

Biodiesel g:kg)

Solvent 1 Requirement

(g per kg biodiesel)







FAME Conversion

Yield (%)

Energy Requirement

(GJ per kg biodiesel)

Extraction - TransE (Wet)

Methanol, Sodium

Methoxide (Cat), HCl,

NaOH

Batan et. al, 2010

0.1:0.0125:0.0071:0.005:1

0.100 0.0125 0.00710 0.00500 51.12 0.00419

Extraction - TransE (Dry)

Methanol, Sodium

Methoxide (Cat), HCl,

NaOH

Batan et. al, 2010

0.1:0.0125:0.0071:0.005:1

0.100 0.0125 0.0071 0.00500 63.7 0.00333

Direct In Situ (Wet)

Methanol, Sulphuric

Acid, Solvent: Hexane

Johnson, 2009

(3.4/0.791):(0.6/1):(4/X):(1/1000 x 0.296 x 0.95)

15300 2130 21600 - 8.45 0.0285

Direct In Situ (Dry)

Methanol, Sulphuric

Acid, Solvent: Hexane

Johnson, 2009

(3.4/0.791):(0.6/1):(4/X):(1/1000 x 0.296 x 0.95)

15300 2130 21600 - 72.79 0.00348

Table 8: Key Modelling Parameters Extracted or Calculated from Literature for Conversion Section

3 - 1

Annex 3 – Options Considered for Harvesting Production Step

The 45 options considered are listed in Table 9.

S/N Stage 1 Stage 2

1 Pressure filtration

2 Tangential flow filtration

3 Electrolytic coagulation

4 Electrolytic flocculation

5 Chitosan Flocculation

6 Centrifugation Pressure filtration

7 Centrifugation Tangential flow filtration

8 Centrifugation Electrolytic coagulation

9 Centrifugation Electrolytic flocculation

10 Centrifugation Chitosan Flocculation

11 Natural filtration Pressure filtration

12 Natural filtration Tangential flow filtration

13 Natural filtration Electrolytic coagulation

14 Natural filtration Electrolytic flocculation

15 Natural filtration Chitosan Flocculation

16 Gravity Sedimentation Pressure filtration

17 Gravity Sedimentation Tangential flow filtration

18 Gravity Sedimentation Electrolytic coagulation

19 Gravity Sedimentation Electrolytic flocculation

20 Gravity Sedimentation Chitosan Flocculation

21 Flocculation-flotation Pressure filtration

22 Flocculation-flotation Tangential flow filtration

23 Flocculation-flotation Electrolytic coagulation

24 Flocculation-flotation Electrolytic flocculation

25 Flocculation-flotation Chitosan Flocculation

26 Vacuum Filtration Pressure filtration

27 Vacuum Filtration Tangential flow filtration

28 Vacuum Filtration Electrolytic coagulation

29 Vacuum Filtration Electrolytic flocculation

30 Vacuum Filtration Chitosan Flocculation

31 Polymer Flocculation Pressure filtration

32 Polymer Flocculation Tangential flow filtration

33 Polymer Flocculation Electrolytic coagulation

34 Polymer Flocculation Electrolytic flocculation

35 Polymer Flocculation Chitosan Flocculation

36 Magnetophoretic Harvesting Pressure filtration

37 Magnetophoretic Harvesting Tangential flow filtration

38 Magnetophoretic Harvesting Electrolytic coagulation

39 Magnetophoretic Harvesting Electrolytic flocculation

40 Magnetophoretic Harvesting Chitosan Flocculation

41 Ultrasonic Aggregation Pressure filtration

42 Ultrasonic Aggregation Tangential flow filtration

43 Ultrasonic Aggregation Electrolytic coagulation

44 Ultrasonic Aggregation Electrolytic flocculation

45 Ultrasonic Aggregation Chitosan Flocculation Table 9: Options Considered for Harvesting Segment of Production Chain

3 - 2

The energy comparison for the harvesting step is enabled by the dewatering energy

usage per unit volume listed in the literature. The main papers used to obtain the

harvesting parameters are: Uduman et. al [68], 2010, Danquah et. al [10], 2009,

Beach et. al [71], Lee et. al [75], and Bosma et. al [72]. Both common dewatering

techniques and novel ones (Magnetophoretic) were considered.

4 - 1

Annex 4 – Options Considered for Extraction Production Step

There are 3 sub-stages considered within the extraction production step. They are: cell disruption, drying, and lipid extraction. 6 options are considered in cell disruption, 6 in drying, and 4 in lipid extraction. These are listed in Tables 10 to 12, resulting in a total of 6 x 6 x 4 = 144 combinations in the extraction step.

S/N Disruption Sub-Stage

1 No Disruption

2 Autoclave disruption

3 Bead-beating

4 Microwave disruption

5 Ultrasound disruption

6 Osmotic shock Table 10: Options in Disruption Sub-Stage

S/N Drying Sub-Stage

1 No drying

2 Spray drying

3 Freeze drying

4 Thermal drying

5 Drum drying

6 Belt drying Table 11: Options in Drying Sub-Stage

S/N Lipid Extraction Sub-Stage

1 Solvent extraction (Lee et. al)

2 Solvent Extraction (Halim et. al)

3 Supercritical CO2 extraction (Andrich et. al)

4 Supercritical CO2 extraction (Mendes et. al) Table 12: Options in Lipid Extraction Sub-Stage

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Annex 5 – Options Considered for Conversion Production Step

The 4 options considered for the conversion production step are listed in Table 13.

S/N Conversion Stage

1 Wet post extraction transesterification (Batan et. al, 2010)

2 Dry post extraction transesterification (Batan et. al, 2010)

3 Wet direct in-situ transesterification (Johnson et. al, 2009)

4 Dry direct in-situ transesterification (Johnson et. al, 2009) Table 13: Options in Conversion Sub-Stage

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Annex 6 – Project Timeline

This project has taken 5 months to its present state. Table 14 indicates the milestones that either have been achieved during this time or will

be achieved in the coming months.

S/N. Milestone Date

1 Completion of Cultivation EIA Studies 31 August 2013

2 Completion of Harvesting EIA Studies 24 September 2013

3 Completion of Interim Report 17 October 2013

4 Completion of Extraction Studies 14 November 2013

5 Completion of Conversion LCA Study 6 December 2013

6 Completion of Environmental Impact Analysis for entire Microalgae-based Biodiesel Production Chain 18 December 2013

7 Completion of FYP Project Review 24 December 2013

8 Completion of Draft FYP Final Report 31 December 2013

9 Projected Completion and Submission of Finalised FYP Final Report 15 January 2014

10 Projected Completion of Draft FYP Presentation 10 February 2014

11 Projected Completion and Submission of Finalised FYP Presentation 28 February 2014

12 Projected Completion of Draft FYP Poster 7 March 2014

13 Projected Completion and Submission of Finalised FYP Poster 14 March 2014 Table 14: Milestone Dates

Combinatorial Life Cycle Analysis (Environmental Impact Assessment) of Microalgae-based Biodiesel...

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