Energy Conversion and Management 52 (2011) 1386–1396

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Energy Conversion and Management

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Modeling and simulation of a downdraft biomass gasifier 1. Model developmentand validation

Avdhesh Kr. Sharma ⇑Mech. Engg. Dept., D.C.R. University of Science & Technology, Murthal, Sonepat 131 039, Haryana, India

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

Article history:Received 29 December 2009Received in revised form 27 September2010Accepted 3 October 2010Available online 29 October 2010

Keywords:ModelingSimulationBiomass gasificationEquilibriumKineticsSuction gasifier

0196-8904/$ - see front matter � 2010 Elsevier Ltd. Adoi:10.1016/j.enconman.2010.10.001

⇑ Tel.: +91 09416722212; fax: +91 01302484004.E-mail address: avdhesh_sharma35@yahoo.co.in

An ‘EQB’ computer program for a downdraft gasifier has been developed to predict steady state perfor-mance. Moving porous bed of suction gasifier is modeled as one-dimensional (1-D) with finite controlvolumes (CVs), where conservation of mass, momentum and energy is represented by fluid flow, heattransfer analysis, drying, pyrolysis, oxidation and reduction reaction modules; which have solved in inte-gral form using tri-diagonal matrix algorithm (TDMA) for reaction temperatures, pressure drop, energet-ics and product composition. Fluid flow module relates the flow rate with pressure drop, while biomassdrying is described by mass transfer 1-D diffusion equation coupled with vapour–liquid-equilibrium rela-tion. When chemical equilibrium is used in oxidation zone, the empirically predicted pyrolysis products(volatiles and char) and kinetic modeling approach for reduction zone constitutes an efficient algorithmallowing rapid convergence with adequate fidelity. Predictions for pressure drop and power output (gas-ifier) are found to be very sensitive, while gas composition or calorific value, temperature profile and gas-ification efficiency are less sensitive within the encountered range of gas flow rate.

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1. Introduction

Thermochemical conversion of woody biomass under restrictedsupply of oxidant is among the most promising non-nuclear formsof future energy. Besides utilizing a renewable energy sources, thetechnology also offers an eco-efficient and self sustainable way ofobtaining gaseous fuel usually called producer gas. It can be usedin either premixed burners (dryers, kilns, furnaces or boilers) forthermal applications or in direct feeding of high efficiency internalcombustion engines/gas turbines for mechanical applications.After adequate cleaning up and reforming, the generated gas canalso be used for feed high temperature fuel cells or for productionof hydrogen fuel [1]. For electric power generation applications, themotive power from prime mover such as IC engine or gas turbinecan be connected to an electric generator to produce electric en-ergy. Applications of IC engines have proved to be the most effi-cient and least expensive decentralized-power-generationsystems at lower power range. Research efforts have been ex-panded worldwide to develop this technology cost-effective andefficient in lower power range.

Recent progression in numerical simulation techniques andcomputer efficacy become the effective means to develop more ad-vanced and sophisticated models in order to provide more accurate

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qualitative and quantitative information on biomass gasification.In the present work, the objective is not merely to develop a theo-retical model of a downdraft gasifier system, but also to develop anefficient algorithm that allow rapid convergence and adequateaccuracy of predictions. Presently, the gasification modeling tech-niques include the application of thermodynamic equilibrium,chemical kinetics, diffusion controlled, diffusion–kinetic approachand CFD tools. None of approaches have clear advantage over theothers. Pure equilibrium approach has thermodynamic limitations,instead of its inherent advantage of being generic, relatively easy toimplement and rapid convergence, even though, researchers havesuccessfully demonstrated the application of equilibrium chemis-try in downdraft gasifiers. Zainal et al. [2] reported an interestingmodel for biomass gasifier describing the equilibrium calculationsconsidering water–gas shift and methane–char reactions. Melgaret al. [3] combine chemical and thermal equilibrium in order topredict gas composition and Baratieri et al. [1] presented anequilibrium model based on minimization of Gibbs energy usingVillars–Cruise–Smith (VCS) algorithm. They validated the predic-tions successfully. Later, Sharma [4] has compared the theoreticalpredictions of reduction zone using equilibrium, kinetic modelingand experimental data. For optimum performance, Sharma hasidentified a critical length for the reduction zone (where all chargets converted). At a more sophisticated level, Ratnadhariya andChanniwala [5], suggested that separate thermodynamic modelingcan be approached to different zones of a downdraft gasifier. Onthe other hand, non-equilibrium formulations such as kinetic rate

Nomenclature

A area (m2)d particle diameter (m)f friction factork thermal conductivity/rate constantM massD _m solid mass conversionR thermal resistanceT temperature (K)V volume/velocity[C] species concentrationD diffusion coefficient/diameterh enthalpy (kJ/kg)L/l length (m)_m= _n mass/molar flow rateDP pressure dropRu universal gas constantDtres residence time in CVr_

diameter ratio of annulusCV control volumeDh hydraulic diameter (m)hhv high heating value (kJ/kg)dL length of CVME methane-equivalent_Q heat flow or release/absorbedRe Reynolds numberY mass fraction or ratioSg specific gravity

Greek lettersl dynamic viscosity (kg m�1 s�1)erad radiative emissivityq densityr Stefan–Boltzmann constant

x humidityeb bed porosityn correction factor for annulus

Subscripts/superscriptsi number of CVsA/a ambientcel celluloseDB dry biomassdev developing flowmfd modified fully developedpreheat preheating zonetuy tuyersat saturated vapourj reaction numberan annular regionhc hemicellulosedry drying zonef fluid (gas/air)p particlepg producer gasvol/v volatilew moisturek speciesash ashlg lignineff effectivefd fully developed flowpyr pyrolysiss solid (biomass, char, ash)red reduction zoneDBp mass percentage in dry biomass

A.Kr. Sharma / Energy Conversion and Management 52 (2011) 1386–1396 1387

and/or diffusion controlled model including CFD tools are moreaccurate, no doubt, but are detailed and computationally moreintensive. It takes time for convergence by a few orders of magni-tudes [6]. Non-equilibrium approaches use char conversion as asurface phenomena describing by char reactivity and global reac-tions of char–gas and gas–gas reactions. An effective global rateconstant may be defined to account for both diffusion and kineticsof these reactions. Wang and Kinoshita [7] modeled the kinetics ofthe heterogeneous and homogeneous reactions of char conversionin reduction zone for a given residence time and bed temperature,while Giltrap et al. [8] used the reaction kinetics parameters re-ported by Wang and Kinoshita in order to develop a model of thegas composition and temperature for char reduction zone of adowndraft gasifier. Babu and Sheth [9] further modified these reac-tion rates using a variable char reactivity factor to predict the re-sults agreeing with experimental data. Later, Gao and Li [10]presented the downdraft gasifier model by combining a pyrolysismodel (based on Koufopanos scheme) and reduction model follow-ing [7–9] to simulate the temperature field and gas concentrationfield in time and space.

The overall pressure drop across the gasifier system is an impor-tant parameter. It monitors not only the health of a suction gasifierbut also the volumetric efficiency of engine and hence the enginepower output. The pressure drop across the conventional packedbed depends on system geometry, medium porosity, permeabilityand physical properties of working medium. Unlike, in gasifiersthe bed maintains widely varying temperature specifications, par-ticle size distribution and bed porosity. Such study on pressuredrop through a downdraft biomass gasifier bed is limited in open

literature. Sharma [11], measured the pressure drops across thegasifier bed at various particle size arrangements in cold and hotflow, and at various locations of a 20 kWe open top downdraft gas-ifier in addition to temperature profile, gas composition, calorificvalue. These data has been used in this work.

In fact, the selection of level and modeling approach (viz. chem-ical equilibrium, chemical kinetics or diffusion controlled) dependson statement of the problem and therefore may vary considerablyfrom one case to another. Since, the objective of the present work isnot to invoke the highest level or most sophisticated gasifier mod-el, yet it is an attempt to develop an efficient algorithm that enablerapid convergence without affecting the validity. Such comprehen-sive work, in fact, is missing in the archival literature. This compre-hensive work, therefore, presents the modular treatment (allowingscope of further improvement at module level) to fluid flow, heattransfer, biomass drying, pyrolysis, and oxidation and reductionreactions processes to form a powerful tool for simulation of suc-tion (downdraft) gasifier. Here biomass drying has been describedvia thermal equilibrium, where mass transfer determines the rateof moisture removal from wet biomass particles. Devolatilizationrate and pyrolysis products is described by single pseudo-first or-der reaction and empirical model, chemical equilibrium for rapidconvergence in oxidation zone (>800 �C), and kinetic scheme forreduction zone (<800 �C), constitutes an efficient algorithm for suc-tion biomass gasifier allowing considerable saving in iterative timewithout degradation in accuracy of predictions. Such gasifier algo-rithms are desired when combining a gasifier model with a gas–engine model towards simulation/optimization of gasifier–enginesystem. Predictions of model and its subroutines have been

Table 1Fluid flow: pressure drop equations.

TuyersDPtuy ¼ ðPatm � Pin;tuyÞ þ ðPin;tuy � Pexit;tuyÞ

¼ qV2in

2 þ f LDþ Kdev

� � qV2in

2 ¼ 1þ f LDþ Kdev

� � qV2

2

(3)

Pressure drop parameter [13]

Kdev ¼ exp 0:3� 2:9� 10�3= LdevRe�D

� �� 1:43� 10�6= Ldev

Re�D

� �2� �

(3a)

LdevD ffi 0:06Re; for Re < 2300 (3b)

ffi 4.4Re1/6; for Re > 4000 (3c)

Porous Bed: Ergun equation [14]

DPi ¼150ð1�eb;iÞ2lðTiÞli

qðTiÞe3b;i

d2p;i AT

ð _mf Þi þ1:75ð1�eb;iÞliqðTiÞe3

b;idp;i A

2Tð _mf Þ2i

(4)

Concentric annulus [15]

DPan ¼ Kdev þ fmfddL1Deff

� �_m2

pg

2qpg A2an

(5)

Reeff = ReDh/n; Deff = Dh/n (5a)

n ¼ ð1� r_Þ2ð1� r

_2Þð1� r

_4Þþð1� r

_2Þ2= lnð r

_Þ

where r_¼ ro=ri

(5b)

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validated, which are showing realistic performance including pres-sure drop trends in cold and hot flow condition.

2. Mathematical formulation

The packed bed of the biomass gasifier has been modeled as aporous medium in which fluid flow rate increases in the directionof flow due to thermo-chemical conversion of solid particles con-stituting the bed. The flow of air and biomass consumption inthe gasifier is related by the phenomena of fluid flow, heat transfer,and thermo-chemical processes, viz., preheating, drying, pyrolysis,combustion and reduction reactions. For simplification, these ther-mo-chemical processes are described by five separate zones inaddition to annular jacket zone as shown in Fig. 1, however, the ac-tual dividing lines between these zones evolve as solution pro-ceeds. The upper end of the oxidation zone starts at the elevationof the tuyers, and all the other zones are determined by theirrespective temperatures as the solution evolves.

For modeling, these zones are further subdivided into a numberof CVs for analysis, where each CV has been characterized by theaverage values of parameters such as temperature, particle size,fluid flow rate, reactor diameter, etc. In all CVs, the solid particlesare considered spherical, with uniform diameters. In the dryingand preheating zones, there is no shrinkage in particle size dueto drying and preheating process. However in pyrolysis, oxidationand reduction zones, feedstock undergoes chemical reactions lead-ing to change in the particle size, thus, the diameter is allowed tovary from one CV to another. The revised particle size is computedfrom the assumption of constant intrinsic density;

mp

mp;initial¼

d3p

d3b

ð1Þ

Here, mp and mp,initial are the mass of particle in the current CV andmass of particle at the start of pyrolysis process. Bulk porosity in thegasifier bed varies with particle diameter and thus can be deter-mined from the correlation of Chen and Gunkel [12] as

eb ¼ 0:5� 0:2ð1� dp=dbÞ ð2Þ

2.1. Fluid flow

Initial phase for gasifier model development is fluid flow mod-eling, which is used to quantify the apportionment of air inflowfrom the open top and through the air tuyers, and the pressuredrop through the gasifier bed as a function of flow rate. The tuyres

Preheating zone

Drying zone

Pyrolysis zone

Oxidation zone

Reduction zone

Air (Top) Biomass

Ash

Producer gas

Annular Jacket regeneration zone

Air (Tuyers)

Gas

Fig. 1. Zonal description of the gasifier.

are straight pipes of circular cross-section, the pressure drop can becomputed from the Darcy–Weisbach equation. The entrance anddeveloping flow effect through the tuyers has been modeled interms of average entry length pressure drop parameter Kdev, fittedto the data of Schmidt and Zeldin in Ref. [13] as given in Table 1.The pressure drop through the gasifier bed (maintaining widelyvarying temperature specifications, particle size distribution andbed porosity) has been obtained using Ergun correlation [14] forcomplete flow regime. Pressure drop through concentric annulusis modeled from modified Darcy–Weisbach friction-factor in termsof effective Reynolds number and effective (annulus) diameter asreported in [15]. The details of equations describing the flow resis-tance across the tuyers, porous bed and annulus are given inTable 1.

2.2. Heat transfer

For heat transfer analysis, the approach of Sharma et al. [16] hasbeen followed in the present work. Here, fuel bed is assumed to beisotropic; solid and gases are considered to be in local thermalequilibrium. These assumptions are justified for fixed bed gasifiersoperating under steady state conditions, since residence time ofsolids in the CV is two to three order magnitude higher than thatof gases. This module describes the formulation of energy interac-tion for the heat inflows and outflows due to advection of fluid andsolids, heat loss through insulated wall, internal thermal interac-tion between adjacent CVs and the quantity of heat generated orconsumed in each CV in order to compute the reaction tempera-ture of each CV. In developing heat transfer module, the heat gen-erated/absorbed during drying, pyrolysis, oxidation or reduction isprescribed as input. These would subsequently be determined bythe modules of the respective sub-processes (cf. Eq. (6) in Table 2)

Table 2Heat transfer equations.

Energy equationPsolidð _miCpiÞin þ

Pgasesð _miCpiÞin

h iTin þ _Qvap þ _Qpyr þ _Qoxid

þ _Qred þP

jk_Qdif ;jk ¼

Psolidð _miCpiÞout þ

Pgasesð _miCpiÞout

h iTout

(6)

Effective thermal conductivity [16]

keff ¼2kskf C1ðln C2þC1Þkg ðln C2þC1Þ�ksC1

þ ks d2ct

d2pþ 4rXdpT3 and X ¼ eb=1þ ebð1�eradÞ

2eradð1�ebÞ(7)

Thermal resistance for ith zone_Qdif ; jk ¼

DTjk

Rsi; where Rsi = Rt(i,bed) + Rt(i,ins) + Rt(i,o)

(8)

where jk = up, down, side

Fig. 2. Single CV used in heat transfer module with all thermal interactions.

Table 3Equations representing to moisture evaporation.

Diffusion equation [17]Xin�Xeqb

Xout�Xeqb¼ 8

p2 ðe�ðp=2Þ2b þ 19 e�9ðp=2Þ2b þ . . .Þ (9)

where b ¼ 4Ddif tres

d2p

; tres ¼ Mb;CV_mb

Simpson [18] relationship

Xeqb ¼ 1800W

Kh1�Khþ

K1 Khþ2K1K2 K2 h2

1þK1Khþ2K1 K2 K2 h2

� �(10)

whereW = 349 + 1.29 (T � 273) + 0.0135 (T � 273)2

K = 0.805 + 0.000736 (T � 273) � 0.00000273 (T � 273)2

K1 = 6.27 � 0.00938 (T � 273) � 0.000303 (T � 273)2 (11)K2 = 1.91 + 0.0407 (T � 273) � 0.000293 (T � 273)2

Relative humidity ratioh ¼ xair

xair;sat¼ xair

pv ;sat mww=pa mwair(12)

Antoine equation [19]

log10ðpv ;satÞ ¼ A� BTþC

� �(13)

Table 4Coefficients for Antoine equation for saturation vapour pressure [19].

Temperature range (K) A B C

255.8–373 4.6543 1435.264 �64.848379–573 3.55959 643.748 �198.043

A.Kr. Sharma / Energy Conversion and Management 52 (2011) 1386–1396 1389

using enthalpy of formation of reactants and products. The transferof energy between adjacent CVs due to fluid and solid particles mo-tion is accounted for by the mass flow rate, temperature of the fluidand solid flows and all heat transfer interactions including the ra-dial outward (heat loss) from the bed to surroundings have beenmodeled using thermal resistance as shown in Fig. 2. The detailsof equations representing the heat transfer module for each CVare given in Table 2.

The total resistance to radial heat loss to the surroundings in theith zone of the gasifier bed is given as the sum of resistances due togranular bed, insulation and the outer surface of the reactor (cf. Eq.(8)). In the preheating zone, there is an additional resistance due tothe annular jacket. The axial heat transfer of the porous gasifier bedhas been modeled by considering advection of solid (biomass/char)and fluid (air/gas) streams, while conductive and radiative heatfluxes at boundaries of each CV have been modeled in terms ofeffective thermal conductivity, Keff, following Sharma et al. [16].The keff model needs inputs in terms of bed temperature, particlesize and bed porosity at current location. Here, bed porosity varieswith current particle size and modeled using Eq. (2), while emissiv-ity of char particles is fixed at 0.75.

2.3. Thermochemical processes

Modeling of the biomass thermo-chemical conversion phenom-ena: preheating, drying and pyrolysis, and chemical reactions: oxi-dation and reduction in a downdraft gasifier has been presented topredict the rate of heat generation/absorption in each CV and out-flow products.

2.3.1. Biomass dryingThe mechanism of moisture transfer to woody biomass includes

diffusion through the fluid film around the solid particles and dif-fusion through the pores to internal adsorption sites. The actualprocess of physical adsorption is practically instantaneous, andequilibrium can be assumed to exist between the surface and thefluid envelope. As moist biomass particles came into contact withair having low humidity level, the particles tend to lose moistureto the surrounding air until equilibrium is attained. For modeling,following assumptions are made:

1. No shrinkage in particle due to moisture evaporation.2. Temperature gradient in moist biomass particles is neglected.3. Equilibrium can be assumed to exist between the surface and

the fluid envelope.4. Drying is allowed to continue through pyrolysis zone as well as

oxidation and reduction zones as well.

The local thermal equilibrium between the gaseous and solidmedia is assumed in each control volume, which makes it implicitthat heat transfer between the solid and gases is much faster thanthe mass transfer. Thus, mass transfer determines the rate of mois-ture removal from the biomass particles to the gases/air flowingaround them. The analytical solution for one-dimensional mass

diffusion in a spherical particle of wood [17] is used in this work.Equations representing the drying process with coefficients arelisted in Tables 3 and 4.

2.3.2. Pyrolysis of biomassIn downdraft gasifier, the pyrolysis process is modeled at slow

heating rate to predict pyrolytic yields (viz., volatile compositionand char) and devolatilization rate as a function of temperatureand residence time. The biomass particles shrink on pyrolysis giv-ing char and ash. Following assumptions are invoked:

� Char and biomass particles are non porous.� Char yields from cellulose, hemicellulose and lignin considered

to be pure carbon.� Char yield in the gasifier is insensitive to pyrolysis temperatures

encountered in the pyrolysis zone.� The complex constituents of volatiles are assumed to be decom-

posed into CO, H2, CO2, H2O, tar (heavy hydrocarbons) and lighthydrocarbons (mixture of methane and ethylene).

The whole process of thermal decomposition of dry biomass canbe represented by a single equation as:

Dry biomass ðDBÞ !kdryChar

þ Volatiles ðCO; H2; CO2; H2O; Methane-Equivalent & TarÞð14Þ

Table 7Fractional char yields from biomass constituents.

Biomassconstituents

Cellulose Hemicellulose Lignin Reference

Fractionalcharyield

0.05 0.10 0.55 Tillmanet al. [21]

Chemicalformula

C6H10O5 C6H10O5 C9H7.95O2.4(OCH3)0.92 Grobskiet al. [23]

1390 A.Kr. Sharma / Energy Conversion and Management 52 (2011) 1386–1396

On heating, these constituents become unstable and decomposeinto char and volatiles. Furthermore, the volatiles break-up intovarious lighter hydrocarbons. For describing the volatile composi-tion and char yield during slow pyrolysis of the biomass, the pres-ent work follows the approach of Sharma et al. [20], where thethermal degradation of biomass constituents has been describedby individual decomposition scheme of cellulose, hemicelluloseand lignin. Model uses mass fractions of cellulose (Ycel), hemicellu-lose (Yhc) and lignin (Ylg) in biomass as input information given inTable 5. The chemical composition can be obtained from the ele-mental balance knowing the mass fractions, chemical formulasand molecular masses of cellulose, hemicellulose and lignin. Therate of devolatlization of biomass during slow pyrolysis processcan be described by a single pseudo-first order reaction as givenby Eq. (15) in Table 6.

Each of the three constituents of dry and ash-free biomass, viz.,cellulose, hemicellulose and lignin are considered to break up intoa fixed fraction of char and volatiles as described by Eqs. (16) and(17) in Table 6. These fractions of char from these three constitu-ents along with their chemical formula are presented in Table 7.Six species are considered to be part of the volatiles, viz., CO,CO2, H2, H2O, C1.16H4 (ME) and C6H6.2O0.2 (tar) following [24]. Thus,the process of pyrolytic decomposition of dry and ash free biomassC6HHBOOB can be represented as:

C6HHBOOB ¼ C1HcharOchar þ _nv1 COþ _nv2 CO2 þ _nv3 H2

þ _nv4 H2Oþ _nv5 C1:16H4 þ _nv6 C6H6:2O0:2 ð23Þ

2.3.3. Oxidation chemistry in gasifier bedThe pyrolysis products get oxidized in short supply of oxygen in

the oxidation zone (near air tuyers) of a gasifier. Owing to thewidely varying reaction equilibrium constants and the reactiontime scales, some of the reactions might not be attaining equilib-rium in the oxidation zone, and hence the solution of full equilib-rium equations to compute oxidation process in the gasifier wouldboth be erroneous and numerically difficult. In the present work,therefore, a heuristic approach is adopted. Oxidation of the pyroly-sis products is allowed to consume the available oxygen in a se-quence of descending order of reaction rates as described below:

Table 5Proportion of cellulose, hemicellulose and lignin in hardwood [21].

Type of wood Cellulose (Ycl) Hemicellulose (Yhc) Lignin (Ylg)

Hardwood 0.43 0.35 0.22

Table 6Equations representing to pyrolysis model.

Rate of devolatilization [22]dMvol

dt ¼ �kpyrMvol ¼ �7:0� 107ðs�1Þ expð�1560=TÞMDBYvol(15)

D _mvol;i ¼ dMvoldt

� �i¼ ðDtresÞi

dmvoldt

� �i

Char yield [20]Ychar,ash-free = YclYchar + Yhcfchar + Ylg cchar (16)Yvol = 1 � Ychar,ash-free (17)

Empirical mass ratios [20]

YCO=CO2¼ e�1:8447896þ7730:317

T þ5019898T2 (18)

YH2 O=CO2= 1 (19)

YME=CO2= 5 � 10�16T5.06 (20)

Heat of pyrolysis [20]

Dhopyr ¼ ho

f

� �DB� Ychar ho

f

� �char� Yvol

Pk¼6k¼1Yk ho

f

� �k

(21)

1. Oxidation of hydrogen (Reaction (R1) in Table 8) completesitself first.

2. If oxygen remains, light hydrocarbons are oxidized to H2O andCO (R3).

3. Oxidation is fast, and is assumed to happen instantaneouslywhenever oxygen is available.

4. Products of oxygen are assumed to attain equilibrium in eachCV.

5. If more oxygen remains, tar (R4) and char (R5) share the oxygenin the proportion of their reaction rate constants at the temper-ature of the CV under consideration to get oxidized to CO.

The principal chemical reactions taking place in the oxidationzone along with their rate expressions are listed in Table 8.Although these expressions are not used in the present computa-tions, they have been used only to guide the sequence of oxidationreactions described above.

If _nVkstands for the molar flow rate (mol/s) of species k, then

after completely consuming all the H2 in the gaseous phase (Reac-tion (R1) in Table 8), the O2 that would remain _nVO2;1 ¼ _nVO2 – _nVH2=2.If oxygen remains ð _nVO2;1 > 0Þ, light hydrocarbon or methane-equiv-alent gets oxidized to CO and H2O, therefore, _nVO2;2 ¼_nVO2;1 � 1:58 _nVCO . If more oxygen remains ð _nVO2;2 > 0Þ, simultaneousconsumption of tar (Reaction (R4)) and char (Reaction (R5) in Ta-ble 8) start taking place. The relative proportions of O2 consumedby these reactions has been accounted for by considering the ratioof the two reaction rates r* = kchar/ktar, where the reaction rates areobtained from Table 8. Two cases can be discussed: one, when thereis enough oxygen to oxidize all the tar and a proportionate quantityof char; and second, there is less oxygen than what is required tooxidize tar completely. Oxygen remains after tar oxidation if_nVO2;2 > ð1þ r�Þð4:45 _nVtar Þ. Here, 4:45 _nVtar mol/s of O2 is used up tooxidize tar and the remainder for char: thus, for every mole of charoxidized, r* moles of char are also oxidized (cf Reaction (R5)). In case_nVO2;2 < ð1þ r�Þð4:45 _nVtar Þ, all oxygen is consumed. In this case, themolar rate of tar oxidation is _nVO2;2=½4:45ð1þ r�Þ�, and the tar thatexits the zone is thus _nVtar � _nVO2;2=½4:45ð1þ r�Þ�. Correspondingly,rate of char oxidation is ½ _nVO2;2 r�=4:45ð1þ r�Þ�mol=s. This gives themoles of char oxidized in the current CV. If oxygen remains all ofit is then used to oxidize CO in a likewise fashion.

Turns [27] quoted that for fuel-rich combustion, the watershift equilibrium equation can be safely applied, therefore wecan write

_nVCO2_nVH2= _nVCO : _nVH2O ¼ KpðTiÞ ¼ expð�DG0ðTiÞ=RuTiÞ ð24Þ

where DG0ðTiÞ ¼ g0COðTiÞ þ g0

H2OðTiÞ � g0CO2ðTiÞ � g0

H2ðTiÞHere, DG0(Ti) is the standard-state Gibbs function changes at

atmospheric pressure. The Gibbs function g0 for each species canbe calculated using Eq. (42).

2.3.4. Modeling reduction chemistry in gasifier bedReduction of the oxidation zone products are primarily domi-

nated by heterogeneous reactions of solid–char (R6)–(R8) andhomogeneous reactions of gas–gas (R9) in complete absence of

Table 8Chemical reactions in oxidation zone.

Reac. no. Oxidation reactions Rate expressions Aj Ej/Ru Ref.

R1 H2 + 0.5O2 ? H2O kH2 = ACOT1.5exp(�ECO/RuT)[CCO2 ][CH2 ]1.5 1.63 � 109 3420 [25]R2 CO + 0.5O2 ? CO2 kCO = ACOexp(�ECO/RuT)[CCO][CO2 ]0.25[CH2O]0.5 1.3 � 108 15,106 [25]R3 aC1.16H4+1.58O2 ? 1.16CO +2H2O kME = ACH4 exp(�ECH4 /RuT)[CO2 ]0.8[CCH4 ]0.7 1.585 � 109 24,157 [25]R4 bC6H6:2o0:2 +4. 45O2 ? 6CO + 3.1H2O ktar ffi kHC = AtarTP0:3

A exp(�Etar/RuT)[CO2 ]1[CHC]0.5 2.07 � 104 41,646 [26]

R5 C + 1/2O2 ? CO kchar = Achar exp(�Echar/RuT) [CO2] 0.554 10,824 [25]R6 CO + H2O M CO2+H2 – – – –

a C1.16H4 (light hydrocarbon or methane-equivalent).b C6H6:2o0:2 (heavy hydrocarbon) represents the methane and tar respectively.

Table 9Reduction reactions, their reaction rates and constants.

Reac. no. Reaction Rate expression Aj [8] Ej (kJ/mol)[7]

R6 C + CO2 M 2 CO r1 ¼ A1 exp �E1Ru T

� �PCO2 �

PCOKeq;1

� �3.616 � 104 77.39

R7 C + H2O M CO + H2 r2 ¼ A2 exp �E2Ru T

� �PH2 O �

PCOPH2Keq;2

� �1.517 � 107 121.62

R8 C + 2H2 M CH4 r3 ¼ A3 exp �E3Ru T

� �P2

H2� PCH4

Keq;3

� �4.189 19.21

R9 CH4+H2O M CO+3H2 r4 ¼ A4 exp �E4Ru T

� �PCH4 PH2O �

PCOP3H2

Keq;4

� �7.301 � 101 36.15

A.Kr. Sharma / Energy Conversion and Management 52 (2011) 1386–1396 1391

oxidants. These reduction reactions are inherently slower than theoxidation reactions by several orders of magnitude, thus, equilib-rium may not be established in the reduction region. At moderatelyhigh temperatures (<800 �C), the equilibrium products may deviatefrom reality, thus, kinetic or non-equilibrium models are moresuitable and accurate[28]. In the present work, therefore, a steadystate kinetic model for reduction reactions has been employed fol-lowing [4,6]. Kinetic model predicts the un-reacted char and finalgas composition. For modeling of reduction chemistry in reductionzone, following assumptions were made:

1 Reduction reactions are slow reactions, and are treated usingthe kinetics of these reactions.

2 All char is consumed by the end of reduction zone3 The average diameter of the ash particle is 5 mm.

The reaction rates of global reduction reactions (R6)–(R9) canbe described by the departure of the reactant concentrations fromtheir equilibrium values and their values of pre-exponential factorsAj and activation energies Ej for reactions j = 1 . . .4 are given byWang and Kinoshita [7]. CRF is the char reactivity factor, which rep-resents the reactivity of char (or number of active sites on the charsurface) and is a key parameter in simulation of fixed bed gasifica-tion. As char burn-off proceeds, the char size decreases and charporosity increases, the gas would encounter more active sites.The higher CRF, the process becomes more fast. Giltrap et al. [8] rec-ommended a constant value of 1000 for the char reactivity factor(CRF). In the present work, the same value of char reactivity factorhas been included in order to account for the active sites presenton char surface (cf. Table 9). The symbol Pk is the partial pressureof gaseous species k of the reduction zone. Keq,j is the equilibriumconstant for reaction j.

The net rate of production of the kth species (Rtk) thus can beevaluated in terms of the above reaction rates: for instance,RtCO = 2r1 + r2 + r4; RtH2 = r2 � 2r3 + 3r4, etc. These Rtk values ofkth species can be used to compute outflow species concentrationfor known inflow concentration of each species and volume of eachCV (VCV) as:

_nk;i ¼ _nk;i�1 þ VCV ;iRtk;i ð25Þ

3. Solution procedure

For fluid flow module, assuming suitable guess of biomass con-sumption rate, the airflow rate can be calculated using global massbalance of produced gas, total air, wet biomass and ash. For a giveninput of gas flow rate at gasifier exit and airflow rate, Eq. (3) for thepressure drop through the tuyers and Eq. (4) for pressure drop ingasifier bed are related in terms of air/gas flow rates through eachCV. Fluid flow rates through these CVs are also related to consump-tion of solid substrate (e.g. dry biomass, moisture in biomass, charand ash) by the intrinsic mass balance for each CV. Thus, the sum ofpressure drops across the preheating, drying, and pyrolysis zonesin terms of fluid flow rate through them can be related to pressuredrop across the tuyers as:

DPpreheat þ DPdry þ DPpyro ¼ DPtuy ð26Þ

Above Eq. (26) in conjunction with Eqs. (3) and (4), gives ratio of aircoming from the open top and through the tuyers. This ratio influ-ences the reaction temperature profile in the bed and thus thechemistry of gasification. In the second stage, which correspondsto heat transfer module, here the energy Eq. (6) in conjunction withEqs. (7)–(8), was solved for temperatures in each CVs simulta-neously using tri diagonal matrix algorithm (TDMA) with knownvalues of heat generation/absorption in different zones. When tem-perature specifications in each CV are known, the actual mass con-version and heat released or absorbed in each CV has been obtainedusing thermochemical phenomena sub-models.

For preheat and drying zone, equilibrium mass fraction of mois-ture in wood, Xeqb, in each CV is computed using vapour–liquidequilibrium relationship, while the knowledge of residence timeand diffusivity gives Xout, the moisture mass fraction of the biomassleaving the CV is calculated using mass transfer one-dimensionaldiffusion Eq. (9) in conjunction with Eqs. (10) and (13), the quan-tity of moisture evaporated from the wood particles and heat ofvapourization can be quantified. The pyrolysis products includingchar and volatile components are obtained using elemental bal-ances for C, H and O and empirical mass ratios as a function of tem-perature as written by Eqs. (18) and (20) in Table 6. Once outletproducts is known this gives heat of pyrolysis, which serves inputto heat transfer module.

Table 10Property data.

Thermal conductivitykb = Sgb(0.1941 + 0.4064Yw) + .1864 + .002 (T � TA) [26] (27)kchar = 1.4 � 10�6T2 � 6.4 � 10�4T + 0.211 [29]kk = Ak + BkT + CkT2 + DkT3 [30] (29)

kmixture ¼Pk

k¼1vk kkðmwkÞ0:333Pk

k¼1vkðmwkÞ0:333

[31] (30)

Specific heatCpDB = 0.1031 + 0.003867T [26] (31)Cpb = [CpDB + 4.19Yw]/(1 + Yw) + (0.02355T � 1.32Yw � 6.191)Yw [26] (32)Cpchar = 1.39 + 0.00036T [26] (33)Cpk = ak + bkT + ckT2 + dkT3 + ekT4 [24] (34)

Cpmixture ¼Pk

k¼1YkCpk(35)

Viscosity [30], [32]lk(T) = lk(Ta)(T/Ta)n (36)lH2O = 7 � 10�12T2 + 5.1 � 10�8T � 6.04 � 10�6 (37)lTar lBenzene = �1.3404 � 10�11T2 + 3.5844 � 10�8T � 2.2588 � 10�6 (38)

lmixtureðTÞ ¼Pk

k¼1vklkPI

I¼1vkukI

(39)

where ukI ¼ð1þðlk=lI Þ

0:5ðmwI=mwkÞ0:25Þ2

2:828ð1þðmwk=mwI ÞÞ0:5

Enthalpy

h0f ;mixture ¼

PkYkh0

f ;k(40)

Heating value [33]hhvDB = 341CDBp + 1323HDBp + 68SDBp � 15.3ashDBp � 120(ODBp + NDBp) (41)

Gibbs function [6]

g0kðTÞ ¼ Agk þ BgkT þ CgkT2 þ DgkT3 þ EgkT4 þ Fgk=T þ Ggk lnðTÞ (42)

6789

10

re d

rop

(mm

wc)

1392 A.Kr. Sharma / Energy Conversion and Management 52 (2011) 1386–1396

For oxidation zone, using temperature specifications from heattransfer module, the value of Kp determined in terms of standardstate of Gibbs function change for water gas shift reaction. UsingKp value in Eq. (24) and the atomic balances, the final compositionof gases leaving the oxidation zone can be determined. The heat re-leased in the oxidation zone has been computed from the enthalpyof formation of the reactants and products. Finally, the char con-sumption and gas composition through the reduction zone canbe obtained solving kinetic rate Eqs. (R6)–(R9) for known reactiontemperature profile. In reduction zone each CV has been subdi-vided into 100 subdivisions to ensure adequate accuracy of ele-mental balances.

The equilibrium constants Keq,j for jth reaction are evaluated atthe temperature of the CV from standard state Gibbs functions ofthe gaseous species k, go

k from Eq. (42). The polynomial fits for stan-dard state enthalpy and entropy used to compute the Gibbs func-tions as a function of temperature are obtained from NASA fitson JANAF Tables data [27]. Similarly, heat absorption in reductionzone has been obtained using heats of formation of the reactantsand products. The thermo-physical properties of working sub-stances in terms of temperature are listed in Table 10, the valuesof constants used in Table 10 are obtained from their respectivereferences. The consumption of char in reduction zone dependsmainly on feedstock composition and equivalence ratio of the gas-ifier, the temperature of reduction zone. The equivalence ratio ofthe gasifier was controlled by the airflow rate. The ratio of air tobiomass was adjusted so that the char flow rate at gasifier exit be-comes zero.

012345

18 20 22 24 26 28 30

Air flow rate (g/s)

Gas

ifier

Pre

ssu

Experimental datadb=34mmdb=42mm

Fig. 3. Comparison with experimental data(freshly charged gasifier) for uniformlydistribution of particle size, Tbed = 300 K, cold flow.

4. Model predictions and validation

A 20 kWe open top downdraft biomass gasifier developed in In-dian Institute of Technology, Bangalore has been chosen. Theexperimental data of Sharma [11], generated on the same configu-ration has been used in the present work for validation or testing ofvarious modules and overall gasifier model.

4.1. Validation or testing of modules constituting the gasifier model

The modules that constitute the gasifier model have been vali-dated against the experimental data or tested for qualitativetrends. The predictions of fluid flow module for pressure drop incold flow have been validated against the experimental data ofSharma [11] for given particle size distribution and flow rate atthe gasifier exit. Since the pressure drop is a strong function of par-ticle size, the two sets of experimental data has been used in thepresent work; one set for freshly charged gasifier with nearly uni-form sized particles, while second set for extinguished gasifier (bedwith decreasing particle size downwards in the direction of gasflow). Simulations are performed: (i) for uniform distribution ofparticle diameter (ii) for spatially varying particle size distribution,as given by Eqs. (1) and (2). Results from the simulations are com-pared with those from the experiments in Figs. 3 and 4 for an initialparticle size in the range between 34 and 42 mm. The predictions,for same range of particle sizes are in reasonable agreement withmeasured values of pressure drop for the case of extinguished gas-ifier, while for freshly charged gasifier, the predictions deviate

0

5

10

15

20

25

10 15 20 25 30Air flow rate (g/s)

Gas

ifier

Pre

ssur

e dr

op (m

mw

c)

Experimentsdb=34mmdb=42mm

Fig. 4. Comparison with experimental data (extinguished gasifier) for spatiallyvarying of particle size distribution of, Tbed = 300 K, cold flow.

A.Kr. Sharma / Energy Conversion and Management 52 (2011) 1386–1396 1393

slightly at higher flow rates. This may be due to the fact that theparticles are not perfectly spherical and due to uncertainty associ-ated with particles (size) constituting the freshly charged bed.

The heat transfer module uses the heat released/absorbed ineach zone as the input to predict the temperatures in each zone.Since the heat released/absorbed in an actual gasifier is closelycoupled with all other parameters, it was not possible to validatethe heat transfer part in isolation against experiments. Therefore,well tested model (tested for qualitative trends) of Sharma for heattransfer [16], has been followed in the present work. The dryingmodel is tested in the preheat zone (of length 1 m) in the gasifierfor the effects of zone temperature and particle diameter for qual-itative trends as shown by Figs. 5 and 6. Fig. 5 shows the trends for

0

0.02

0.04

0.06

0.08

0.1

0.12

0 20 40 60 80 100Distance along preheating zone (cm)

Moi

stur

e co

nten

t in

biom

ass

T=350KT=400KT=500KT=600K

Fig. 5. Effect of drying zone temperature on moisture loss profile, dp = 4 cm.

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0 20 40 60 80 100Distance along preheating zone (cm)

Moi

stur

e co

nten

t in

biom

ass dp=1cm

dp=2cmdp=3cmdp=4cmdp=5cm

Fig. 6. Effect of particle size on moisture loss in drying zone, T = 400 K.

moisture loss distribution along the testing bed for four isothermaltemperatures i.e., 350, 400, 500 and 600 K. The results show that astemperature increases, the biomass dries up quickly within theshort length along the testing bed, as expected. In order to studythe effect of particle size on moisture evaporation; five levels ofaverage particle size i.e. 10, 20, 30, 40 and 50 mm are consideredin this analysis (Fig. 6). Predicted results shows faster biomass dry-ing with decrease in particle diameter, as expected.

A well tested pyrolysis sub-model of Sharma et al. [20] is usedto predict the species concentration in volatile matter and charyield at known pyrolysis temperature. It uses input of the percent-ages of three major constituents – cellulose, hemicellulose and lig-nin in biomass and fraction of char due to the breakup of each ofthese three constituents from Tables 5 and 7. For validation ofthe oxidation module, the oxidation of volatiles alone has beenconsidered. The products of oxidation of volatiles predicted bythe present model have been compared with equilibrium code ofOlikara and Borman as given in Ref. [27], which uses input in theform of CNHMOLNK and equivalence ratio U. Volatiles are consid-ered to have the chemical formula of C1.3H3O1.4. Char oxidationhas been excluded from the validation part since the code of Olika-ra and Borman is meant only for those reactions which are ex-pected to reach equilibrium. Fig. 7 shows the comparison of CO,H2 and CO2 contents in the products of oxidation as predicted bythe present model with the predictions of the code of Olikaraand Borman, for an equivalence ratio U = 1.85. The comparison isfound to be quite good. These figures also show the variation inthe content of these species with the reaction temperature. Withincrease in temperature, the CO content increases while H2 andCO2 decrease, as expected. For reduction environment, a welltested kinetic model for reduction reactions has been used [4,6].

4.2. Validation of gasifier model

After the validation and testing of above modules individually,it is also essential to validate the overall gasifier model after cou-pling of these modules. The gasifier model predicts the pressuredrops, biomass consumption rate, airflow rates, gas compositionand its calorific value for a given value of producer gas flow rateand size of the feedstock particles being fed from the top.

For validation, the experimental data of Sharma [11] on the20 kWe downdraft gasifier has been used at wide range of pro-ducer gas flow rate. In his experiments, Sharma used sun driedKikar wood (Acacia), chopped in cubic shape with average size36 mm having average moisture content in the range of 11–13%on dry basis. Simulations are also performed for the similar operat-ing condition for gasification of hardwood feedstock. However,

5

6

7

8

9

10

11

12

13

14

1100 1200 1300 1400 1500Temperature in oxidation zone (K)

Com

posi

tion

(%vo

l)

CO: Present work CO: ’PER’ model

H2: Present work H2: ’PER’ model

CO2: Present work CO2: ’PER’ model

Fig. 7. Comparison of predicted product composition of oxidation model with thoseobtained using Olikara and Borman code [27].

5

10

15

20

25

30

Gas flow rate (g/s)

Gas

com

posi

tion

(%vo

l)

Experiments (H2) Predictions(H2)Experiments (CO) Predictions (CO)

4 9 14 19 24 29

Fig. 10. Comparison of predicted CO and H2 composition in producer gas withexperiments.

2000

2500

3000

3500

4000

4500

5000

rific

val

ue o

f gas

(kJ/

m3)

ExperimentsPredictions

1394 A.Kr. Sharma / Energy Conversion and Management 52 (2011) 1386–1396

since in experiments, the particle size even at the gasifier inlet var-ies considerably, the choice of a constant particle size at gasifier in-let can have a strong bearing on comparison of simulation with theexperimental values. Thus, for comparison with experimental data,the simulation results have been plotted in particle size range from36 to 50 mm. The pressure drop across the gasifier predicted by themodel for various producer gas flow rates is compared with theexperimental values in Fig. 8. This deviation could be owing tothe uncertainty in the particle size of the feedstock. It is observedthat the predicted pressure drops are in agreement with the mea-sured data within the experimental uncertainty. Predicted temper-ature profile in the gasifier bed at gas flow rate of 7 g/s has beencompared with experimental data as shown in Fig. 9. As expected,the maximum temperature in oxidation zone predicted by themodel is 1217 K at the gas flow rate of 7 g/s. A good agreementof predicted and measured temperature profile across the bedcan be clearly observed.

In Fig. 10, predicted gas composition has been compared withexperimental data of Sharma. Predictions for CO and H2 percentagein gas increase gently with gas flow rate. The theoretical trends forCO and H2 composition are in good agreement with experimentalmeasurements of Sharma[11]. The calorific value of the gas fromprediction and experiments is compared in Fig. 11, and good agree-ment is obtained. Since the experimental data is limited to a littlerange of gas flow rate, the predictions have been extended to 30 g/s, in order to demonstrate the predictable capability of the abovemodel at higher flow rates. Model predicts a very small percentageof CH4 below 0.4%, water vapour varies from 10% to 12% and tar

0

5

10

15

20

25

30

35

0 2 4 6 8 10 12 14 16Producer gas flow rate (g/s)

Gas

ifier

Pre

ssur

e dr

op (m

mw

c)

dp=36mmdp=50mmExperiments

Fig. 8. Comparison of the predicted pressure drop with experimental data, spatiallyvarying particle size, hot flow.

250

450

650

850

1050

1250

1450

Distance from open top (cm)

Tem

pera

ture

(K)

Experiments

Predictions

0 50 100 150 200

Fig. 9. Comparison of predicted temperature profile with experimental data,db = 36 mm, mpg = 7.0 g/s, Hardwood.

1000

1500

4 9 14 19 24 29Gas flow rate (g/s)

Cal

o

Fig. 11. Comparison of predicted calorific value of gas with experiments.

content was theoretically absent in the resulting gas for the widerange of gas flow.

4.3. Gas flow rate

Some trends of pressure drop, temperature profile, dry gas com-position and calorific value against gas flow rate have been dis-cussed in previous section (cf. Figs. 8–11). In this section, thetrends of temperature profiles across gasifier bed for different val-ues of gas flow rates; cold gasification efficiency and gasifier poweroutput for wide range of gas flow rate are studied as shown in

250

450

650

850

1050

1250

0 50 100 150 200Distance from open top (cm)

Tem

pera

ture

(K)

mpg=6g/smpg=9g/smpg=12g/smpg=17g/smpg=21g/s

Fig. 12. Effect of producer gas flow rate on temperature profile in the gasifier.

65

67

69

71

73

75

77

79

5 10 15 20Gas flow rate (g/s)

Gas

ifica

tion

effic

ienc

y

0

10

20

30

40

50

60

70

80

90

100

Gas

ifica

tion

pow

er o

utpu

t (kW

)

Conversion efficiencyGasifier power output

Fig. 13. Effect of producer gas flow rate on gasification efficiency and gasifier poweroutput (kW).

A.Kr. Sharma / Energy Conversion and Management 52 (2011) 1386–1396 1395

Figs. 12 and 13. The variations in temperature profiles for five dif-ferent gas flow rates viz., 6, 9, 12, 17 and 21 g/s have been com-pared in Fig. 12. As expected, the maximum temperatures(predicted) can be observed in oxidation zone. The overall temper-ature profiles at increasing gas flow rates are found to be improv-ing. A maximum temperature is found to be increasing from1141 K to 1354 K for typical gas flow rate variation of 6–21 g/s.The gasification efficiency on cold basis can be described in termsof the ratio of net heating value of gas at ambient (neglecting thesensible heat) to the input energy intake by biomass feedstock.The heating values of biomass and product gas at the gasifier exitcan be obtained from literature [26,27,31] in terms of heating val-ues of individual components. With these heating values, the gas-ification efficiency (cold basis) and gasifier power output can becomputed and results of cold gasification efficiency and gasifierpower output (kW) are plotted in Fig. 13. The cold gasification effi-ciency is observed to be increasing from 72% to 74% with gas flowrate variation from 6 to 25 g/s. A steep increase in gasifier poweroutput (21–92 kW) can be observed (almost linear trend) for abovegas flow rate variation.

Increase in gas flow rate improves the temperature profile lead-ing transformation of the non-combustibles components (i.e. CO2,H2O) into combustibles (i.e. CO, H2) and thus improving the calo-rific value of the product gas, the cold gasification efficiency andgasifier power output as well. However, the temperatures in dryingand pyrolysis zone are lower at higher flow rates, and thus thepressure drop in these regions may be less at higher flow rate.But in reduction zone, where maximum char conversion takesplace, the particle sizes are the smallest, has higher temperatureat higher gas flow rates. This would add significantly to the pres-sure drop. The predicted trends agree with this expectedbehaviour.

5. Conclusions

A mathematical model EQB for a downdraft biomass gasifier hasbeen developed to predict the pressure drop, airflow rate fromopen top and through the tuyers, biomass consumption, tempera-ture profile and gas composition for given gas flow rate. Model wasdeveloped in three stages: first stage, fluid flow module is carriedout, where isothermal flow of air was considered through the gas-ifier bed; second stage corresponds to heat transfer module, hereenergy equation was solved to obtain the temperatures in eachCV with heat generation/absorption in different zones consideredas known; third stage, the physical and chemical phenomena take

place due to biomass drying, pyrolysis, oxidation and reductionreaction sub-process, and their energetics decide the heat genera-tion or absorption in each CVs. The subroutines constituting thegasifier model have been validated or tested. The fluid flow modulehas been validated in cold flow for constant particle size (freshlycharged gasifier) as well as for variable (decreasing) particle sizedistribution in gasifier bed (due to thermochemical conversion).Mass transfer model for biomass drying have been tested in pre-heating zone and found working well for right trends of responseto particle size, rate of drying and prevailing temperature. Equilib-rium based oxidation model is validated with the equilibrium codeof Olikara and Borman and found to be robust and adequate forprediction of product composition, but predicts a steep tempera-ture rise within a single control volume where oxidation completesitself. Finally, the gasifier model was validated against the experi-mental data with good agreement.

For the range of gas flow rate encountered in this work, anyimprovement in the reaction temperature leads to better thermo-chemical transformation of biomass material into combustibles(i.e., CO, H2), thus, improving the gasifier performance in termsof energy efficiency and power output. The rise in gasifier temper-ature due to chemical reactions specially at high gas flow rate alsostrongly influences the gasifier pressure drop. Furthermore, reduc-tion zone is recognized as the most sensitive region for remarkablyhigh pressure drop, where highest char conversion leads to small-est particle sizes and high reaction temperatures as well speciallyat higher gas flow rate.

Chemical equilibrium for oxidation zone (where reaction tem-peratures proceeds beyond 800 �C establishing equilibrium) andempirically predicted pyrolysis products (volatiles and char) allow-ing faster convergence, while implementing kinetic modeling forreduction zone is helpful in restoring the accuracy of predictions(where reaction temperatures less than 800 �C and thus equilib-rium is far away from reality). This combination constitutes an effi-cient algorithm allowing rapid convergence with adequate fidelity.When, objective is to couple a gasifier model with a gas enginemodel for predicting the performance of a gasifier–engine systemmodel, the above algorithm of gasifier simulation may be a prefer-able choice.

Acknowledgements

Author is grateful to Prof. M.R. Ravi and Prof. S. Kohli, IndianInstitute of Technology, Delhi for their valuable contribution in car-rying out of mathematical modeling and computational work.

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