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

Energy Conversion and Management 52 (2011) 1386–1396

Contents lists available at ScienceDirect

Energy Conversion and Management
journal homepage: www.elsevier.com/locate/enconman

Modeling and simulation of a downdraft biomass gasifier 1. Model development
and 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

Article history:
Received 29 December 2009
Received in revised form 27 September
2010
Accepted 3 October 2010
Available online 29 October 2010
Keywords:
Modeling
Simulation
Biomass gasification
Equilibrium
Kinetics
Suction gasifier

a b s t r a c t

An ‘EQB’ computer program for a downdraft gasifier has been developed to predict steady state performance. Moving porous bed of suction gasifier is modeled as one-dimensional (1-D) with finite control
volumes (CVs), where conservation of mass, momentum and energy is represented by fluid flow, heat
transfer analysis, drying, pyrolysis, oxidation and reduction reaction modules; which have solved in integral form using tri-diagonal matrix algorithm (TDMA) for reaction temperatures, pressure drop, energetics and product composition. Fluid flow module relates the flow rate with pressure drop, while biomass
drying is described by mass transfer 1-D diffusion equation coupled with vapour–liquid-equilibrium relation. 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 algorithm
allowing rapid convergence with adequate fidelity. Predictions for pressure drop and power output (gasifier) are found to be very sensitive, while gas composition or calorific value, temperature profile and gasification efficiency are less sensitive within the encountered range of gas flow rate.
Ó 2010 Elsevier Ltd. All rights reserved.

1. Introduction
Thermochemical conversion of woody biomass under restricted
supply of oxidant is among the most promising non-nuclear forms
of future energy. Besides utilizing a renewable energy sources, the
technology also offers an eco-efficient and self sustainable way of
obtaining gaseous fuel usually called producer gas. It can be used
in either premixed burners (dryers, kilns, furnaces or boilers) for
thermal applications or in direct feeding of high efficiency internal
combustion engines/gas turbines for mechanical applications.
After adequate cleaning up and reforming, the generated gas can
also be used for feed high temperature fuel cells or for production
of hydrogen fuel [1]. For electric power generation applications, the
motive power from prime mover such as IC engine or gas turbine
can be connected to an electric generator to produce electric energy. Applications of IC engines have proved to be the most efficient and least expensive decentralized-power-generation
systems at lower power range. Research efforts have been expanded worldwide to develop this technology cost-effective and
efficient in lower power range.
Recent progression in numerical simulation techniques and
computer efficacy become the effective means to develop more advanced and sophisticated models in order to provide more accurate

⇑ Tel.: +91 09416722212; fax: +91 01302484004.
E-mail address:
0196-8904/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved.

doi:10.1016/j.enconman.2010.10.001

qualitative and quantitative information on biomass gasification.
In the present work, the objective is not merely to develop a theoretical model of a downdraft gasifier system, but also to develop an
efficient algorithm that allow rapid convergence and adequate
accuracy of predictions. Presently, the gasification modeling techniques include the application of thermodynamic equilibrium,
chemical kinetics, diffusion controlled, diffusion–kinetic approach
and CFD tools. None of approaches have clear advantage over the
others. Pure equilibrium approach has thermodynamic limitations,
instead of its inherent advantage of being generic, relatively easy to
implement and rapid convergence, even though, researchers have
successfully demonstrated the application of equilibrium chemistry in downdraft gasifiers. Zainal et al. [2] reported an interesting
model for biomass gasifier describing the equilibrium calculations
considering water–gas shift and methane–char reactions. Melgar
et al. [3] combine chemical and thermal equilibrium in order to
predict gas composition and Baratieri et al. [1] presented an
equilibrium model based on minimization of Gibbs energy using
Villars–Cruise–Smith (VCS) algorithm. They validated the predictions successfully. Later, Sharma [4] has compared the theoretical
predictions of reduction zone using equilibrium, kinetic modeling
and experimental data. For optimum performance, Sharma has
identified a critical length for the reduction zone (where all char
gets converted). At a more sophisticated level, Ratnadhariya and
Channiwala [5], suggested that separate thermodynamic modeling
can be approached to different zones of a downdraft gasifier. On
the other hand, non-equilibrium formulations such as kinetic rate


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

1387

Nomenclature
A
d
f
k
M
_
Dm
R
T
V
[C]
D
h
L/l
_ n_
m=
DP
Ru
D
t
_ res
r
CV
Dh
hhv
dL
ME
Q_

Re
Y
Sg

area (m2)
particle diameter (m)
friction factor
thermal conductivity/rate constant
mass
solid mass conversion
thermal resistance
temperature (K)
volume/velocity
species concentration
diffusion coefficient/diameter
enthalpy (kJ/kg)
length (m)
mass/molar flow rate
pressure drop
universal gas constant
residence time in CV
diameter ratio of annulus
control volume
hydraulic diameter (m)
high heating value (kJ/kg)
length of CV
methane-equivalent
heat flow or release/absorbed
Reynolds number
mass fraction or ratio

specific gravity

Greek letters
l
dynamic viscosity (kg mÀ1 sÀ1)
erad
radiative emissivity
q
density
r
Stefan–Boltzmann constant

and/or diffusion controlled model including CFD tools are more
accurate, no doubt, but are detailed and computationally more
intensive. It takes time for convergence by a few orders of magnitudes [6]. Non-equilibrium approaches use char conversion as a
surface phenomena describing by char reactivity and global reactions of char–gas and gas–gas reactions. An effective global rate
constant may be defined to account for both diffusion and kinetics
of these reactions. Wang and Kinoshita [7] modeled the kinetics of
the heterogeneous and homogeneous reactions of char conversion
in reduction zone for a given residence time and bed temperature,
while Giltrap et al. [8] used the reaction kinetics parameters reported by Wang and Kinoshita in order to develop a model of the
gas composition and temperature for char reduction zone of a
downdraft gasifier. Babu and Sheth [9] further modified these reaction rates using a variable char reactivity factor to predict the results agreeing with experimental data. Later, Gao and Li [10]
presented the downdraft gasifier model by combining a pyrolysis
model (based on Koufopanos scheme) and reduction model following [7–9] to simulate the temperature field and gas concentration
field in time and space.
The overall pressure drop across the gasifier system is an important parameter. It monitors not only the health of a suction gasifier
but also the volumetric efficiency of engine and hence the engine
power output. The pressure drop across the conventional packed
bed depends on system geometry, medium porosity, permeability

and physical properties of working medium. Unlike, in gasifiers
the bed maintains widely varying temperature specifications, particle size distribution and bed porosity. Such study on pressure
drop through a downdraft biomass gasifier bed is limited in open

x
eb
n

humidity
bed porosity
correction factor for annulus

Subscripts/superscripts
i
number of CVs
A/a
ambient
cel
cellulose
DB
dry biomass
dev
developing flow
mfd
modified fully developed
preheat preheating zone
tuy
tuyer
sat
saturated vapour

j
reaction number
an
annular region
hc
hemicellulose
dry
drying zone
f
fluid (gas/air)
p
particle
pg
producer gas
vol/v
volatile
w
moisture
k
species
ash
ash
lg
lignin
eff
effective
fd
fully developed flow
pyr
pyrolysis

s
solid (biomass, char, ash)
red
reduction zone
DBp
mass percentage in dry biomass

literature. Sharma [11], measured the pressure drops across the
gasifier bed at various particle size arrangements in cold and hot
flow, and at various locations of a 20 kWe open top downdraft gasifier in addition to temperature profile, gas composition, calorific
value. These data has been used in this work.
In fact, the selection of level and modeling approach (viz. chemical equilibrium, chemical kinetics or diffusion controlled) depends
on statement of the problem and therefore may vary considerably
from one case to another. Since, the objective of the present work is
not to invoke the highest level or most sophisticated gasifier model, yet it is an attempt to develop an efficient algorithm that enable
rapid convergence without affecting the validity. Such comprehensive work, in fact, is missing in the archival literature. This comprehensive work, therefore, presents the modular treatment (allowing
scope of further improvement at module level) to fluid flow, heat
transfer, biomass drying, pyrolysis, and oxidation and reduction
reactions processes to form a powerful tool for simulation of suction (downdraft) gasifier. Here biomass drying has been described
via thermal equilibrium, where mass transfer determines the rate
of moisture removal from wet biomass particles. Devolatilization
rate and pyrolysis products is described by single pseudo-first order reaction and empirical model, chemical equilibrium for rapid
convergence in oxidation zone (>800 °C), and kinetic scheme for
reduction zone (<800 °C), constitutes an efficient algorithm for suction biomass gasifier allowing considerable saving in iterative time
without degradation in accuracy of predictions. Such gasifier algorithms are desired when combining a gasifier model with a gas–
engine model towards simulation/optimization of gasifier–engine
system. Predictions of model and its subroutines have been


1388

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

validated, which are showing realistic performance including pressure drop trends in cold and hot flow condition.
2. Mathematical formulation
The packed bed of the biomass gasifier has been modeled as a
porous medium in which fluid flow rate increases in the direction
of flow due to thermo-chemical conversion of solid particles constituting the bed. The flow of air and biomass consumption in
the 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 thermo-chemical processes are described by five separate zones in
addition to annular jacket zone as shown in Fig. 1, however, the actual dividing lines between these zones evolve as solution proceeds. The upper end of the oxidation zone starts at the elevation
of the tuyers, and all the other zones are determined by their
respective temperatures as the solution evolves.
For modeling, these zones are further subdivided into a number
of CVs for analysis, where each CV has been characterized by the
average values of parameters such as temperature, particle size,
fluid flow rate, reactor diameter, etc. In all CVs, the solid particles
are considered spherical, with uniform diameters. In the drying
and preheating zones, there is no shrinkage in particle size due
to drying and preheating process. However in pyrolysis, oxidation
and reduction zones, feedstock undergoes chemical reactions leading to change in the particle size, thus, the diameter is allowed to
vary from one CV to another. The revised particle size is computed
from the assumption of constant intrinsic density;

mp
mp;initial

3

¼

dp

ð1Þ

3

db

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

eb ¼ 0:5 À 0:2ð1 À dp =db Þ

ð2Þ

2.1. Fluid flow
Initial phase for gasifier model development is fluid flow modeling, which is used to quantify the apportionment of air inflow
from the open top and through the air tuyers, and the pressure
drop through the gasifier bed as a function of flow rate. The tuyres

Air (Top)

Biomass
Producer gas

Preheating zone

Drying zone

Annular Jacket regeneration zone

Pyrolysis zone
Oxidation zone

Air (Tuyers)

Table 1
Fluid flow: pressure drop equations.
Tuyers
DP tuy ¼ ðP atm À P in;tuy Þ þ ðP in;tuy À P exit;tuy Þ
À
Á qV 2
À
Á 2
qV 2
¼ 2in þ f DL þ K dev 2in ¼ 1 þ f DL þ K dev q2V

(3)

Pressure drop parameter [13]

 
 2 
Ldev
Ldev
À 1:43 Â 10À6 = ReÁD
K dev ¼ exp 0:3 À 2:9 Â 10À3 = ReÁD

(3a)

Ldev
D

(3b)

ffi 0:06Re; for Re < 2300
ffi 4.4Re1/6; for Re > 4000

(3c)

Porous Bed: Ergun equation [14]

DP i ¼

150ð1Àeb;i Þ2 lðT i Þli

qðT i Þe3b;i d2p;i AT

_ f Þi þ
ðm

Concentric annulus [15]


DP an ¼ K dev þ fmfd DdLeff1
Reeff = ReDh/n;
_

n¼

(5)

_ 2pg
m

Deff = Dh/n

_

_
ð1À r 4 Þþð1À r 2 Þ2 = lnð r Þ

(4)

2qpg A2an

_

ð1À r Þ2 ð1À r 2 Þ
_

1:75ð1Àeb;i Þli
_ f Þ2i
ðm
qðT i Þe3b;i dp;i A2T

_

where r ¼ r o =ri

(5a)
(5b)

are straight pipes of circular cross-section, the pressure drop can be
computed from the Darcy–Weisbach equation. The entrance and
developing flow effect through the tuyers has been modeled in
terms of average entry length pressure drop parameter Kdev, fitted
to the data of Schmidt and Zeldin in Ref. [13] as given in Table 1.
The pressure drop through the gasifier bed (maintaining widely
varying temperature specifications, particle size distribution and
bed porosity) has been obtained using Ergun correlation [14] for
complete flow regime. Pressure drop through concentric annulus
is modeled from modified Darcy–Weisbach friction-factor in terms
of effective Reynolds number and effective (annulus) diameter as
reported in [15]. The details of equations describing the flow resistance across the tuyers, porous bed and annulus are given in
Table 1.
2.2. Heat transfer
For heat transfer analysis, the approach of Sharma et al. [16] has
been followed in the present work. Here, fuel bed is assumed to be
isotropic; solid and gases are considered to be in local thermal
equilibrium. These assumptions are justified for fixed bed gasifiers
operating under steady state conditions, since residence time of
solids in the CV is two to three order magnitude higher than that
of gases. This module describes the formulation of energy interaction for the heat inflows and outflows due to advection of fluid and
solids, heat loss through insulated wall, internal thermal interaction between adjacent CVs and the quantity of heat generated or
consumed in each CV in order to compute the reaction temperature of each CV. In developing heat transfer module, the heat generated/absorbed during drying, pyrolysis, oxidation or reduction is
prescribed as input. These would subsequently be determined by

the modules of the respective sub-processes (cf. Eq. (6) in Table 2)

Table 2
Heat transfer equations.
Energy equation
hP
i
P
_
_
_
_
_
solid ðmi Cpi Þin þ
gases ðmi Cpi Þin T in þ Q v ap þ Q pyr þ Q oxid
hP
i
P _
P
_
_
_
þQ red þ jk Q dif ;jk ¼
solid ðmi Cpi Þout þ
gases ðmi Cpi Þout T out

(6)

Effective thermal conductivity [16]

Reduction zone
Gas
Ash
Fig. 1. Zonal description of the gasifier.

2k k C ðln C þC Þ

s f 1
2
1
þ
keff ¼ kg ðln
C 2 þC 1 ÞÀks C 1

ks d2ct
2
dp

ð1Àerad Þ
þ 4rXdp T 3 and X ¼ eb =1 þ 2eebrad
ð1Àeb Þ

Thermal resistance for ith zone
DT
¼ jk ; where Rsi = Rt(i,bed) + Rt(i,ins) + Rt(i,o)
Q_
dif ; jk

Rsi

where jk = up, down, side

(7)

(8)


1389

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

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

using enthalpy of formation of reactants and products. The transfer
of energy between adjacent CVs due to fluid and solid particles motion is accounted for by the mass flow rate, temperature of the fluid
and solid flows and all heat transfer interactions including the radial outward (heat loss) from the bed to surroundings have been
modeled using thermal resistance as shown in Fig. 2. The details
of equations representing the heat transfer module for each CV
are given in Table 2.
The total resistance to radial heat loss to the surroundings in the
ith zone of the gasifier bed is given as the sum of resistances due to
granular bed, insulation and the outer surface of the reactor (cf. Eq.
(8)). In the preheating zone, there is an additional resistance due to
the annular jacket. The axial heat transfer of the porous gasifier bed
has been modeled by considering advection of solid (biomass/char)
and fluid (air/gas) streams, while conductive and radiative heat
fluxes at boundaries of each CV have been modeled in terms of
effective thermal conductivity, Keff, following Sharma et al. [16].
The keff model needs inputs in terms of bed temperature, particle
size and bed porosity at current location. Here, bed porosity varies

with current particle size and modeled using Eq. (2), while emissivity of char particles is fixed at 0.75.
2.3. Thermochemical processes
Modeling of the biomass thermo-chemical conversion phenomena: preheating, drying and pyrolysis, and chemical reactions: oxidation and reduction in a downdraft gasifier has been presented to
predict the rate of heat generation/absorption in each CV and outflow products.
2.3.1. Biomass drying
The mechanism of moisture transfer to woody biomass includes
diffusion through the fluid film around the solid particles and diffusion through the pores to internal adsorption sites. The actual
process of physical adsorption is practically instantaneous, and
equilibrium can be assumed to exist between the surface and the
fluid envelope. As moist biomass particles came into contact with
air having low humidity level, the particles tend to lose moisture
to 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 solid
media is assumed in each control volume, which makes it implicit
that heat transfer between the solid and gases is much faster than
the mass transfer. Thus, mass transfer determines the rate of moisture removal from the biomass particles to the gases/air flowing
around 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 are
listed in Tables 3 and 4.
2.3.2. Pyrolysis of biomass
In downdraft gasifier, the pyrolysis process is modeled at slow

heating rate to predict pyrolytic yields (viz., volatile composition
and char) and devolatilization rate as a function of temperature
and residence time. The biomass particles shrink on pyrolysis giving 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 decomposed into CO, H2, CO2, H2O, tar (heavy hydrocarbons) and light
hydrocarbons (mixture of methane and ethylene).
The whole process of thermal decomposition of dry biomass can
be represented by a single equation as:
kdry

Dry biomass ðDBÞ ! Char
þ Volatiles ðCO; H2 ; CO2 ; H2 O; Methane-Equivalent & TarÞ
ð14Þ

Table 3
Equations representing to moisture evaporation.
Diffusion equation [17]
X in ÀX eqb
X out ÀX eqb

2

¼ p82 ðeÀðp=2Þ

where b ¼

4Ddif t res
d2p

b

2

þ 19 eÀ9ðp=2Þ

; tres ¼

b

(9)

þ . . .Þ

M b;CV
_b
m

Simpson [18] relationship


2 2
Kh
þ K 1 Khþ2K 1 K 2 K h2 2
X eqb ¼ 1800
W
1ÀKh

(10)

1þK 1 Khþ2K 1 K 2 K h

where
W = 349 + 1.29 (T À 273) + 0.0135 (T À 273)2
K = 0.805 + 0.000736 (T À 273) À 0.00000273 (T À 273)2
K1 = 6.27 À 0.00938 (T À 273) À 0.000303 (T À 273)2
K2 = 1.91 + 0.0407 (T À 273) À 0.000293 (T À 273)2

(11)

Relative humidity ratio
air
h ¼ xxair;sat
¼ p mwxwair=p mwair

(12)

Antoine equation [19]
 
B
log10 ðpv ;sat Þ ¼ A À TþC

(13)

v ;sat

a

Table 4
Coefficients for Antoine equation for saturation vapour pressure [19].
Temperature range (K)

A

B

C

255.8–373
379–573

4.6543
3.55959

1435.264
643.748

À64.848
À198.043


1390

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

On heating, these constituents become unstable and decompose
into char and volatiles. Furthermore, the volatiles break-up into

various lighter hydrocarbons. For describing the volatile composition and char yield during slow pyrolysis of the biomass, the present work follows the approach of Sharma et al. [20], where the
thermal degradation of biomass constituents has been described
by individual decomposition scheme of cellulose, hemicellulose
and lignin. Model uses mass fractions of cellulose (Ycel), hemicellulose (Yhc) and lignin (Ylg) in biomass as input information given in
Table 5. The chemical composition can be obtained from the elemental balance knowing the mass fractions, chemical formulas
and molecular masses of cellulose, hemicellulose and lignin. The
rate of devolatlization of biomass during slow pyrolysis process
can be described by a single pseudo-first order reaction as given
by 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 into
a fixed fraction of char and volatiles as described by Eqs. (16) and
(17) in Table 6. These fractions of char from these three constituents 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 biomass
C6HHBOOB can be represented as:

C6 HHB OOB ¼ C1 Hchar Ochar þ n_ v 1 CO þ n_ v 2 CO2 þ n_ v 3 H2
þ n_ v 4 H2 O þ n_ v 5 C1:16 H4 þ n_ v 6 C6 H6:2 O0:2

ð23Þ

2.3.3. Oxidation chemistry in gasifier bed
The pyrolysis products get oxidized in short supply of oxygen in
the oxidation zone (near air tuyers) of a gasifier. Owing to the
widely varying reaction equilibrium constants and the reaction
time scales, some of the reactions might not be attaining equilibrium in the oxidation zone, and hence the solution of full equilibrium equations to compute oxidation process in the gasifier would
both be erroneous and numerically difficult. In the present work,
therefore, a heuristic approach is adopted. Oxidation of the pyrolysis products is allowed to consume the available oxygen in a sequence of descending order of reaction rates as described below:

Table 5
Proportion 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 6
Equations representing to pyrolysis model.
Rate of devolatilization [22]
dMv ol
dt

¼ Àkpyr M v ol ¼ À7:0  107 ðsÀ1 Þ expðÀ1560=TÞM DB Y v ol





dM v ol
¼ ðDt res Þi dmdtv ol
dt

_ v ol;i ¼
Dm

i

(15)

(16)
(17)

Empirical mass ratios [20]
(18)

À1:8447896þ7730:317þ5019898
2

T
Y CO=CO2 ¼ e
Y H2 O=CO2 = 1
Y ME=CO2 = 5 Â 10À16T5.06

T

(19)
(20)

Heat of pyrolysis [20]
 
 
o
o
o
Dhpyr ¼ hf
À Y char hf
DB

char

À Y v ol

Pk¼6

k¼1 Y k

 
o
hf

(21)
k

Biomass
constituents

Cellulose

Hemicellulose

Lignin

Reference

Fractional
char
yield
Chemical
formula

0.05

0.10

0.55

Tillman
et al. [21]

C6H10O5

C6H10O5

C9H7.95O2.4(OCH3)0.92

Grobski
et al. [23]

1. Oxidation of hydrogen (Reaction (R1) in Table 8) completes
itself first.
2. If oxygen remains, light hydrocarbons are oxidized to H2O and
CO (R3).
3. Oxidation is fast, and is assumed to happen instantaneously
whenever oxygen is available.
4. Products of oxygen are assumed to attain equilibrium in each
CV.
5. If more oxygen remains, tar (R4) and char (R5) share the oxygen
in the proportion of their reaction rate constants at the temperature of the CV under consideration to get oxidized to CO.
The principal chemical reactions taking place in the oxidation
zone along with their rate expressions are listed in Table 8.
Although these expressions are not used in the present computations, they have been used only to guide the sequence of oxidation
reactions described above.
If n_ V k stands for the molar flow rate (mol/s) of species k, then
after completely consuming all the H2 in the gaseous phase (Reaction (R1) in Table 8), the O2 that would remain n_ V O2;1 ¼ n_ V O2 –n_ V H2 =2.
If oxygen remains ðn_ V O2;1 > 0Þ, light hydrocarbon or methane-equivalent gets oxidized to CO and H2O, therefore, n_ V O2;2 ¼
n_ V O2;1 À 1:58n_ V CO . If more oxygen remains ðn_ V O2;2 > 0Þ, simultaneous
consumption of tar (Reaction (R4)) and char (Reaction (R5) in Table 8) start taking place. The relative proportions of O2 consumed
by these reactions has been accounted for by considering the ratio
of the two reaction rates r* = kchar/ktar, where the reaction rates are
obtained from Table 8. Two cases can be discussed: one, when there
is enough oxygen to oxidize all the tar and a proportionate quantity
of char; and second, there is less oxygen than what is required to
oxidize tar completely. Oxygen remains after tar oxidation if
n_ V O2;2 > ð1 þ r à Þð4:45n_ V tar Þ. Here, 4:45n_ V tar mol/s of O2 is used up to
oxidize tar and the remainder for char: thus, for every mole of char
oxidized, r* moles of char are also oxidized (cf Reaction (R5)). In case
n_ V O2;2 < ð1 þ r à Þð4:45n_ V tar Þ, all oxygen is consumed. In this case, the
molar rate of tar oxidation is n_ V O2;2 =½4:45ð1 þ rà ފ, and the tar that

exits the zone is thus n_ V tar À n_ V O2;2 =½4:45ð1 þ rà ފ. Correspondingly,
rate of char oxidation is ½n_ V O2;2 rà =4:45ð1 þ rà ފ mol=s. This gives the
moles of char oxidized in the current CV. If oxygen remains all of
it is then used to oxidize CO in a likewise fashion.
Turns [27] quoted that for fuel-rich combustion, the water
shift equilibrium equation can be safely applied, therefore we
can write

n_ V CO2 n_ V H2 =n_ V CO :n_ V H2O ¼ KpðT i Þ ¼ expðÀDG0 ðT i Þ=Ru T i Þ

i

Char yield [20]
Ychar,ash-free = YclYchar + Yhcfchar + Ylg cchar
Yvol = 1 À Ychar,ash-free

Table 7
Fractional char yields from biomass constituents.

ð24Þ

where DG0 ðT i Þ ¼ g 0CO ðT i Þ þ g 0H2 O ðT i Þ À g 0CO2 ðT i Þ À g 0H2 ðT i Þ
Here, DG0(Ti) is the standard-state Gibbs function changes at
atmospheric pressure. The Gibbs function g0 for each species can
be calculated using Eq. (42).
2.3.4. Modeling reduction chemistry in gasifier bed
Reduction of the oxidation zone products are primarily dominated by heterogeneous reactions of solid–char (R6)–(R8) and
homogeneous reactions of gas–gas (R9) in complete absence of

1391

A.Kr. Sharma / Energy Conversion and Management 52 (2011) 1386–1396
Table 8
Chemical reactions in oxidation zone.
Reac. no.
R1
R2
R3
R4
R5
R6
a
b

Oxidation reactions
H2 + 0.5O2 ? H2O
CO + 0.5O2 ? CO2
C1.16H4+1.58O2 ? 1.16CO +2H2O
b
C6H6:2o0:2 +4. 45O2 ? 6CO + 3.1H2O
a

C + 1/2O2 ? CO
CO + H2O M CO2+H2

Rate expressions
1.5

Aj

1.5

kH2 = ACOT exp(ÀECO/RuT)[C CO2 ][C H2 ]
kCO = ACOexp(ÀECO/RuT)[CCO][C O2 ]0.25[C H2 O ]0.5
kME = ACH4 exp(ÀECH4 /RuT)[C O2 ]0.8[C CH4 ]0.7
1
0.5
ktar ffi kHC = AtarTP 0:3
A exp(ÀEtar/RuT)[C O2 ] [CHC]
kchar = Achar exp(ÀEchar/RuT) [CO2]
–

Ej/Ru

Ref.

1.63 Â 10
1.3 Â 108
1.585 Â 109
2.07 Â 104

3420
15,106
24,157
41,646

[25]
[25]
[25]
[26]

0.554
–

10,824
–

[25]
–

9

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

Table 9
Reduction reactions, their reaction rates and constants.
Reac. no.

Reaction

R6

C + CO2 M 2 CO

R7

C + H2O M CO + H2

R8

C + 2H2 M CH4

R9

CH4+H2O M CO+3H2

Rate expression
 

1
CO
r 1 ¼ A1 exp ÀE
P CO2 À KPeq;1
Ru T
 

PCO P H
2
r 2 ¼ A2 exp ÀE
P H2 O À K eq;2 2
Ru T
 

PCH
3
r 3 ¼ A3 exp ÀE
P 2H2 À K eq;34
Ru T


 
P CO P3H
4
r 4 ¼ A4 exp ÀE
P CH4 P H2 O À K eq;4 2
Ru T

oxidants. These reduction reactions are inherently slower than the
oxidation reactions by several orders of magnitude, thus, equilibrium may not be established in the reduction region. At moderately
high temperatures (<800 °C), the equilibrium products may deviate
from reality, thus, kinetic or non-equilibrium models are more
suitable and accurate[28]. In the present work, therefore, a steady
state kinetic model for reduction reactions has been employed following [4,6]. Kinetic model predicts the un-reacted char and final
gas composition. For modeling of reduction chemistry in reduction
zone, following assumptions were made:
1 Reduction reactions are slow reactions, and are treated using
the kinetics of these reactions.
2 All char is consumed by the end of reduction zone
3 The average diameter of the ash particle is 5 mm.

Aj [8]

77.39

7

121.62

1.517 Â 10
4.189

n_ k;i ¼ n_ k;iÀ1 þ V CV;i Rt k;i

ð25Þ

19.21
1

7.301 Â 10

36.15

3. Solution procedure
For fluid flow module, assuming suitable guess of biomass consumption rate, the airflow rate can be calculated using global mass
balance of produced gas, total air, wet biomass and ash. For a given
input of gas flow rate at gasifier exit and airflow rate, Eq. (3) for the
pressure drop through the tuyers and Eq. (4) for pressure drop in
gasifier bed are related in terms of air/gas flow rates through each
CV. Fluid flow rates through these CVs are also related to consumption of solid substrate (e.g. dry biomass, moisture in biomass, char
and ash) by the intrinsic mass balance for each CV. Thus, the sum of
pressure drops across the preheating, drying, and pyrolysis zones
in terms of fluid flow rate through them can be related to pressure
drop across the tuyers as:

DPpreheat þ DPdry þ DP pyro ¼ DP tuy
The reaction rates of global reduction reactions (R6)–(R9) can
be described by the departure of the reactant concentrations from
their equilibrium values and their values of pre-exponential factors
Aj and activation energies Ej for reactions j = 1 . . . 4 are given by
Wang and Kinoshita [7]. CRF is the char reactivity factor, which represents the reactivity of char (or number of active sites on the char

surface) and is a key parameter in simulation of fixed bed gasification. As char burn-off proceeds, the char size decreases and char
porosity increases, the gas would encounter more active sites.
The higher CRF, the process becomes more fast. Giltrap et al. [8] recommended a constant value of 1000 for the char reactivity factor
(CRF). In the present work, the same value of char reactivity factor
has been included in order to account for the active sites present
on char surface (cf. Table 9). The symbol Pk is the partial pressure
of gaseous species k of the reduction zone. Keq,j is the equilibrium
constant for reaction j.
The net rate of production of the kth species (Rtk) thus can be
evaluated in terms of the above reaction rates: for instance,
RtCO = 2r1 + r2 + r4; RtH2 = r2 À 2r3 + 3r4, etc. These Rtk values of
kth species can be used to compute outflow species concentration
for known inflow concentration of each species and volume of each
CV (VCV) as:

Ej (kJ/mol)[7]

3.616 Â 104

ð26Þ

Above Eq. (26) in conjunction with Eqs. (3) and (4), gives ratio of air
coming from the open top and through the tuyers. This ratio influences the reaction temperature profile in the bed and thus the
chemistry of gasification. In the second stage, which corresponds
to heat transfer module, here the energy Eq. (6) in conjunction with
Eqs. (7)–(8), was solved for temperatures in each CVs simultaneously using tri diagonal matrix algorithm (TDMA) with known
values of heat generation/absorption in different zones. When temperature specifications in each CV are known, the actual mass conversion and heat released or absorbed in each CV has been obtained
using thermochemical phenomena sub-models.
For preheat and drying zone, equilibrium mass fraction of moisture in wood, Xeqb, in each CV is computed using vapour–liquid
equilibrium relationship, while the knowledge of residence time

and diffusivity gives Xout, the moisture mass fraction of the biomass
leaving the CV is calculated using mass transfer one-dimensional
diffusion Eq. (9) in conjunction with Eqs. (10) and (13), the quantity of moisture evaporated from the wood particles and heat of
vapourization can be quantified. The pyrolysis products including
char and volatile components are obtained using elemental balances for C, H and O and empirical mass ratios as a function of temperature as written by Eqs. (18) and (20) in Table 6. Once outlet
products is known this gives heat of pyrolysis, which serves input
to heat transfer module.


1392

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

Table 10
Property data.
Thermal conductivity
kb = Sgb(0.1941 + 0.4064Yw) + .1864 + .002 (T À TA)
kchar = 1.4 Â 10À6T2 À 6.4 Â 10À4T + 0.211
kk = Ak + BkT + CkT2 + DkT3
Pk
vk kk ðmwk Þ0:333
kmixture ¼ Pk¼1
k
0:333
k¼1

[26]
[29]
[30]
[31]

(27)

[26]
[26]
[26]
[24]

(31)
(32)
(33)
(34)
(35)

(29)
(30)

vk ðmwk Þ

Specific heat
CpDB = 0.1031 + 0.003867T
Cpb = [CpDB + 4.19Yw]/(1 + Yw) + (0.02355T À 1.32Yw À 6.191)Yw
Cpchar = 1.39 + 0.00036T
Cpk = ak + bkT + ckT2 + dkT3 + ekT4
P
Cpmixture ¼ kk¼1 Y k Cpk
Viscosity [30], [32]

lk(T) = lk(Ta)(T/Ta)n

(36)
(37)

lH2 O = 7 Â 10À12T2 + 5.1 Â 10À8T À 6.04 Â 10À6
lTar % lBenzene = À1.3404 Â 10À11T2 + 3.5844 Â 10À8T À 2.2588 Â 10À6
P
lmixture ðTÞ ¼ kk¼1 PIvk lk
I¼1

where ukI ¼
Enthalpy
0

hf ;mixture ¼

(38)
(39)

vk ukI

ð1þðlk =lI Þ0:5 ðmwI =mwk Þ0:25 Þ2
2:828ð1þðmwk =mwI ÞÞ0:5

P

(40)

0
k Y k hf ;k

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

(41)

Gibbs function [6]
(42)

g 0k ðTÞ ¼ Ag k þ Bg k T þ Cg k T 2 þ Dg k T 3 þ Eg k T 4 þ Fg k =T þ Gg k lnðTÞ

4. Model predictions and validation
A 20 kWe open top downdraft biomass gasifier developed in Indian Institute of Technology, Bangalore has been chosen. The
experimental data of Sharma [11], generated on the same configuration has been used in the present work for validation or testing of
various 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 validated against the experimental data or tested for qualitative
trends. The predictions of fluid flow module for pressure drop in
cold flow have been validated against the experimental data of
Sharma [11] for given particle size distribution and flow rate at
the gasifier exit. Since the pressure drop is a strong function of particle size, the two sets of experimental data has been used in the
present work; one set for freshly charged gasifier with nearly uniform sized particles, while second set for extinguished gasifier (bed
with decreasing particle size downwards in the direction of gas
flow). Simulations are performed: (i) for uniform distribution of
particle diameter (ii) for spatially varying particle size distribution,
as given by Eqs. (1) and (2). Results from the simulations are compared with those from the experiments in Figs. 3 and 4 for an initial
particle size in the range between 34 and 42 mm. The predictions,
for same range of particle sizes are in reasonable agreement with
measured values of pressure drop for the case of extinguished gasifier, while for freshly charged gasifier, the predictions deviate

Gasifier Pressure drop (mmwc)

For oxidation zone, using temperature specifications from heat
transfer module, the value of Kp determined in terms of standard
state of Gibbs function change for water gas shift reaction. Using
Kp value in Eq. (24) and the atomic balances, the final composition
of gases leaving the oxidation zone can be determined. The heat released in the oxidation zone has been computed from the enthalpy
of formation of the reactants and products. Finally, the char consumption and gas composition through the reduction zone can
be obtained solving kinetic rate Eqs. (R6)–(R9) for known reaction
temperature profile. In reduction zone each CV has been subdivided into 100 subdivisions to ensure adequate accuracy of elemental balances.
The equilibrium constants Keq,j for jth reaction are evaluated at
the temperature of the CV from standard state Gibbs functions of
the gaseous species k, g ok from Eq. (42). The polynomial fits for standard state enthalpy and entropy used to compute the Gibbs functions as a function of temperature are obtained from NASA fits
on JANAF Tables data [27]. Similarly, heat absorption in reduction
zone has been obtained using heats of formation of the reactants
and products. The thermo-physical properties of working substances in terms of temperature are listed in Table 10, the values
of constants used in Table 10 are obtained from their respective
references. The consumption of char in reduction zone depends
mainly on feedstock composition and equivalence ratio of the gasifier, the temperature of reduction zone. The equivalence ratio of
the gasifier was controlled by the airflow rate. The ratio of air to
biomass was adjusted so that the char flow rate at gasifier exit becomes zero.

10
9
8
7
6
5
4
3

2
1
0

Experimental data
db=34mm
db=42mm

18

20

22

24

26

28

30

Air flow rate (g/s)
Fig. 3. Comparison with experimental data(freshly charged gasifier) for uniformly
distribution of particle size, Tbed = 300 K, cold flow.


1393

Gasifier Pressure drop (mmwc)

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

25
Experiments

20

db=34mm
db=42mm

15
10
5
0
10

15

20

25

30

Air flow rate (g/s)
Fig. 4. Comparison with experimental data (extinguished gasifier) for spatially
varying of particle size distribution of, Tbed = 300 K, cold flow.

slightly at higher flow rates. This may be due to the fact that the

particles are not perfectly spherical and due to uncertainty associated with particles (size) constituting the freshly charged bed.
The heat transfer module uses the heat released/absorbed in
each zone as the input to predict the temperatures in each zone.
Since the heat released/absorbed in an actual gasifier is closely
coupled with all other parameters, it was not possible to validate
the heat transfer part in isolation against experiments. Therefore,
well tested model (tested for qualitative trends) of Sharma for heat
transfer [16], has been followed in the present work. The drying
model is tested in the preheat zone (of length 1 m) in the gasifier
for the effects of zone temperature and particle diameter for qualitative trends as shown by Figs. 5 and 6. Fig. 5 shows the trends for

Moisture content in biomass

0.12

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

0.1

4.2. Validation of gasifier model

0.08
0.06
0.04
0.02
0
0

20

40

60

80

moisture loss distribution along the testing bed for four isothermal
temperatures i.e., 350, 400, 500 and 600 K. The results show that as
temperature increases, the biomass dries up quickly within the
short length along the testing bed, as expected. In order to study
the effect of particle size on moisture evaporation; five levels of
average particle size i.e. 10, 20, 30, 40 and 50 mm are considered
in this analysis (Fig. 6). Predicted results shows faster biomass drying with decrease in particle diameter, as expected.
A well tested pyrolysis sub-model of Sharma et al. [20] is used
to predict the species concentration in volatile matter and char
yield at known pyrolysis temperature. It uses input of the percentages of three major constituents – cellulose, hemicellulose and lignin in biomass and fraction of char due to the breakup of each of
these three constituents from Tables 5 and 7. For validation of
the oxidation module, the oxidation of volatiles alone has been
considered. The products of oxidation of volatiles predicted by
the present model have been compared with equilibrium code of
Olikara and Borman as given in Ref. [27], which uses input in the
form of CNHMOLNK and equivalence ratio U. Volatiles are considered to have the chemical formula of C1.3H3O1.4. Char oxidation
has been excluded from the validation part since the code of Olikara and Borman is meant only for those reactions which are expected to reach equilibrium. Fig. 7 shows the comparison of CO,
H2 and CO2 contents in the products of oxidation as predicted by
the present model with the predictions of the code of Olikara
and Borman, for an equivalence ratio U = 1.85. The comparison is
found to be quite good. These figures also show the variation in

the content of these species with the reaction temperature. With
increase in temperature, the CO content increases while H2 and
CO2 decrease, as expected. For reduction environment, a well
tested kinetic model for reduction reactions has been used [4,6].

100

Distance along preheating zone (cm)

After the validation and testing of above modules individually,
it is also essential to validate the overall gasifier model after coupling of these modules. The gasifier model predicts the pressure
drops, biomass consumption rate, airflow rates, gas composition
and its calorific value for a given value of producer gas flow rate
and size of the feedstock particles being fed from the top.
For validation, the experimental data of Sharma [11] on the
20 kWe downdraft gasifier has been used at wide range of producer gas flow rate. In his experiments, Sharma used sun dried
Kikar wood (Acacia), chopped in cubic shape with average size
36 mm having average moisture content in the range of 11–13%
on dry basis. Simulations are also performed for the similar operating condition for gasification of hardwood feedstock. However,

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

14
13
dp=1cm

0.12

Composition (%vol)

Moisture content in biomass

0.14
dp=2cm
dp=3cm

0.1

dp=4cm
dp=5cm

0.08
0.06
0.04
0.02

12
11
10
9
8

CO: Present work

CO: ’PER’ model

7

H2: Present work

H2: ’PER’ model

6

CO2: Present work

CO2: ’PER’ model

5
1100

0
0

20

40

60

80

100

1200

1300

1400

1500

Temperature in oxidation zone (K)

Distance along preheating zone (cm)
Fig. 6. Effect of particle size on moisture loss in drying zone, T = 400 K.

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


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

Gasifier Pressure drop (mmwc)

30

Gas composition (%vol)

since in experiments, the particle size even at the gasifier inlet varies considerably, the choice of a constant particle size at gasifier inlet can have a strong bearing on comparison of simulation with the
experimental values. Thus, for comparison with experimental data,
the simulation results have been plotted in particle size range from
36 to 50 mm. The pressure drop across the gasifier predicted by the
model for various producer gas flow rates is compared with the
experimental values in Fig. 8. This deviation could be owing to
the uncertainty in the particle size of the feedstock. It is observed
that the predicted pressure drops are in agreement with the measured data within the experimental uncertainty. Predicted temperature profile in the gasifier bed at gas flow rate of 7 g/s has been
compared with experimental data as shown in Fig. 9. As expected,
the maximum temperature in oxidation zone predicted by the
model is 1217 K at the gas flow rate of 7 g/s. A good agreement

of predicted and measured temperature profile across the bed
can be clearly observed.
In Fig. 10, predicted gas composition has been compared with
experimental data of Sharma. Predictions for CO and H2 percentage
in gas increase gently with gas flow rate. The theoretical trends for
CO and H2 composition are in good agreement with experimental
measurements of Sharma[11]. The calorific value of the gas from
prediction and experiments is compared in Fig. 11, and good agreement is obtained. Since the experimental data is limited to a little
range of gas flow rate, the predictions have been extended to 30 g/
s, in order to demonstrate the predictable capability of the above
model at higher flow rates. Model predicts a very small percentage
of CH4 below 0.4%, water vapour varies from 10% to 12% and tar

Predictions(H2)

Experiments (CO)

Predictions (CO)

25
20
15
10

4

9

14

19

24

29

Gas flow rate (g/s)
Fig. 10. Comparison of predicted CO and H2 composition in producer gas with
experiments.

5000
4500
4000
3500
3000
Experiments

2500

Predictions

2000
1500
1000

35

4

9

14

dp=36mm

30

19

24

29

Gas flow rate (g/s)

dp=50mm

25

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

Experiments

20

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

15
10

4.3. Gas flow rate
5
0
0

2

4

6

8

10

12

14

16

Producer gas flow rate (g/s)
Fig. 8. Comparison of the predicted pressure drop with experimental data, spatially
varying particle size, hot flow.

Some trends of pressure drop, temperature profile, dry gas composition and calorific value against gas flow rate have been discussed in previous section (cf. Figs. 8–11). In this section, the
trends of temperature profiles across gasifier bed for different values of gas flow rates; cold gasification efficiency and gasifier power
output for wide range of gas flow rate are studied as shown in

Experiments

1450

Predictions

1250

Temperature (K)

1250

Temperature (K)

Experiments (H2)

5

Calorific value of gas (kJ/m3)

1394

1050
850
650
450

mpg=6g/s
mpg=9g/s
mpg=12g/s

mpg=17g/s
mpg=21g/s

1050
850
650
450

250
0

50

100

150

200

Distance from open top (cm)

250
0

50

100

150

200

Distance from open top (cm)
Fig. 9. Comparison of predicted temperature profile with experimental data,
db = 36 mm, mpg = 7.0 g/s, Hardwood.

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


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

Gasification efficiency

90
80

77

70

75

60
73

50
40

71

30

69
Conversion efficiency

67

Gasifier power output

20
10

Gasification power output (kW)

100
79

0

65
5

10

15

20

Gas flow rate (g/s)
Fig. 13. Effect of producer gas flow rate on gasification efficiency and gasifier power

output (kW).

Figs. 12 and 13. The variations in temperature profiles for five different gas flow rates viz., 6, 9, 12, 17 and 21 g/s have been compared in Fig. 12. As expected, the maximum temperatures
(predicted) can be observed in oxidation zone. The overall temperature profiles at increasing gas flow rates are found to be improving. A maximum temperature is found to be increasing from
1141 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 terms
of the ratio of net heating value of gas at ambient (neglecting the
sensible heat) to the input energy intake by biomass feedstock.
The heating values of biomass and product gas at the gasifier exit
can be obtained from literature [26,27,31] in terms of heating values of individual components. With these heating values, the gasification efficiency (cold basis) and gasifier power output can be
computed and results of cold gasification efficiency and gasifier
power output (kW) are plotted in Fig. 13. The cold gasification efficiency is observed to be increasing from 72% to 74% with gas flow
rate variation from 6 to 25 g/s. A steep increase in gasifier power
output (21–92 kW) can be observed (almost linear trend) for above
gas flow rate variation.
Increase in gas flow rate improves the temperature profile leading transformation of the non-combustibles components (i.e. CO2,
H2O) into combustibles (i.e. CO, H2) and thus improving the calorific value of the product gas, the cold gasification efficiency and
gasifier power output as well. However, the temperatures in drying
and pyrolysis zone are lower at higher flow rates, and thus the
pressure drop in these regions may be less at higher flow rate.
But in reduction zone, where maximum char conversion takes
place, the particle sizes are the smallest, has higher temperature
at higher gas flow rates. This would add significantly to the pressure drop. The predicted trends agree with this expected
behaviour.
5. Conclusions
A mathematical model EQB for a downdraft biomass gasifier has
been developed to predict the pressure drop, airflow rate from
open top and through the tuyers, biomass consumption, temperature profile and gas composition for given gas flow rate. Model was
developed in three stages: first stage, fluid flow module is carried
out, where isothermal flow of air was considered through the gasifier bed; second stage corresponds to heat transfer module, here

energy equation was solved to obtain the temperatures in each
CV with heat generation/absorption in different zones considered
as known; third stage, the physical and chemical phenomena take

1395

place due to biomass drying, pyrolysis, oxidation and reduction
reaction sub-process, and their energetics decide the heat generation or absorption in each CVs. The subroutines constituting the
gasifier model have been validated or tested. The fluid flow module
has been validated in cold flow for constant particle size (freshly
charged gasifier) as well as for variable (decreasing) particle size
distribution in gasifier bed (due to thermochemical conversion).
Mass transfer model for biomass drying have been tested in preheating zone and found working well for right trends of response
to particle size, rate of drying and prevailing temperature. Equilibrium based oxidation model is validated with the equilibrium code
of Olikara and Borman and found to be robust and adequate for
prediction of product composition, but predicts a steep temperature rise within a single control volume where oxidation completes
itself. Finally, the gasifier model was validated against the experimental data with good agreement.
For the range of gas flow rate encountered in this work, any
improvement in the reaction temperature leads to better thermochemical transformation of biomass material into combustibles
(i.e., CO, H2), thus, improving the gasifier performance in terms
of energy efficiency and power output. The rise in gasifier temperature due to chemical reactions specially at high gas flow rate also
strongly influences the gasifier pressure drop. Furthermore, reduction zone is recognized as the most sensitive region for remarkably
high pressure drop, where highest char conversion leads to smallest particle sizes and high reaction temperatures as well specially
at higher gas flow rate.
Chemical equilibrium for oxidation zone (where reaction temperatures proceeds beyond 800 °C establishing equilibrium) and
empirically predicted pyrolysis products (volatiles and char) allowing faster convergence, while implementing kinetic modeling for
reduction zone is helpful in restoring the accuracy of predictions
(where reaction temperatures less than 800 °C and thus equilibrium is far away from reality). This combination constitutes an efficient algorithm allowing rapid convergence with adequate fidelity.
When, objective is to couple a gasifier model with a gas engine
model for predicting the performance of a gasifier–engine system

model, the above algorithm of gasifier simulation may be a preferable choice.
Acknowledgements
Author is grateful to Prof. M.R. Ravi and Prof. S. Kohli, Indian
Institute of Technology, Delhi for their valuable contribution in carrying out of mathematical modeling and computational work.
References
[1] Baratieri M, Baggio P, Fiori L, Grigiante M. Biomass as an energy source:
thermodynamic constraints on the performance of the conversion process.
Bioresour Technol 2008;99:7063–73.
[2] Zainal ZA, Ali R, Lean CH, Seetharamu KN. Prediction of performance of a
downdraft gasifier using equilibrium modeling for different biomass materials.
Energy Convers Manage 2001;42:1499–515.
[3] Malgar A, Pérez JF, Laget H, Horillo A. Thermochemical equilibrium modelling
of a gasifying process. Energy Convers Manage 2007;48(1):59–67.
[4] Sharma AK. Equilibrium & kinetic modeling of reduction reactions in the char
bed of a downdraft (biomass) gasifier: a comparison. Int J Solar Energy
2008;82:918–28.
[5] Ratnadhariya JK, Channiwala SA. Three zone equilibrium and kinetic free
modeling of biomass gasifier – a novel approach. Renew Energy
2009;34:1050–8.
[6] Sharma AK. Simulation of a biomass gasifier–engine system. PhD thesis, Indian
Institute of Technology, Delhi, India; 2006.
[7] Wang Y, Kinoshita CM. Kinetic model of biomass gasification. Solar Energy
1993;51(1):19–25.
[8] Giltrap DL, McKibbin R, Barnes GRG. A steady state model of gas–char reactions
in a downdraft biomass gasifier. Solar Energy 2003;74:85–91.
[9] Babu BV, Sheth PN. Modeling and simulation of reduction zone of downdraft
biomass gasifier: effect of char reactivity factor. Energy Convers Manage
2006;47(15-16):2602–11.

1396

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

[10] Gao N, Li A. Modelling and simulation of combined pyrolysis and reduction
zone for a downdraft biomass gasifier. Energy Convers Manage
2008;49:3483–90.
[11] Sharma AK. Experimental investigation on a 20 kWe, solid biomass gasification
system. Biomass Bioenerg, in press, doi:10.1016/j.biombioe.2010.08.060.
[12] Chen J, Gunkel WW. Modelling and simulation of co-current moving bed
gasification reactors – part II: a detailed gasifier model. Biomass
1987;14:75–98.
[13] White FM. Viscous fluid flow. 3rd ed. Singapore: McGraw Hill; 1991.
[14] Kaviany M. Principles of heat transfer in porous media. 2nd ed. New
York: Springer-Verlag; 1995.
[15] White FM. Fluid mechanics. 5th ed. Singapore: McGraw Hill; 2003.
[16] Sharma AK, Ravi MR, Kohli S. Modelling heat transfer in biomass gasifier
(paper code HMT-2006-C184). In: Proceedings of 18th national & 7th ISHMT —
ASME. Heat transfer conference, 315 (1315), IIT, Guawahati, India; 2006.
[17] McCabe WL, Smith JC, Harriott P. Unit operations of chemical engineering. fifth
ed. New York: MGH; 1993.
[18] Simpson WT. Equilibrium moisture content of wood in outdoor locations in
the United States and worldwide. Research note FPL-RN-0268. Forest Products
Laboratory, United States Department of Agriculture; 1998.
[19] NIST, NIST Chemistry; 2005. <>.
[20] Sharma AK, Ravi MR, Kohli S. Modelling product composition in slow pyrolysis
of wood. SESI J 2006;16(1):1–11.
[21] Tillman DA, Rossi AM, Kitto WA. Wood combustion. Academic Press Inc.;
1981.
[22] Robert AF. A review of kinetics data for the pyrolysis of wood and related

substances. Combust Flame 1970;14:263–72.

[23] Grobski M, Bain R. Properties of biomass relevant to gasification. In: Reed, TB,
editor .Biomass gasification: principles & technology: energy technology, Solar
Energy Research Inst. (SERI); 1981. p. 41–70.
[24] Thunman H, Niklasson F, Johnson F, Leckner B. Composition of volatile gases
and thermochemical properties of wood for modeling of fixed or fluidized
beds. Energy Fuel 2001;15:1488–97.
[25] Ducrane ED, Dolignier JC, Marty E, Martin G, Delfosse L. Modelling of gaseous
pollutants emissions in circulating fluidized bed combustion of municipal
refuse. Fuel 1998;77(13):1399–410.
[26] Ragland KW, Arts DJ, Baker AJ. Properties of wood for combustion analysis.
Bioresour Technol 1901;37:161–8.
[27] Turns SR. Introduction to combustion. 2nd ed. McGraw Hill; 2000.
[28] Altafini CR, Wander PR, Barreto RM. Prediction of the working parameters of a
wood waste gasifier through an equilibrium model. Energy Convers Manage
2003;44:2763–77.
[29] Petersen SB, Pedersen LTh, Henriksen U. Theoretical and experimental
investigation on heat transfer in fixed char beds. In: Bridgewater AV,
Boocock DGB, editors. Developments in thermochemical biomass conversion,
vol. 1. Blackie Academic and Professional; 1997.
[30] Reid RC, Prausnitz JM, Poling BE. The properties of gases and liquids. Fourth
ed. New York: McGraw-Hill Company; 1988.
[31] Perry RH, Green DW. Perry’s chemical engineers handbook. Seventh ed. New
York: MGH; 1998.
[32] Vargaftik NB. Handbook of physical properties of liquids and
gases. NY: Hemisphere; 1975.
[33] Dogru M, Howarth CR, Akay G, Keskinler B, Malik AA. Gasification of hazelnut
shells in a downdraft gasifier. Energy 2002;27:415–27.