Energy Conversion and Management 50 (2009) 1426ndash1435

Contents lists available at ScienceDirect

Energy Conversion and Management

journal homepage wwwelsevier comlocate enconman

Experimental and modeling analysis of a batch gasificationpyrolysis reactor

P Baggio a M Baratieri b L Fiori a M Grigiante a D Avi c P Tosi c

a Department of Civil and Environmental Engineering University of Trento Via Mesiano 77 38050 Trento Italyb Faculty of Science and Technology Free University of Bolzano Sernesi street 1 39100 Bolzano Italyc Department of Physics University of Trento Via Sommarive 14 38050 Trento Italy

a r t i c l e i n f o a b s t r a c t

Article historyReceived 1 October 2007Received in revised form 24 July 2008Accepted 8 March 2009Available online 5 April 2009

KeywordsBatch reactorGasificationThermodynamicsChemical equilibrium

0196-8904$ - see front matter 2009 Elsevier Ltd Adoi101016jenconman200903004

Corresponding author Tel +39 0471017127 faxE-mail address marcobaratieriunibzit (M Barat

This paper presents some experimental results about biomass gasification obtained with a bench-scalegasifier consisting of an indirectly heated batch reactor inserted in a high temperature furnace Sprucewood has been used as feedstock in different forms and sizes (as pellets and sawdust) The experimentalactivity includes the analysis of the thermal response of the system (using an inert bed material) and thecharacterization of the gasification products The yield of the gaseous compounds found in the syngas hasbeen measured using an on-line gas-chromatography technique The experimental results have beencompared against calculations obtained by applying a thermochemical equilibrium model improved topredict both the gas and the solid phase product yields The experimental (average) yield of reactionproducts has been found to be in a satisfying agreement with the thermodynamic model

2009 Elsevier Ltd All rights reserved

1 Introduction

In the current energetic outlook one of the strategies pursued isthe energy generation from renewable sources in order to reducethe fossil fuels dependence and to rely on environmental-compat-ible technologies In this context the biomass gasification processis considered as a viable fuel upgrading path allowing the transfor-mation of a solid fuel into a gaseous energy vector (ie syngas)This chance is particularly interesting for the current scenario inwhich small and medium size conversion plant technologies couldbe integrated in a distributed energy generation model that is ex-pected to increase its diffusion In this perspective the biomass gas-ification represents an alternative to improve innovative energyprocesses in particular for small rural communities In fact thelarge amount of waste biomass widely spread on rural territorieslooks promising for syngas production as fuel for electric powergeneration by direct utilization in conventional internal combus-tion engines or after clean up and reforming stages for innovativegeneration systems as fuel cells This work presents the experi-mental results coming from a biomass gasification pilot plantand the assessment of its performance based on an equilibriummodeling approach

The proposed work refers specifically to experimental activitiescarried out on a small allothermic batch reactor in line with otherstudies reported in recent literature [1] The apparatus designedby the authors for the purpose of the present research is presentedin detail The configuration of the reactor allows to investigate the

ll rights reserved

+39 0471017009ieri)

thermal response of the system itself and the energy transportphenomena occurring between the reactor shell and the bed Therole of the thermal contact resistance (reactorbed) has been inves-tigated This is noteworthy noting that both modeling [23] andexperimental works [4] concerning pyrolysis and gasificationinvestigations focus on the heat transfer phenomena affecting sin-gle particles This approach does not take into account the generalview of the process that is controlled by heat transfer at a largerscale including contact resistance with the reactor wall Otherstudies provide experimental thermal analysis applied to combus-tion furnaces [5] or auto-thermal pyrolysis-combustion systems[67] while these studies focus on allothermic conversionprocesses

An enhanced thermochemical equilibrium model has beenimplemented to describe the gasificationpyrolysis conversionyields The adopted methodology based on the Gibbs energy min-imization approach does not require the selection of the lsquolsquorepresen-tativerdquo chemical reactions Nevertheless only the list of thechemical species inclusive of the ones expected in the product mix-ture must be assigned This model previously developed andwidely detailed [8] represents an effective engineering tool to sup-port the design of actual biomass conversion plants Other ap-proaches to estimate the product composition are available suchas kineticdynamic models [910] and neural network applications[11] allowing in some instances to obtain a better accuracy Never-theless the proposed approach presents a high predictive charac-ter and does not require an extended set of data to train themodel itself More the used solidndashgas formulation represents animprovement inside the traditional equilibrium calculations gener-ally based on a single gas phase formulation [12ndash18] Similar

P Baggio et al Energy Conversion and Management 50 (2009) 1426ndash1435 1427

applications have been presented in the literature only recently[19ndash23] The contribution of this paper encompass experimentalresults theoretical analysis and model validation and shows alsothat slow batch gasification processes can be treated in terms ofthe chemical reaction equilibrium

2 Gasification equipment

21 Overview

An experimental bench-scale apparatus for biomass gasificationand pyrolysis has been designed it consists of an indirectly heatedbatch reactor operating at variable pressure (1ndash7 barg) set in anexternal furnace capable of reaching temperatures up to 1000 C[24]

The reactor (Fig 1) is inserted from above in the laboratorycylindrical furnace open on the top side where all the controland acquisition devices are connected

The vessel half filled of feeding material (biomass) can be con-tinuously purged with nitrogen to sweep the product gases fromthe reaction zone either to avoid high concentration of the prod-ucts in the effluent gas or to reduce the extent of secondary reac-tions The temperature is monitored by a set of thermocouplesplaced in the reactor

In order to assess the yields of the syngas and its composition asa function of the parameters able to influence the process principalreactions the characterization of the effluent gas has been per-formed by means of gas-chromatographic analysis on gas samples

22 Reactor

The reactor consists of a cylindrical-shaped Incoloy vessel hav-ing internal diameter of 142 mm height of 400 mm thickness of4 mm and internal net volume of 63 l Two blind flanges(210 mm diameter and 20 mm thickness) made of the same mate-rial seal the tubular reactor on the top and bottom sides

The top flange is equipped with eight pipes (diameter10 mm length 300 mm) used both for inserting the measureand control devices and for the gas inlets and outlets on the same

Fig 1 Gasification equipment overview (top left) pit furnace (top rig

flange there is also a central flue gas discharge duct provided witha ball valve

The total reactor height including flanges and ancillary pipes isequal to 746 mm The reactor air-tightness is assured by 12 boltsand nuts fastened on each flange moreover union sleeves withthreads have been welded on the top of all the inletoutlet tubesin order to connect the necessary measure probes

The reactor has been manufactured using Incoloy 800 (30Ni21Cr 46Fe and other metals in traces) an alloy suitable for oper-ating at high temperatures and pressures This is a material widelyused for construction of equipment requiring high corrosion resis-tance heat resistance strength and stability for service up to800 C At high temperatures it offers resistance to oxidation car-burization and sulfidation along with rupture and creep strengthThe reactor design parameters are a maximum temperature of800 C at 3 barg pressure and 700 C at 7 barg

23 Furnace

The reactor is inserted in a pit furnace with a circular cross-sec-tion of 100 cm (external diameter) and 98 cm height the net inter-nal volume is about 51 l with 33 cm diameter and 60 cm heightThe furnace is open at the top side with a 21 cm circular holeThere is an insulation layer (335 cm thick) that keeps the externalshell at low temperature (lower than 60 C) moreover the internalchamber is lined by a coat of refractory material The reactor can behandled by means of an electric cable hoist

The control panel is installed on the right side of the furnace andincludes the master switch the heating switch the hoist switchtwo temperature controllers an amperometer a voltmeter and atemperature safety alarm

The heating is provided by several electric resistances (maxi-mum power 105 kW at 230 V 50 Hz) up to a maximum worktemperature of 1000 C The power supplied to the resistances isregulated by a PID temperature controller (Fuji PX3 series) control-ling both the maximum temperature (ie the desired temperature)and the heating rate (temperature ramp) After selecting the heat-ing rate and the set point (it is possible to select up to 8 differentset points and as many ramp for the same experimental run) the

ht) reactor (bottom right) and reactor design data (bottom left)

1428 P Baggio et al Energy Conversion and Management 50 (2009) 1426ndash1435

furnace begins the operation cycle using a constant power propor-tional to the heating rate value until the temperature is close tothe set point and then it reaches the set value (and keeps it stable)by means of a pulse width modulation (PWM) control strategyPulse-width modulation control works by switching the powersupplied to the furnace electric resistors (ie the heating system)with a variable duty cycle

A second safety temperature controller is installed on the fur-nace panel cutting the power to the resistors in case the tempera-ture exceeds the safety temperature set point (usually selected20 C above the first controller set point)

The temperature is measured using two K-type thermocouplesinsulated by a refractory sheath and inserted into the furnacethrough the insulation layer at 26 cm from the top side of thefurnace

Table 1TH-type experimental runs

Run Load Initial mass (g) Maximum temperature (C)

1TH Gravel Wet 2450 6002TH Gravel Dry 2450 6003TH Empty ndash ndash 6004TH Gravel Dry 2450 6005TH Sand Wet 2500 6006TH Sand Dry 2500 600

24 Data acquisition unit and measurement devices

The data acquisition unit consists of an Agilent34970A digitalmultimeter (DMM 6frac12 accuracy) with a 20-channel plug-in moduleconnected to a PC by means of a RS-232 port

For the temperature measurements inside the reactor K-typethermocouples (ChromelndashAlumel) have been used because theyare well suited for corrosive and oxidizing environments and havea wide operating temperature range between200 C and 1370 C(accuracy range plusmn11 C to plusmn22 C) the DMM internal referencejunction has been used since its accuracy (05 C) is of the same or-der of magnitude of the K-type thermocouples

To measure the temperature and its variation during the exper-imental runs four thermocouples have been used placed asfollows

1 lsquofurnacersquo between the internal furnace wall and the externalreactor wall to measure the furnace heating rate

2 lsquowallrsquo on the external reactor wall (usually at 50 mm from thereactor bottom) to measure the wall heating rate

3 lsquobedrsquo in the biomass bed (usually at 30 mm from the reactorbottom) to measure the feedstock thermal behavior

4 lsquointernalrsquo in the upper side of the reactor (at 130 mm from thereactor top) to measure of the syngas temperature

Two different kinds of thermocouple junctions have been usedlsquobare wirersquo (for the lsquofurnacersquo thermocouple) and sheathed (for thelsquowallrsquo lsquobedrsquo and lsquointernalrsquo ones) The former typology consists ofChromel and Alumel wires (with glass fiber insulation) twisted to-gether at the tip so that a good mechanical contact is achievedSheathed thermocouples have a welded junction protected by anInconel600 sheath (diameter 32 mm and length 500ndash800 mm)with mineral oxide insulation these thermocouples (usuallynamed MIMS ie mineral-insulated metal-sheathed) have theadvantage of the protection offered by a metal-sheath while stillretaining a reasonable amount of flexibility

All the thermocouples have been calibrated before and after theexperimental runs by means of a precision bath (Hart Scientificseries) and a reference Platinum Resistance thermometer (PT100)

A 90 cm long tube connected to the central reactor outlet (lo-cated on the top flange) acts as discharge duct The dischargeduct is open to the atmosphere and the tar compounds tendto condense on the chimney cold surfaces thus a small container(for the condensate) has been located at the effluent dischargepoint The gas sampling line consists of a 140 stainless steel tubeconnected to the discharge duct and equipped with a samplingvalve and a diaphragm pump (N 820 KNF series max flow rate20 lmin) to let the desired syngas amount to flow towards thegas analysis unit

Between the gas sampling valve and the gas analyzer a tar trapand a particulate filter have been installed in particular a filterflask collects the liquid coming from a condensation coil immersedin a cooling water tank while the remaining gases are piped to theanalyzing unit

When nitrogen has been used as the inert gas carrier in order tocontinuously purge the vessel a flowmeter (MKS Instruments ser-ies operating range 2ndash20000 Scm3) has been installed betweenthe gas cylinder and the reactor inlet

The analyzing unit is an Agilent micro gas-chromatograph(3000 micro-GC series repeatability less than 05 RSD at constantpressure and temperature) well suited for refinery and reformergas analysis It consists of two complementary chromatographicchannels each consisting of an injection device a separation col-umn and a detector that analyze a portion of sample in parallelIn the performed experiments Argon has been used as inert gascarrier detecting nine different gaseous compounds H2 O2 N2CH4 CO CO2 C2H4 C2H6 and C2H2

3 Thermal analysis of the system

31 Experimental

To characterize the thermal response of the system (furnace andreactor) six runs (named lsquolsquoTH-typerdquo) have been carried out at dif-ferent operating conditions as summarized in Table 1 In theseexperiments the reactor was filled with inert materials (graveland sand) or empty (test 3TH)

All the experiments have been performed at atmospheric pres-sure and at peak temperatures of 600 C The same temperaturemeasurement system has been used for all the runs

The characterization of the system thermal response is animportant issue for the design of pyrolysis or gasification reactorssince the syngas production processes (gasification or pyrolysis ofsolid matter) need a significant energy supply to support the con-version reactions (usually endothermic) the heat transfer phe-nomenon between the vessel and the bed material subjected tothe thermal treatment is of primary importance for the conversionprocess efficiency Thus the experimental data have been used totest the application of a finite element model for the analysis ofthe temperature distribution inside the bed and of the contactthermal resistance between the bed material and the reactor wall

These experiments have been carried out using an inert feed-stock with granulometry similar to that of the biomass used inthe actual experimental runs In particular two different types ofstony material have been utilized gravel (diameters between 6and 10 mm) and sand (diameters between 1 and 3 mm) similarin dimension to wood pellets and sawdust respectively

The ambient temperature (ie the temperature of the externalenvironment Tamb) was in the range 17ndash22 C and the peak valueof 600 C was reached by means of a heating rate equal to approx-imately 30 C min1

Fig 2a and b shows the results of the thermal analysis carriedout with the empty reactor (3TH) and with the reactor filled (par-

0

100

200

300

400

500

600

700

time [s]

Tem

pera

ture

[degC

]

T bottom

T internal

T wall

T furnace

0

100

200

300

400

500

600

700

0 2000 4000 6000 8000 10000 12000

0 2000 4000 6000 8000 10000 12000time [s]

Tem

pera

ture

[degC

]

T bed

T internal

T wall

T furnace

Fig 2 Thermal field behavior (a) empty reactor 3TH run and (b) dried gravel 4THrun

0

50

100

150

200

250

300

0 500 1000 1500 2000 2500 3000 3500

time [s]

Tem

pera

ture

[degC

]

T pellets(1BIO)

T sawdust(2BIO)

T gravel -wet (1TH)

T gravel -dry (4TH)

T sand -dry (6TH)

T sand - wet (5TH)

Fig 3 Thermal field behavior comparison between inert and reactant materials

Table 2Experimental runs

Run Load Initial mass (g) Maximum temperature (C)

1BIO Pellets Wet 375 6002BIO Sawdust Wet 150 6003BIO Sawdust Wet 300 6004BIO Sawdust Wet 300 8005BIO Sawdust Dry 285 8006BIOa Sawdust Wet 300 8007BIOa Sawdust Wet 300 800

a Use of N2 flux

P Baggio et al Energy Conversion and Management 50 (2009) 1426ndash1435 1429

tially) with dried gravel (4TH sample mass of 2450 g bed volumeof 154 dm3 bed height of 100 mm)

The first figure shows the variation of the thermal field for theempty vessel (3TH run) During the initial transient at the begin-ning the temperature increase affects the external wall of the reac-tor (Twall) then the internal gases (air and Tinternal) and finally theinternal surface of the reactor bottom flange (Tbottom) After reach-ing stationary conditions (more than 9000 s runtime) all the tem-peratures tend to the set point value (600 C asymptoticbehavior) except for the Tinternal that is affected by some heatlosses from the reactor upper flange

Comparing Fig 2a (empty) and b (gravel bed) it is worth men-tioning the difference between Tbottom and Tbed showing a signifi-cant delay of the gravel thermal response (Tbed measured at30 mm from the vessel bottom and 30 mm from the internal wall)this is due to the low bulk thermal conductivity of the gravel bedthat has a high void fraction Moreover it is interesting to observethe deviation of Twall at 1800 s runtime ndash Fig 2b ndash (measured onthe external reactor wall at 50 mm from the bottom flange) withrespect to Twall ndash Fig 2a ndash since under the same conditions (iesame available furnace thermal power) the global system thermalcapacity is greater because of the presence of the gravel mass

In Fig 3 the thermal behavior of different materials is reportedThe curves of the inert wet materials (gravel ndash 1TH and sand ndash5TH) are initially steeper than the ones of the inert dry materials(4TH and 6TH) due to the higher conductivity of the former com-pared to the latter Increasing the temperature (70ndash100 C) moistureevaporation lowers the slope of the heating curves (because of theapparent thermal capacity due to the latent heat of vaporization)

A similar behavior has been observed also for wet biomass feed-stocks (1BIO and 2BIO ndash refer to Table 2) in particular the woodsawdust heating curve is characterized by an inflexion point witha positive first derivative while for the pellets curve the corre-sponding inflexion point has a first derivative value close to zeroThis can be explained by the different lsquostatusrsquo of the moisture inthe bed material for gravel sand and sawdust the moisture ismostly present as hygroscopic water in the interstices while forthe pellets there is a significant water fraction bonded inside thesingle grain In fact the densification processes used for pelletsmanufacturing are carried out using starch which causes a partialsintering of the pellet external surface

The higher temperature increase of pellets and sawdust (withrespect to sand and gravel) is due to the different amount of mate-rial loaded into the vessel

32 Thermal modeling

As mentioned before the experimental measurements havebeen compared with the results of a thermal simulation of the sys-tem carried out through a finite element thermal model

The whole system has been discretized using a 2D-axisymmet-ric geometry Fig 4 shows the geometry of the system highlightingthe points where experimental and simulated data have beencompared

The Partial Differential Equations (PDErsquos) discretization andsolution procedure has been performed by means of the ComsolMultiphysics [25] commercial software As initial condition anuniform temperature (ambient temperature Tamb) has been as-signed to the whole computational domain assuming that the ini-tial state of an experimental run corresponds to the ambienttemperature For the reactor external wall a linear temperaturechange in time has been imposed interpolating the experimental

Fig 4 Finite element model geometric structure

1430 P Baggio et al Energy Conversion and Management 50 (2009) 1426ndash1435

temperature data (Twall) The average value of the increasing tem-perature ramp deduced from the experimental data is approxi-mately equal to a rate h = 32 C min1

On the bottom flange of the reactor an adiabatic condition hasbeen assumed while on the upper flange a natural convection fluxboundary condition has been chosen (heat transfer coefficienth = 8 Wm2 K1) with the external air at Tamb The symmetry axis(r = 0) is assumed adiabatic

The model has been calibrated in order to simulate the bed re-sponse adjusting the (simulated and constant) bulk thermal con-ductivity (kbed) with a best fitting procedure in order to obtain agood agreement with the experimental data (ie Tbed at a distanceof 30 mm from the internal wall of the vessel) values equal to 032and 035 Wm1 C1 for gravel and sand respectively have beenobtained

Moreover a further best fitting procedure has been carried outconsidering a variable bed thermal conductivity (ie a linear and aquadratic function of the temperature) The resulting conductivityvalues that vary between 02 and 04 Wm1 C1 give a bettermodel calibration (Fig 5) and are in agreement with some experi-mental data available in literature (eg Deru 2003 found that

0

100

200

300

400

500

600

700

0 3000 6000 9000 12000 15000

time [s]

Tem

pera

ture

[degC

]

experimental data

model estimate (linear)

model estimate (quadratic)

[ λbed = a + bT ]

[ λbed = c + dT2]

Fig 5 Finite element analysis gravel thermal conductivity estimate (a =015 Wm1 C1 b = 255 104 Wm1 C2 c = 019 Wm1 C1 and d =300 107 Wm1 C3)

gravel thermal conductivity is an increasing function of the tem-perature [26])

In addition the contact resistance between the inert bed mate-rial and the reactor internal wall has been estimated This param-eter represents an additional resistance to the heat transfer and iscaused by the contact between different materials (gravel or sandand Incoloy) For its calculation the mixed bed composition (inertand air) in contact with the steel wall has been taken into accountThis resistance has been simulated inserting an additional layer(2 mm thick) inside the reactor shell The calibration of this modelagainst experimental data gives values of the thermal resistancearound 014 and 013 C W1 for the gravel and the sand bedrespectively [27]

These contact resistance values have been also comparedagainst the value calculated using both a simplified model (lumpedcapacitance method) [27] and the correlation suggested by Holman[28] obtaining a good agreement (respectively 018 and0135 C W1 for gravel) In conclusion for the considered scenar-ios the contact thermal resistance can be safely considerednegligible

4 Gasification activities

41 Experimental procedures

The gasification experimental activity has been carried out byfilling the reactor with spruce wood biomass (pellets or sawdust)and performing seven runs (named lsquolsquoBIO-typerdquo) at the selectedoperating conditions summarized in Table 2

All the experiments have been performed at atmospheric pres-sure and at peak temperatures of 600 C or 800 C The same tem-perature measurement system has been used for all the runs whilegas analysis has been performed only for experiments 4ndash7BIO car-ried out on spruce sawdust The 6BIO and 7BIO runs have beenconducted using nitrogen to continuously purge the vessel

Spruce sawdust has been used as feedstock for the 2ndash7BIO runsonly a test has been carried out on spruce pellets (1BIO) to charac-terize the thermal field behavior and without performing any gasanalysis

The sawdust has been characterized by means of its ultimateanalysis and measuring its moisture and ash content The ultimateanalysis results (expressed in terms of daf mass fractions ie ondry ash free basis) are in good agreement with other experimentaldata available in literature (Table 3) In particular Phyllis [29] andBIOBIB [30] databases have been chosen as they contain a largenumber of information on the composition of biomass (grass andwood) and waste These databases are useful for analysis of indi-vidual available biomass or waste materials and offer the possibil-ity to obtain an average composition of any combination ofdifferent materials

The moisture content has been measured using a precision ther-mobalance (Eurotherm series plusmn002 accuracy) performing several

Table 3Spruce sawdust characterization

( mass daf) Phyllis BIOBIB Measured

C 5010 4953 5158H 600 577 590Oa 4390 4450 4244N 000 019 008S 000 001 000

Moisture ( mass wet) 825Ash ( mass wet) 090Bulk density (kg m3) 192

a Measured by difference

0

100

200

300

400

500

600

700

time [s]

Tem

pera

ture

[degC

]

T bottom

T internal

T wall

T furnace

0

100

200

300

400

500

600

700

0 2000 4000 6000 8000 10000 12000

0 2000 4000 6000 8000 10000 12000time [s]

Tem

pera

ture

[degC

]

T bed

T internal

T wall

T furnace

Fig 2 Thermal field behavior (a) empty reactor 3TH run and (b) dried gravel 4THrun

0

50

100

150

200

250

300

0 500 1000 1500 2000 2500 3000 3500

time [s]

Tem

pera

ture

[degC

]

T pellets(1BIO)

T sawdust(2BIO)

T gravel -wet (1TH)

T gravel -dry (4TH)

T sand -dry (6TH)

T sand - wet (5TH)

Fig 3 Thermal field behavior comparison between inert and reactant materials

Table 2Experimental runs

Run Load Initial mass (g) Maximum temperature (C)

1BIO Pellets Wet 375 6002BIO Sawdust Wet 150 6003BIO Sawdust Wet 300 6004BIO Sawdust Wet 300 8005BIO Sawdust Dry 285 8006BIOa Sawdust Wet 300 8007BIOa Sawdust Wet 300 800

a Use of N2 flux

P Baggio et al Energy Conversion and Management 50 (2009) 1426ndash1435 1429

tially) with dried gravel (4TH sample mass of 2450 g bed volumeof 154 dm3 bed height of 100 mm)

The first figure shows the variation of the thermal field for theempty vessel (3TH run) During the initial transient at the begin-ning the temperature increase affects the external wall of the reac-tor (Twall) then the internal gases (air and Tinternal) and finally theinternal surface of the reactor bottom flange (Tbottom) After reach-ing stationary conditions (more than 9000 s runtime) all the tem-peratures tend to the set point value (600 C asymptoticbehavior) except for the Tinternal that is affected by some heatlosses from the reactor upper flange

Comparing Fig 2a (empty) and b (gravel bed) it is worth men-tioning the difference between Tbottom and Tbed showing a signifi-cant delay of the gravel thermal response (Tbed measured at30 mm from the vessel bottom and 30 mm from the internal wall)this is due to the low bulk thermal conductivity of the gravel bedthat has a high void fraction Moreover it is interesting to observethe deviation of Twall at 1800 s runtime ndash Fig 2b ndash (measured onthe external reactor wall at 50 mm from the bottom flange) withrespect to Twall ndash Fig 2a ndash since under the same conditions (iesame available furnace thermal power) the global system thermalcapacity is greater because of the presence of the gravel mass

In Fig 3 the thermal behavior of different materials is reportedThe curves of the inert wet materials (gravel ndash 1TH and sand ndash5TH) are initially steeper than the ones of the inert dry materials(4TH and 6TH) due to the higher conductivity of the former com-pared to the latter Increasing the temperature (70ndash100 C) moistureevaporation lowers the slope of the heating curves (because of theapparent thermal capacity due to the latent heat of vaporization)

A similar behavior has been observed also for wet biomass feed-stocks (1BIO and 2BIO ndash refer to Table 2) in particular the woodsawdust heating curve is characterized by an inflexion point witha positive first derivative while for the pellets curve the corre-sponding inflexion point has a first derivative value close to zeroThis can be explained by the different lsquostatusrsquo of the moisture inthe bed material for gravel sand and sawdust the moisture ismostly present as hygroscopic water in the interstices while forthe pellets there is a significant water fraction bonded inside thesingle grain In fact the densification processes used for pelletsmanufacturing are carried out using starch which causes a partialsintering of the pellet external surface

The higher temperature increase of pellets and sawdust (withrespect to sand and gravel) is due to the different amount of mate-rial loaded into the vessel

32 Thermal modeling

As mentioned before the experimental measurements havebeen compared with the results of a thermal simulation of the sys-tem carried out through a finite element thermal model

The whole system has been discretized using a 2D-axisymmet-ric geometry Fig 4 shows the geometry of the system highlightingthe points where experimental and simulated data have beencompared

The Partial Differential Equations (PDErsquos) discretization andsolution procedure has been performed by means of the ComsolMultiphysics [25] commercial software As initial condition anuniform temperature (ambient temperature Tamb) has been as-signed to the whole computational domain assuming that the ini-tial state of an experimental run corresponds to the ambienttemperature For the reactor external wall a linear temperaturechange in time has been imposed interpolating the experimental

Fig 4 Finite element model geometric structure

1430 P Baggio et al Energy Conversion and Management 50 (2009) 1426ndash1435

temperature data (Twall) The average value of the increasing tem-perature ramp deduced from the experimental data is approxi-mately equal to a rate h = 32 C min1

On the bottom flange of the reactor an adiabatic condition hasbeen assumed while on the upper flange a natural convection fluxboundary condition has been chosen (heat transfer coefficienth = 8 Wm2 K1) with the external air at Tamb The symmetry axis(r = 0) is assumed adiabatic

The model has been calibrated in order to simulate the bed re-sponse adjusting the (simulated and constant) bulk thermal con-ductivity (kbed) with a best fitting procedure in order to obtain agood agreement with the experimental data (ie Tbed at a distanceof 30 mm from the internal wall of the vessel) values equal to 032and 035 Wm1 C1 for gravel and sand respectively have beenobtained

Moreover a further best fitting procedure has been carried outconsidering a variable bed thermal conductivity (ie a linear and aquadratic function of the temperature) The resulting conductivityvalues that vary between 02 and 04 Wm1 C1 give a bettermodel calibration (Fig 5) and are in agreement with some experi-mental data available in literature (eg Deru 2003 found that

0

100

200

300

400

500

600

700

0 3000 6000 9000 12000 15000

time [s]

Tem

pera

ture

[degC

]

experimental data

model estimate (linear)

model estimate (quadratic)

[ λbed = a + bT ]

[ λbed = c + dT2]

Fig 5 Finite element analysis gravel thermal conductivity estimate (a =015 Wm1 C1 b = 255 104 Wm1 C2 c = 019 Wm1 C1 and d =300 107 Wm1 C3)

gravel thermal conductivity is an increasing function of the tem-perature [26])

In addition the contact resistance between the inert bed mate-rial and the reactor internal wall has been estimated This param-eter represents an additional resistance to the heat transfer and iscaused by the contact between different materials (gravel or sandand Incoloy) For its calculation the mixed bed composition (inertand air) in contact with the steel wall has been taken into accountThis resistance has been simulated inserting an additional layer(2 mm thick) inside the reactor shell The calibration of this modelagainst experimental data gives values of the thermal resistancearound 014 and 013 C W1 for the gravel and the sand bedrespectively [27]

These contact resistance values have been also comparedagainst the value calculated using both a simplified model (lumpedcapacitance method) [27] and the correlation suggested by Holman[28] obtaining a good agreement (respectively 018 and0135 C W1 for gravel) In conclusion for the considered scenar-ios the contact thermal resistance can be safely considerednegligible

4 Gasification activities

41 Experimental procedures

The gasification experimental activity has been carried out byfilling the reactor with spruce wood biomass (pellets or sawdust)and performing seven runs (named lsquolsquoBIO-typerdquo) at the selectedoperating conditions summarized in Table 2

All the experiments have been performed at atmospheric pres-sure and at peak temperatures of 600 C or 800 C The same tem-perature measurement system has been used for all the runs whilegas analysis has been performed only for experiments 4ndash7BIO car-ried out on spruce sawdust The 6BIO and 7BIO runs have beenconducted using nitrogen to continuously purge the vessel

Spruce sawdust has been used as feedstock for the 2ndash7BIO runsonly a test has been carried out on spruce pellets (1BIO) to charac-terize the thermal field behavior and without performing any gasanalysis

The sawdust has been characterized by means of its ultimateanalysis and measuring its moisture and ash content The ultimateanalysis results (expressed in terms of daf mass fractions ie ondry ash free basis) are in good agreement with other experimentaldata available in literature (Table 3) In particular Phyllis [29] andBIOBIB [30] databases have been chosen as they contain a largenumber of information on the composition of biomass (grass andwood) and waste These databases are useful for analysis of indi-vidual available biomass or waste materials and offer the possibil-ity to obtain an average composition of any combination ofdifferent materials

The moisture content has been measured using a precision ther-mobalance (Eurotherm series plusmn002 accuracy) performing several

Table 3Spruce sawdust characterization

( mass daf) Phyllis BIOBIB Measured

C 5010 4953 5158H 600 577 590Oa 4390 4450 4244N 000 019 008S 000 001 000

Moisture ( mass wet) 825Ash ( mass wet) 090Bulk density (kg m3) 192

a Measured by difference

Fig 4 Finite element model geometric structure

1430 P Baggio et al Energy Conversion and Management 50 (2009) 1426ndash1435

temperature data (Twall) The average value of the increasing tem-perature ramp deduced from the experimental data is approxi-mately equal to a rate h = 32 C min1

On the bottom flange of the reactor an adiabatic condition hasbeen assumed while on the upper flange a natural convection fluxboundary condition has been chosen (heat transfer coefficienth = 8 Wm2 K1) with the external air at Tamb The symmetry axis(r = 0) is assumed adiabatic

The model has been calibrated in order to simulate the bed re-sponse adjusting the (simulated and constant) bulk thermal con-ductivity (kbed) with a best fitting procedure in order to obtain agood agreement with the experimental data (ie Tbed at a distanceof 30 mm from the internal wall of the vessel) values equal to 032and 035 Wm1 C1 for gravel and sand respectively have beenobtained

Moreover a further best fitting procedure has been carried outconsidering a variable bed thermal conductivity (ie a linear and aquadratic function of the temperature) The resulting conductivityvalues that vary between 02 and 04 Wm1 C1 give a bettermodel calibration (Fig 5) and are in agreement with some experi-mental data available in literature (eg Deru 2003 found that

0

100

200

300

400

500

600

700

0 3000 6000 9000 12000 15000

time [s]

Tem

pera

ture

[degC

]

experimental data

model estimate (linear)

model estimate (quadratic)

[ λbed = a + bT ]

[ λbed = c + dT2]

Fig 5 Finite element analysis gravel thermal conductivity estimate (a =015 Wm1 C1 b = 255 104 Wm1 C2 c = 019 Wm1 C1 and d =300 107 Wm1 C3)

gravel thermal conductivity is an increasing function of the tem-perature [26])

In addition the contact resistance between the inert bed mate-rial and the reactor internal wall has been estimated This param-eter represents an additional resistance to the heat transfer and iscaused by the contact between different materials (gravel or sandand Incoloy) For its calculation the mixed bed composition (inertand air) in contact with the steel wall has been taken into accountThis resistance has been simulated inserting an additional layer(2 mm thick) inside the reactor shell The calibration of this modelagainst experimental data gives values of the thermal resistancearound 014 and 013 C W1 for the gravel and the sand bedrespectively [27]

These contact resistance values have been also comparedagainst the value calculated using both a simplified model (lumpedcapacitance method) [27] and the correlation suggested by Holman[28] obtaining a good agreement (respectively 018 and0135 C W1 for gravel) In conclusion for the considered scenar-ios the contact thermal resistance can be safely considerednegligible

4 Gasification activities

41 Experimental procedures

The gasification experimental activity has been carried out byfilling the reactor with spruce wood biomass (pellets or sawdust)and performing seven runs (named lsquolsquoBIO-typerdquo) at the selectedoperating conditions summarized in Table 2

All the experiments have been performed at atmospheric pres-sure and at peak temperatures of 600 C or 800 C The same tem-perature measurement system has been used for all the runs whilegas analysis has been performed only for experiments 4ndash7BIO car-ried out on spruce sawdust The 6BIO and 7BIO runs have beenconducted using nitrogen to continuously purge the vessel

Spruce sawdust has been used as feedstock for the 2ndash7BIO runsonly a test has been carried out on spruce pellets (1BIO) to charac-terize the thermal field behavior and without performing any gasanalysis

The sawdust has been characterized by means of its ultimateanalysis and measuring its moisture and ash content The ultimateanalysis results (expressed in terms of daf mass fractions ie ondry ash free basis) are in good agreement with other experimentaldata available in literature (Table 3) In particular Phyllis [29] andBIOBIB [30] databases have been chosen as they contain a largenumber of information on the composition of biomass (grass andwood) and waste These databases are useful for analysis of indi-vidual available biomass or waste materials and offer the possibil-ity to obtain an average composition of any combination ofdifferent materials

The moisture content has been measured using a precision ther-mobalance (Eurotherm series plusmn002 accuracy) performing several

Table 3Spruce sawdust characterization

( mass daf) Phyllis BIOBIB Measured

C 5010 4953 5158H 600 577 590Oa 4390 4450 4244N 000 019 008S 000 001 000

Moisture ( mass wet) 825Ash ( mass wet) 090Bulk density (kg m3) 192

a Measured by difference

0 20 40

0

50000

100000

150000

200000

250000

300000

Run 5BIOColumn 1

COCH4

H2

40 60

0

5000

O2

N2

Sign

al o

ut (V

)

t (sec)Sign

al o

ut (V

)

t (sec)60

0 10 20 30 40 50 60 70

0

100000

200000

300000

400000

500000

Run 5BIOColumn 2

C2H2

C2H6

C2H4

CO2

H2 O2 N2

30 40 50

0

4000

8000

12000

Sign

al o

ut (V

)

t (sec)

Sign

al o

ut (V

)

t (sec)

Fig 6 Example of GC analysis layout

P Baggio et al Energy Conversion and Management 50 (2009) 1426ndash1435 1431

runs at 105 C drying the sample for long time (usually for a cou-ple of hours or even for 1 day) or using the balance lsquoconstant massrsquofunction which stops the run when the difference of the mass in atime step is less than a fixed tolerance The obtained moisture con-tent values are in the range 76ndash87 with an average value (usedfor the model calculations) of 82

The ash content has been measured analyzing the residual mat-ter after an experimental run The residual solid matter has an ashmass fraction of 4 corresponding to a value of about 1 on thebiomass sample as received Values between 08 and 18 forspruce wood (as sawdust) have been found in literature [2930]

The bulk density has been measured by means of a precisionbalance and a graduated cylinder obtaining 192 kg m3

42 Solid product experimental characterization

The experimental runs considered in the further analysis are the3ndash4ndash5ndash6ndash7BIO performed on spruce sawdust both at 600 and800 C as maximum temperatures These experiments have beencarried out on the feedstock as received except for the 5BIO runperformed using dried biomass All the experiments have beenconducted at atmospheric pressure and for the 6ndash7BIO nitrogenhas been fluxed into the reactor

The ultimate analysis on the residual solid matter shows that itconsists mainly of solid carbon (951 mass fraction) ash and otherelements in traces (eg H 046 and N 007)

In Table 4 it is shown that for the 4ndash5ndash6ndash7BIO runs there is asignificant production of solid residual during the process (195ndash225 of the wet samples)

43 Syngas experimental characterization

Fig 6 shows an example of the gas analysis performed duringthe 5BIO run by the adopted GC The sampling rate is almost con-stant with 30 measurements per hour

In the chromatograms there are nine different peaks corre-sponding to the many different gaseous compounds detectedThe main components of the syngas are H2 CH4 CO CO2 C2H4C2H6 C2H2 and possibly some O2 and N2 due to the initial pres-ence of air in the reactor volume not occupied by the biomass inthe non-purged runs

Observing the experimental syngas molar composition versusthe run time for the non-purged experiments (eg 4BIO run inFig 7) it is possible to distinguish a lsquopyrolysis zonersquo (roughly ex-tended between 1500 and 3500 s runtime) in which the conver-sion reactions take place producing a significant percentage ofthe chemical species having a high calorific values

Before this zone (ie for lower syngas temperature) there arestill significant concentrations of O2 and N2 due to the presenceof air in the reactor This residual amount tends to flow out fromthe vessel because of the gas expansion due to the increasing tem-perature and because of the production of synthesis gas

On the contrary after the lsquopyrolysis zonersquo the N2 molar concen-tration increases until it reaches an almost constant value (aroundatmospheric values) Since the system itself is airtight this N2 con-centration increase is probably due to air backflow through the

Table 4Residual solid (char and ash) mass fractions ( of the wet feedstock)

Run Residual Mass fraction ()

4BIO Char + ash 1955BIO Char + ash 2256BIO Char + ash 1957BIO Char + ash 198

chimney occurring when the syngas production rate becomesnegligible

In N2 purged experiments the conversion process has beencharacterized estimating the syngas yield versus the run time indi-rectly ie calculating it from the nitrogen inlet mass flow rate andthe (N2 diluted) produced gas composition (Fig 8c) The N2 massflow rate varies between 20 l min1 (6BIO) and 10 l min1 (7BIO)

Thus it is possible to compare purged and non-purged nitrogenfree compositions for the lsquopyrolysis zonersquo (Fig 8a and b shows thecomparison of 4ndash7BIO run results) In Fig 8 the oxygen concentra-tion has not been reported being negligible

In all the performed experiments the biomass thermal conver-sion process starts with a significant production of CO2 then grad-ually there is an increase of CO CH4 and H2 concentrations Thisearly CO2 peak has been found also by other authors [31] reportingthat for the thermal conversion of fine shredded wooden biomass(chips or sawdust) carbon dioxide precedes carbon monoxide for-mation The effective reaction range occurs between 1500 and3500 s run time and is characterized by the typical pyrolysis andgasification processes specifically the gasification reactions (gasshift and reforming) use the steam coming from vaporization ofthe biomass water content

Table 5 shows the average syngas molar composition in theaforementioned lsquopyrolysis zonersquo (1500ndash3500 s run time) all the re-ported values are dry basis (since water is eliminated before inject-ing the syngas into the GC)

The gaseous effluents mainly consist of the above mentionedgases with low concentrations of C2H4 and C2H6 and traces ofC2H2 The small hydrocarbon molecules (alkanes alkenes and al-kynes compounds) found in the analyzed mixture are probably

RUN 4BIO

0

10

20

30

40

50

60

70

80

90

0 1000 2000 3000 4000 5000 6000 7000 8000 9000

time

Mol

ar f

ract

ion

[] _

0

100

200

300

400

500

600

700

800

900

Tem

pera

ture

[degC

]

C2H2

C2H4

C2H6

CH4

CO

CO2

H2

N2

O2

Tinternal

[s]

Fig 7 Syngas molar composition experimental analysis for 4BIO run

RUN 4BIO(N2 free)

0

10

20

30

40

50

60

70

80

90

100

time [s]

Mol

ar f

ract

ion

[]

_

0

100

200

300

400

500

600

700

800

900

Tem

pera

ture

[degC

] C2H2

C2H4

C2H6

CH4

CO

CO2

H2

Tinternal

RUN 7BIO (N2 free)

0

10

20

30

40

50

60

70

80

90

100

time [s]

Mol

ar f

ract

ion

[]

_

0

100

200

300

400

500

600

700

800

900

Tem

pera

ture

[degC

] C2H2

C2H4

C2H6

CH4

CO

CO2

H2

Tinternal

RUN 7BIO(N2 free)

00

05

10

15

20

25

30

35

1100 1300 1500 1700 1900 2100 2300 2500 2700 2900 3100 3300 3500 3700 3900 4100 4300

1250 1500 1750 2000 2250 2500 2750 3000 3250 3500 3750 4000 4250 4500

1250 1500 1750 2000 2250 2500 2750 3000 3250 3500 3750 4000 4250 4500

time [s]

Syng

as f

low

rat

e [N

l min

-1]

C2H2

C2H4

C2H6

CH4

CO

CO2

H2

Sum

(a)

(b)

(c)

Fig 8 Syngas characterization for 4ndash7BIO runs in the lsquopyrolysis zonersquo (a) (b) molar composition and (c) flow rate

1432 P Baggio et al Energy Conversion and Management 50 (2009) 1426ndash1435

Table 5Syngas average molar composition (N2 free) in the lsquopyrolysis zonersquo

3BIO 4BIO 5BIO 6BIO 7BIO

CH4 1341 1807 1917 1411 1434CO 5144 3670 3400 3044 3089CO2 2532 1931 1809 1374 1351H2 607 2153 2493 4050 3801C2H2 006 017 013 ndash 016C2H4 202 330 288 074 239C2H6 169 091 080 046 070

P Baggio et al Energy Conversion and Management 50 (2009) 1426ndash1435 1433

the results of the cracking action of the pyrolysis process that tendsto split large molecules (tar) into smaller ones

44 Gasification model description

For the considered conversion zone the syngas temperature(ie the internal temperature) has been assumed as the local con-version temperature used in the equilibrium model (actually therange of Tinternal temperature variation in the pyrolysis zone isroughly between 400 and 500 C and the peak temperature) Thebiomass conversion process has been modeled with a thermody-namic chemical equilibrium approach To this purpose a codewritten in Matlab environment [32] has been developed usingthe Cantera software library (a collection of object-oriented soft-ware tools for problems involving chemical kinetics thermody-namics and transport phenomena [33]) The solver implementedin Cantera is a version of the VillarsndashCruisendashSmith (VCS) algorithm(a well suited method to handle multiphase problems) that findsthe composition minimizing the total Gibbs free energy of a mix-ture [34] The NASA [35] and the GRI-MECH [36] databases havebeen used to evaluate the thermodynamic properties of the chem-ical species considered in the model

The applied procedure for solving the minimization problem isbased on the stoichiometric formulation in which the closed-sys-tem constraint is treated by means of the linearly independentstoichiometric Eq (1) so as to result in an unconstrained minimiza-tion problem

n frac14 n0 thornXR

jfrac141

mjnj eth1THORN

where n0 is the initial composition vector mj represents the stoichi-ometric coefficients vector and nj are the so called lsquoreactioncoordinatesrsquo

The minimization procedure applied to the G function impliesthe computation of its partial derivatives with respect to the reac-tion coordinates (2) and gives the equilibrium condition (3)

Gnj

TPnkndashj

frac14XN

ifrac141

Gni

TPnkndashj

ni

nj

nkndashj

eth2THORN

Xi

v ijli frac14 0 eth3THORN

Table 6Chemical species considered by the equilibrium model

Phase Group Compo

Gas Inorganic carbon compounds C(g) COHydrogen compounds H H2 ONitrogen compounds N N2 NSulfur compounds S SO2 SHydrocarbons CH CHOther organic compounds CH2O C

Solid Carbon C(s) gr

where ni represents the number of moles and li is the chemicalpotential

The VCS algorithm utilizes this procedure and it is very usefulwhen dealing with only a few independent stoichiometric equa-tions Among the set of states which satisfy the element mass bal-ance VCS algorithm finds the state with the lowest total Gibbs freeenergy

To estimate the yield of both the gaseous and solid phases animproved two-phase formulation of the model has been used Asshown in Table 6 61 chemical species 60 in the gaseous phaseand one in the solid state (ie the graphite allotropic form of car-bon) are taken in account for the reaction products The chosencompounds are composed of the C H O N and S elements charac-terizing the biomass The calculated yield of solid carbon can beused as an estimate of the actual charcoal residue of the thermalconversion process

For a more detailed description of the model the reader can re-fer to [8]

45 Model performance

Despite the experimental conversion process occurs in non-sta-tionary conditions the average values of the experimental datahave been used to assess the reliability of the proposed model

The comparison of the measurements with the equilibriummodel results has been carried out on the residual solid matter(char and ash) yield and on the syngas composition

The char yields are reported versus temperature (Fig 9) and forthe experimental data the representative temperature is the mea-sured peak temperature (used as an estimate of the characteristicconversion temperature of the whole process ndash see also Figs 7and 8a and b)

Even if there are only few experimental data and the compari-son is limited only to a narrow temperature range the agreementbetween measured and computed values seems to be satisfying(maximum deviation less than 15 and +5 on the measured va-lue respectively for lsquowetrsquo and lsquodryrsquo biomass data)

Furthermore it is interesting to observe that as predicted bythe equilibrium model the water in the biomass acts as a gasifyingagent promoting a more efficient conversion process and so reduc-ing the amount of residual solid carbon (compare 4 and 5BIO runschar yields)

From the comparison between experimental and computedsyngas average molar composition (Fig 10) it is possible to affirmthat in N2 purged runs there is a better agreement than in non-purged runs The effect of the carrier gas is to transport quicklythe reaction products towards the measurement probe loweringthe system response time and minimizing the extent of secondaryreactions

There is a good agreement in the prediction of the carbonmonoxide and carbon dioxide production while the calculatedhydrogen and methane concentrations tend respectively to over-estimate and underestimate the measured values As previousstudies have shown for coal gasification [3738] the high measured

unds

CO2

O2 OH H2O HO2 H2O2 HCOH NH2 NH3 NNH NO NO2 N2O HNO CN HCN H2CN HCNN HCNO HOCN HNCO NCOO3 H2S COS CS2

2 CH3 CH4 C2H C2H2 C2H3 C2H4 C2H5 C2H6 C3H7 C3H8 C6H6 C10H8 C12H10

H2OH CH3O CH3OH HCCO CH2CO HCCOH CH2CHO CH3CHO

aphite

Table 5Syngas average molar composition (N2 free) in the lsquopyrolysis zonersquo

3BIO 4BIO 5BIO 6BIO 7BIO

CH4 1341 1807 1917 1411 1434CO 5144 3670 3400 3044 3089CO2 2532 1931 1809 1374 1351H2 607 2153 2493 4050 3801C2H2 006 017 013 ndash 016C2H4 202 330 288 074 239C2H6 169 091 080 046 070

P Baggio et al Energy Conversion and Management 50 (2009) 1426ndash1435 1433

the results of the cracking action of the pyrolysis process that tendsto split large molecules (tar) into smaller ones

44 Gasification model description

For the considered conversion zone the syngas temperature(ie the internal temperature) has been assumed as the local con-version temperature used in the equilibrium model (actually therange of Tinternal temperature variation in the pyrolysis zone isroughly between 400 and 500 C and the peak temperature) Thebiomass conversion process has been modeled with a thermody-namic chemical equilibrium approach To this purpose a codewritten in Matlab environment [32] has been developed usingthe Cantera software library (a collection of object-oriented soft-ware tools for problems involving chemical kinetics thermody-namics and transport phenomena [33]) The solver implementedin Cantera is a version of the VillarsndashCruisendashSmith (VCS) algorithm(a well suited method to handle multiphase problems) that findsthe composition minimizing the total Gibbs free energy of a mix-ture [34] The NASA [35] and the GRI-MECH [36] databases havebeen used to evaluate the thermodynamic properties of the chem-ical species considered in the model

The applied procedure for solving the minimization problem isbased on the stoichiometric formulation in which the closed-sys-tem constraint is treated by means of the linearly independentstoichiometric Eq (1) so as to result in an unconstrained minimiza-tion problem

n frac14 n0 thornXR

jfrac141

mjnj eth1THORN

where n0 is the initial composition vector mj represents the stoichi-ometric coefficients vector and nj are the so called lsquoreactioncoordinatesrsquo

The minimization procedure applied to the G function impliesthe computation of its partial derivatives with respect to the reac-tion coordinates (2) and gives the equilibrium condition (3)

Gnj

TPnkndashj

frac14XN

ifrac141

Gni

TPnkndashj

ni

nj

nkndashj

eth2THORN

Xi

v ijli frac14 0 eth3THORN

Table 6Chemical species considered by the equilibrium model

Phase Group Compo

Gas Inorganic carbon compounds C(g) COHydrogen compounds H H2 ONitrogen compounds N N2 NSulfur compounds S SO2 SHydrocarbons CH CHOther organic compounds CH2O C

Solid Carbon C(s) gr

where ni represents the number of moles and li is the chemicalpotential

The VCS algorithm utilizes this procedure and it is very usefulwhen dealing with only a few independent stoichiometric equa-tions Among the set of states which satisfy the element mass bal-ance VCS algorithm finds the state with the lowest total Gibbs freeenergy

To estimate the yield of both the gaseous and solid phases animproved two-phase formulation of the model has been used Asshown in Table 6 61 chemical species 60 in the gaseous phaseand one in the solid state (ie the graphite allotropic form of car-bon) are taken in account for the reaction products The chosencompounds are composed of the C H O N and S elements charac-terizing the biomass The calculated yield of solid carbon can beused as an estimate of the actual charcoal residue of the thermalconversion process

For a more detailed description of the model the reader can re-fer to [8]

45 Model performance

Despite the experimental conversion process occurs in non-sta-tionary conditions the average values of the experimental datahave been used to assess the reliability of the proposed model

The comparison of the measurements with the equilibriummodel results has been carried out on the residual solid matter(char and ash) yield and on the syngas composition

The char yields are reported versus temperature (Fig 9) and forthe experimental data the representative temperature is the mea-sured peak temperature (used as an estimate of the characteristicconversion temperature of the whole process ndash see also Figs 7and 8a and b)

Even if there are only few experimental data and the compari-son is limited only to a narrow temperature range the agreementbetween measured and computed values seems to be satisfying(maximum deviation less than 15 and +5 on the measured va-lue respectively for lsquowetrsquo and lsquodryrsquo biomass data)

Furthermore it is interesting to observe that as predicted bythe equilibrium model the water in the biomass acts as a gasifyingagent promoting a more efficient conversion process and so reduc-ing the amount of residual solid carbon (compare 4 and 5BIO runschar yields)

From the comparison between experimental and computedsyngas average molar composition (Fig 10) it is possible to affirmthat in N2 purged runs there is a better agreement than in non-purged runs The effect of the carrier gas is to transport quicklythe reaction products towards the measurement probe loweringthe system response time and minimizing the extent of secondaryreactions

There is a good agreement in the prediction of the carbonmonoxide and carbon dioxide production while the calculatedhydrogen and methane concentrations tend respectively to over-estimate and underestimate the measured values As previousstudies have shown for coal gasification [3738] the high measured

unds

CO2

O2 OH H2O HO2 H2O2 HCOH NH2 NH3 NNH NO NO2 N2O HNO CN HCN H2CN HCNN HCNO HOCN HNCO NCOO3 H2S COS CS2

2 CH3 CH4 C2H C2H2 C2H3 C2H4 C2H5 C2H6 C3H7 C3H8 C6H6 C10H8 C12H10

H2OH CH3O CH3OH HCCO CH2CO HCCOH CH2CHO CH3CHO

aphite

0

10

20

30

40

50

60

Ave

rage

mol

ar c

once

ntra

tion

(

) 4BIO 6BIO 7BIO Model

CH 4 CO CO2 H2

Fig 10 Syngas average molar composition (N2 free) in the 600ndash800 C temperaturerange comparison between measured and equilibrium computed data (6-7BIO areN2 purged runs)

0

10

20

30

40

50

600 700 800 900Temperature [degC]

Res

idua

l sol

id m

ass

frac

tion

(

) _

model (wet biomass) model (dry biomass)

measured (wet biomass) measured (dry biomass)

Fig 9 Residual solid matter (char and ash) yield versus temperature comparisonbetween measured and equilibrium computed data

1434 P Baggio et al Energy Conversion and Management 50 (2009) 1426ndash1435

concentrations of methane can result from incomplete conversionof pyrolysis products This probably applies also to the biomassconversion process (ie measured methane concentration cannotbe explained on a purely thermodynamic basis) The deviationcan result from one or more non-equilibrium factors such asincomplete cracking of the pyrolysis products temperature gradi-ents in the gasifier or catalysis of the methanation reaction takingplace in the piping system towards the measurement probe or inthe low temperature zones of the reactor bed in contact with theshell in fact many steel alloys (including Incoloy used here) con-tains nickel that can have a selective catalytic action on the meth-ane formation kinetics

5 Conclusions

The overall agreement between model predictions and experi-mental results is reasonably satisfactory in particular the residualsolid yield (char) obtained experimentally at 800 C is very close(in the range 5 to +15) to the value obtained by the simulationConsidering the gas phase the agreement is quite good also for COand CO2 it is fair for H2 while it is poor for CH4

The simulation code based on a thermochemical equilibriumapproach can then be considered as an useful tool in the designof biomass thermal conversion processes lasting long enough toachieve (or to approach) equilibrium conditions

However despite the satisfying results presented here thisstudy must be considered as a preliminary one for several reasonsFirst of all as already mentioned the computed reaction product

compositions is representative only of processes where the resi-dence time is long enough to establish thermodynamic equilibriumconditions This could be the typical case of some continuous realreactors (eg fixed or fluidized bed type) while for other kindsof process (eg fast pyrolysis) the equilibrium conditions will benot achieved

Moreover the proposed model is still in the development stagesince even if the main reaction products (syngas and char) can al-ready be computed the assessment of the liquid phase yield is notyet available The estimation of this third condensable phase (tar)is very important when designing a gasification system Further re-search is then currently in progress to evaluate on the modelingimprovement the feasibility of an advanced model to calculatealso the liquid phase and on the experimental activities the eval-uation of the tar yield and its chemical characterization

References

[1] Chen G Andries J Luo Z Spliethoff H Biomass pyrolysisgasification forproduct gas production the overall investigation of parametric effects EnergyConvers Manage 2003441875ndash84

[2] Jalan RK Srivastava VK Studies on pyrolysis of a single biomass cylindricalpellet ndash kinetic and heat transfer effects Energy Convers Manage 199940467ndash94

[3] Babu BV Chaurasia AS Pyrolysis of biomass improved models forsimultaneous kinetics and transport of heat mass and momentum EnergyConvers Manage 2004451297ndash327

[4] Pyle DL Zaror CA Heat transfer and kinetics in the low temperature pyrolysisof solids Chem Eng Sci 198439147ndash58

[5] Al-Omari S-AB Experimental investigation on combustion and heat transfercharacteristics in a furnace fueled with unconventional biomass fuels (datestones and palm stalks) Energy Convers Manage 200647778ndash90

[6] Sharma A Rao RT Analysis of an annular finned pyrolyser Energy ConversManage 199839985ndash97

[7] Batra D Rao TR Analysis of an annular finned pyrolyser-II Energy ConversManage 200041573ndash83

[8] Baratieri M Baggio P Fiori L Grigiante M Biomass as an energy sourcethermodynamic constraints on the performance of the conversion processBioresour Technol 2008997063ndash73

[9] Babu BV Sheth PN Modeling and simulation of reduction zone of downdraftbiomass gasifier effect of char reactivity factor Energy Convers Manage2006472602ndash11

[10] Goslashbel B Henriksen U Jensen TK Qvale B Houbak N The development of acomputer model for a fixed gasifier and its use for optimization and controlBioresour Technol 2007982043ndash52

[11] Guo B Li D Cheng C Luuml Z Shen Y Simulation of biomass gasification with ahybrid neural network Bioresour Technol 20017677ndash83

[12] Altafini CR Wander PR Barretto RM Predictions of the working parameters ofa wood waste gasifier through an equilibrium model Energy Convers Manage2003442763ndash77

[13] Fagbenle RL Oguaka ABC Olakoyejo OT A thermodynamic analysis of abiogas-fired integrated gasification steam injected gas turbine (BIGSTIG)plant Appl Therm Eng 2007272220ndash5

[14] Ginsburg J de Lasa HI Catalytic gasification of biomass in a CREC fluidizedriser simulator Int J Chem React Eng 20053A38 lthttpwwwbepresscomijcrevol3A38gt [accessed December 2007]

[15] Jarungthammachote S Dutta A Equilibrium modeling of gasification Gibbsfree energy minimization approach and its application to spouted bed andspout-fluid bed gasifiers Energy Convers Manage 2008491345ndash56

[16] Lutz AE Bradshaw RW Bromberg L Rabinovich A Thermodynamic analysis ofhydrogen production by partial oxidation reforming Int J Hydrogen Energy200429809ndash16

[17] Schuster G Loumlffler G Weigl K Hofbaure H Biomass steam gasification ndash anextensive parametric modeling study Bioresour Technol 20017771ndash9

[18] Zainal ZA Ali R Lean CH Seetharamu KN Prediction of performance of adowndraft gasifier using equilibrium modelling for different biomassmaterials Energy Convers Manage 2001421499ndash515

[19] Kuramochi H Wu W Kawamoto K Prediction of the behaviors of H2S and HClduring gasification residual biomass fuels by equilibrium calculation Fuel200584377ndash87

[20] Lee DH Yang H Yan R Liang DT Prediction of gaseous products from biomasspyrolysis through combined kinetic and thermodynamic simulations Fuel200786410ndash7

[21] Li X Grace JR Watkinson AP Lim CJ Erguumldenler A Equilibrium modelling ofgasification a free energy minimization approach and its application to acirculating fluidized bed coal gasifier Fuel 200180195ndash207

[22] Mahishi MR Goswami DY Thermodynamic optimization of biomass gasifierfor hydrogen production Int J Hydrogen Energy 2007323831ndash40

[23] Prins MJ Ptasinski KJ Janssen FJJG From coal to biomass gasificationcomparison of thermodynamic efficiency Energy 2007321248ndash59

P Baggio et al Energy Conversion and Management 50 (2009) 1426ndash1435 1435

[24] Baggio P Baratieri M Fiori L Grigiante M Ragazzi M Biomass gasificationexperimental investigation literature review and design of a bench-scalereactor In Proceedings 14th European biomass conference 2005 p 692ndash5

[25] Comsol multiphysics 33 2008 lthttpwwwcomsolcomgt[26] Deru M A model for ground-coupled heat and moisture transfer from

buildings National Renewable Energy Laboratory Technical report NRELTP-550-33954 2003

[27] Baggio P Baratieri M Grigiante M Indagine teorico-sperimentale sul ruolodella resistenza di contatto nei reattori per biomasserisultati preliminary InProceedings XII AIPT conference 2006 p 105ndash14

[28] Holman JP Heat transfer 9th ed Singapore McGraw-Hill 1992[29] Phyllis Database for biomass and waste The Netherlands ECN 2003 lthttp

wwwecnnlphyllisgt[30] Reisinger K Haslinger C Herger M Hofbauer H BIOBIB ndash a database for

biofuels In Thermie ndash conference renewable energy databases 1996[31] Simmons WW Ragland KW Burning rate of millimeter sized wood particles in

a furnace Combust Sci Technol 1986461ndash15

[32] Matlab 704365 (R14) The MathWorks Inc 2005 lthttpwwwmathworkscomgt

[33] Goodwin D Cantera object oriented software for reacting flows CaliforniaInstitute for Technology (Caltech) 2005 ltwwwcanteraorggt

[34] Smith WR Missen RW Chemical reaction equilibrium analysis theory andalgorithm New York Wiley Interscience 1982

[35] McBride BJ Gordon S Reno MA Coefficients for calculating thermodynamicand transport properties of individual species NASA report TM-4513 1993

[36] GRI-Mech thermodynamic values 2000 ltwwwmeberkeleyedugri_mechgt[37] von Fredersdorff CG Elliott MA Coal gasification In Lowry HH editor

Chemistry of coal utilization New York Wiley 1963[38] Coates RL Chen CL Pope BJ Coal devolatilization in a low pressure low

residence time entrained flow reactor In Massey LG editor Advances inchemistry series vol 131 Washington (DC) American Chemical Society1974 p 92ndash107