A comprehensive two dimensional Computational Fluid Dynamics model for an updraft biomass gasifier

Renewable Energy 99 (2016) 698e710

Contents lists available at ScienceDirect

Renewable Energy
journal homepage: www.elsevier.com/locate/renene

A comprehensive two dimensional Computational Fluid Dynamics
model for an updraft biomass gasifier
Niranjan Fernando*, Mahinsasa Narayana
Department of Chemical and Process Engineering, University of Moratuwa, Sri Lanka

a r t i c l e i n f o

a b s t r a c t

Article history:
Received 27 June 2015
Received in revised form
19 July 2016
Accepted 21 July 2016

This study focuses on developing a dynamic two dimensional Computational Fluid Dynamics (CFD)
model of a moving bed updraft biomass gasifier. The model uses inlet air at room temperature as the
gasifying medium and a fixed batch of biomass. The biomass batch is initially ignited by a heat source
which is removed after a certain amount of time. This model operates by the heat emitted by combustion
reactions, until the fuel is finished. Since the operation is batch wise, model is transient and takes into
consideration the effect of bed movement as a result of shrinkage. The CFD model is capable of simulating the movement of interface between solid packed bed and gas free board and this motion is also
presented. The model is validated by comparing the simulation results with experimental data obtained
from a laboratory scale updraft gasifier operated in batch mode with Gliricidia. The developed model is
used to find the optimum air flow rate that maximizes the cumulative CO production. It is found that

from the simulation study for the particular experimental gasifier, a flow rate of 7 m3/h maximizes the
CO production. The maximum cumulative CO production was 6.4 m3 for a 28 kg batch of Gliricidia.
© 2016 Elsevier Ltd. All rights reserved.

Keywords:
Gasification
Mathematical model
Computational Fluid Dynamics
Moving bed

1. Introduction
With the depletion of fossil fuels, alternative, renewable energy
sources are promoted as possible ways of providing the world's
energy demand. In this respect, biomass is a promising energy
source to produce green energy. It is expected that biomass will
provide half of the present world's main energy consumption in
future [1] [2]. However, the direct combustion of biomass has
several drawbacks to produce thermal energy. These drawbacks
include; low heating value of biomass, unsuitability as a fuel for
high temperature applications, cannot be used directly as a fuel for
internal combustion engines and low versatility. Therefore, corresponding to industrial requirements, biomass is usually converted
into a more versatile secondary fuel by thermo-chemical, bio
echemical or extraction processes [3] [4]. Gasification is a major
thermo-chemical process, which is being used worldwide to
convert biomass into a versatile, energy efficient fuel gas called
Syngas. This gas is a mixture of carbon monoxide, carbon dioxide,
hydrogen, methane, small amount of light hydrocarbons and nitrogen [5]. The gas produced is more versatile than original raw

* Corresponding author.
E-mail addresses: (N. Fernando), mahinsasa@

uom.lk (M. Narayana).
/>0960-1481/© 2016 Elsevier Ltd. All rights reserved.

biomass fuel and can be used for a variety of applications. Examples
are electricity generation, heat generation and hydrogen production [5]. It can also be used as a raw material to produce liquid
biofuels [6].
Gasification of biomass is carried out in a special reactor called a
gasifier. Number of other factors related to gasifier design and fuel
properties significantly affect the produced gas quality. These
include; gasifying medium, properties of biomass, moisture content, particle size, temperature of the gasification zone, operating
pressure and equivalence ratio [5] [7].
The optimization of gasifiers based on these design factors can
be done in two main ways; through experimental approach and
through computer aided simulations. Experimental approach follows a series of experiments, usually on scaled down laboratory
scale gasifiers. Parameters such as the optimum equivalence ratio
can be determined by measuring the gas quality under various
equivalence ratios until the best results are obtained. However,
experimental approach leads in to a series of difficulties and
drawbacks. It is very difficult to perform experimental analysis on
pilot scales systems, especially when considering geometry optimization, therefore scaled down models have to be used for
experimental analysis. The results obtained on scaled down systems may not fully work on the pilot scale system. The scale down
systems cannot be used to determine the effects of biomass particle


N. Fernando, M. Narayana / Renewable Energy 99 (2016) 698e710

Nomenclature
A
Ac
Ad

Ag
Aj
Ar
a
ap
Cg
Cs
Di;g
d
Ei
fi
G
h
k
kg
ks
km;j
Mi
mi
Nu
n
Pr
p
pin
Qrad
Qi
Qsg
qr
Re
Rg;pyro

Specific surface area of packed bed (mÀ1)
Specific surface area of char (mÀ1)
Specific surface area for gas diffusion (mÀ1)
Cross sectional area of gasifier (m2)
pre-exponential factor for heterogeneous reactions (m
sÀ1 TÀ1)
Specific surface area available for radiation (mÀ1)
Absorption coefficient of gas phase (mÀ1)
Absorption coefficient of solid phase (mÀ1)
Heat capacity of gas phase (J kgÀ1KÀ1)
Heat capacity of solid phase (J kgÀ1KÀ1)
Diffusion coefficient of gas species i (m2 sÀ1)
Particle size of biomass (m)
Activation energy of reaction i (J molÀ1)
Pre-exponential factor of reaction i (sÀ1)
Radiation intensity (W mÀ2)
Heat transfer coefficient (W mÀ2 KÀ1)
Turbulent kinetic energy (m2 sÀ2)
Thermal conductivity of gas phase (W mÀ1 KÀ1)
Thermal conductivity of solid phase (W mÀ1 KÀ1)
Mass transfer coefficient of species j (m sÀ1)
Molecular weight of species i (kg molÀ1)
Specific mass of species i in a computational cell (kg
mÀ3)
Nusselt number
Refractive index of gas phase
Prandtl number
Pressure (Pa)
Inlet pressure (Pa)

Radiation heat source (W mÀ3)
Initial heat source (W mÀ3)
Convective heat transfer rate (W mÀ3)
Radiation heat flux (W mÀ2)
Reynolds number
Rate of release of pyrolytic volatiles (Kg mÀ3 sÀ1)

sizes, as particle size relies on the diameter of the real system.
Scaling down the particle size will not produce equivalent results
because the packing factors will differ between the two systems.
Also, taking measurements inside packed beds is a difficult task
considering the higher temperatures present in an operational
gasifier. Because of these reasons, the experimental approach is
usually difficult, time consuming, costly and the accuracy of the
results are also low.
Therefore many researchers use the computer based approach
to analyze packed bed processes. A large number of research works
are available in literature where numerical models are used to
optimize packed bed processes [2] [8] [9] [10]. Mathematical
models offer certain advantages over the conventional experimental procedure. Mathematical models can produce a large
number of data points as compared to fewer experimental data, for
example, when measuring temperature, experimental analysis can
provide temperatures at only a finite number of locations along the
packed bed, while numerical models can provide the complete
variation of the temperature profile over the region of interest.
With the development of the computer hardware technology,
Computational Fluid Dynamics (CFD) is widely applied as a numerical modeling tool [11] [12] [13] [14]. CFD models can be made
to match the exact geometry of the real scale gasifier, as a result no

ri

ri;hetero
ri;homo
rm;i
rk;i
rt;i
Shj
S∅
Ss;∅
Sg;∅
sij
Tg
Tg;in
Ts
Ug
Ug;in
Us
vi
Yi;g
Yi;air
Yi;s

s
sp

ε
∅
εg
εs

rg

rs
rj
m
si;air
Ui;air
ε

DHi
5

699

Rate of reaction i (Kg mÀ3 sÀ1)
Rate of heterogeneous reaction i (Kg mÀ3 sÀ1)
Rate of homogenous reaction i (Kg mÀ3 sÀ1)
Mass transfer limited reaction rate (Kg mÀ3 sÀ1)
Kinetic reaction rate (Kg mÀ3 sÀ1)
Turbulent mixing limited reaction rate (kg mÀ3 sÀ1)
Sherwood number for species j
Source term for property ∅
Source term for property ∅ due to solid phase
Source term for property ∅ due to gas phase
Reynolds stress tensor (Pa)
Gas phase temperature (K)
Inlet gas temperature (K)
Solid phase temperature (K)
Gas phase velocity (m sÀ1)
Inlet gas velocity (m sÀ1)
Shrinkage velocity (m sÀ1)
Stoichiometric coefficient of species i

Mole fraction of gas species i
Mole fraction of i in air
Mole fraction of solid species i
Stefan constant (W mÀ2 KÀ4)
Scattering coefficient of solid particles (mÀ1)
Emissivity of solid particles
A general transport property
Volume fraction of gas phase
Volume fraction of solid phase
Density of gas phase (Kg mÀ3)
Density of solid phase (Kg mÀ3)
Cell density of species j (Kg mÀ3)
Dynamic viscosity (Pa s)
Average collision diameter (A)
Diffusion collision integral
Turbulent dissipation rate (m2 sÀ3)
Enthalpy of reaction i (J kgÀ1)
Vector outer product

scaling down problems arise, in CFD simulations, any number of
input parameters can be easily changed at will, including equivalence ratio, particle size, moisture content, feed properties, superficial velocity etc. and system performance can be obtained
accordingly. CFD simulations are best suited to perform geometry
optimization. A large number of geometrical parameters can be
optimized by simply changing the computational mesh. Because of
these advantages CFD models are now widely used by researchers
around the world as a tool to study and optimize gasification
process.
In the present work a two dimensional dynamic two fluids CFD
model has been developed for an updraft biomass moving bed
gasifier. This model uses inlet air at room temperature as the

gasification medium and a fixed batch of biomass. The biomass
batch is initially ignited by a heat source, which is removed after a
certain amount of time. The mathematical model developed in this
study is capable of maintaining the operation by the own heat
emitted by combustion reactions, until the fuel is finished, as in the
real world scenario. Since the operation is batch wise, model is
transient and takes into consideration the effect of bed movement
as a result of shrinkage. This model is capable to detect the
movement of interface between solid packed bed and gas free
board in time domain. The two phase model is developed by using


700

N. Fernando, M. Narayana / Renewable Energy 99 (2016) 698e710

the Euler-Euler approach. The overall model consists of several sub
models; including reaction models, which govern the reaction rates
and compositions of the products, turbulence model for packed bed
gas phase and free board, a radiation model for solid phase, a bed
shrinkage model, and interphase heat transfer model. In this study,
the ultimate mathematical model for the gasifier is converted into a
numerical model by using open source CFD tool OpenFOAM. CFD
code was developed using Cþþ language and available tools in
OpenFOAM package to include all the relevant differential equations and procedures in the mathematical model. The code is
developed for two dimensional generic analyses, which is capable
to perform two dimensional geometrical optimizations, such as
inclusion of tapered sections in gasifier body. To validate the CFD
model, simulation results are compared against experimental data
from an operational laboratory gasifier. It is found that the model is

in good agreement with experimental data.

considered in the present work. A schematic diagram of the presented mathematical model is shown in Fig. 1.

2.1. Governing equations
Conservation equations for momentum, energy and species are
solved in the gas phase.



v
rg εg U g þ V$ rg εg U g 5U g À V$mεg VU g
vt
!

À
ÁT  2
À rg kI þ S
¼ Àεg Vp þ V$εg VU g þ VU g
3
With I the second order identity tensor.
Gasesolid momentum exchange rate [17];

À

1 À εg
S ¼ 150
d2 ε2g

2. Mathematical model

A two dimensional, transient, two-phase, Euler-Euler model was
developed in the present study. The two phases consist of gas and
solid phases. In the Euler-Euler approach, both solid and gas phases
are treated as continuums, as a result, motion of individual particles
in solid phase are not calculated based on forces acting on them.
The motion of solid continuum is resolved using continuity equation. Heat and mass transfer between two phases due to chemical
reactions are modelled through source terms of governing equations. Radiation heat transfer is modelled in the solid phase. It is
assumed that the optical thickness of gas phase is small and gas
does not absorb radiation energy [15]. Gas phase turbulence is
modelled using standard k À ε model [16]including the effects of
porosity. Motion of the biomass bed and solid-freeboard interface is

(1)



Á2
Ug þ 1:75

À
Á
rg 1 À εg Ug 
dεg

Ug

À
Á
vrg εg Cv;g Tg
þ V$rg εg Cv;g Tg U g À V$ εg kg VTg

vt
À
Á X
À
Á
¼ hA Ts À Tg þ
DHi ri;homo þ Rg;pyro Cv;g Ts À Tg

(2)

(3)

i




À
Á
v
rg εg Yi;g þ V$ rg εg Yi;g U À V$ εg Di;g VYi;g
vt
X
X
¼
ri;homo þ
ri;hetero
i

(4)

i

Energy conservation and species conservation equations are
solved in the solid phase,

Fig. 1. Schematic diagram of the mathematical model.


N. Fernando, M. Narayana / Renewable Energy 99 (2016) 698e710

vrs εs Cs Ts
þ V$rs εs Cs Ts U s À V$ðεs ks VTs Þ
vt
À
Á X
¼ ÀhA Ts À Tg þ
DHi ri;hetero þ Qrad þ Qi

(5)

i

Á
À
Á
À
Á X
vÀ
r εs Y þ V$ rs εs Yi;s U s À V$ εs Di;s VYi;s ¼

ri;hetero
vt s i;s
i

(6)

2.2. Evaluation of source terms
Terms appearing on the right hand side of each governing
equation represent generation terms of transport property
described by the equation. These consist of convective heat transfer
between phases, radiation heat transfer terms, heat generation due
to chemical reactions and species generation due to chemical reactions. These source terms are evaluated by considering correlations of parameters and kinetic models of chemical reactions.
2.2.1. Inter-phase heat transfer
Two main processes are responsible for interphase heat transfer.
These are;
1. Convective transfer of heat between two phases as a result of
temperature difference between gas and solid phases.
2. During pyrolysis stage, hot volatile gases generated within the
porous structure of biomass release into gas phase. These hot
volatile gases introduce an energy flow to the gas phase.

701

2.2.2. Radiation heat transfer
Radiation heat transfer plays a major role in transporting heat
generated in combustion zone from combustion reactions, to top
wood layers of the packed bed. This heat provides the energy for
thermal cracking of biomass and other endothermic solid phase
reactions that take place in top layers. In the present work, P1 radiation model is applied to model radiation in the packed bed with
following assumptions [19] [20].

 Biomass bed can be treated as an absorbing, emitting, scattering
medium of dispersed solid particles.
 Combustion zone can be approximated by a hot emissive plate
located at the bottom of the gasifier.
 The gas phase is optically thin and does not interact with
radiation.
A schematic diagram of the radiation model is shown in Fig. 2.
The governing transport equation of P1 model for incident intensity, G, with a dispersed solid phase is given by equation (12)
[20];

 À

Á
V$ðGVGÞ þ 4 an2 sTg4 þ Ep À a þ ap G ¼ 0

(12)

whereG is given by,

1
Á
3 a þ ap þ sp

G¼ À

(13)

The equivalent emission of particles, Ep , is calculated by;

Ep ¼ εAr Ts4

(14)

Convective heat transfer is modelled by using an overall heat
transfer coefficient. The heat transfer rate is evaluated by;

With the simplifying assumptions of an optically thin gas phase
(a ¼ 0 and n ¼ 0), Eq. (12) can be reduced to;

À
Á
Qsg ¼ hA Ts À Tg

V$ðGVGÞ þ 4Ep À ap G ¼ 0

(7)

The specific surface area A of biomass is calculated by the correlation of the following equation [18].

A¼

6εs
d

(8)

The heat transfer coefficient h is evaluated using definition of
the Nusselt number [14].

kg εg Nu

h¼
d

(9)

The Nusselt number is evaluated using the following relationship [14].

where,

1
Á
3 ap þ sp

G¼ À

(16)

The radiation heat flux in P1 model is given by Eq. (17) [19].

qr ¼ ÀGVG

(17)

The radiation source term in energy equation is given by ÀVqr ,
which is obtained by applying gradient operator to Eq. (17) and
simplifying with the use of Eq. (15).

ÀVqr ¼ ap G À 4Ep



 

Nu ¼ 7 À 10εg þ 5ε2g 1 þ 0:7Re0:2 Pr 0:33 þ 1:33 À 2:4εg

þ 1:2ε2g Re0:7 Pr 0:33

(18)

This term is substituted in the solid phase energy equation as
the source term for thermal radiation.

(10)
During the process of pyrolysis, the solid biomass decomposes
into gas products, which is released into the gas phase. It is
assumed that these gas products are of the temperature of the solid
phase. The release of these higher temperature gases into the gas
phase results in an additional energy transfer term in gas phase
energy equation given by;

À
Á
Hpyro ¼ Rg;pyro Cv;g Ts À Tg ;

(15)

(11)

where, Hpyro is the heat generation rate due to biomass pyrolysis.

Fig. 2. Schematic of Radiation model.

702

N. Fernando, M. Narayana / Renewable Energy 99 (2016) 698e710

2.2.3. Reaction sub models
2.2.3.1. Heterogeneous reactions. Four main heterogeneous reactions are considered in the present work. These are drying, pyrolysis, reduction and combustion. In mathematical modelling of a
gasifier, these processes are included in the mathematical model as
rate terms in governing equations. Because of this, in modelling
view point, the most important parameter of these processes is the
rate of the process. A number of different models are available for
describing the rate of each of these processes. The reaction sub
models used in the present study are described in following
sections.
2.2.3.1.1. Drying. Drying is the process through which moisture
in the biomass transfers into the gas phase. In the present work
drying process is represented by a one-step global reaction in
which moisture in the solid phase transfers to gas phase [21].

ðC2:85 H3:69 O:H2 OÞs /ðC2:85 H3:69 OÞdry;s þ ðH2 OÞ g

(R1)

Here ðC2:85 H3:69 O$H2 OÞs represents the initial moist Gliricidia
biomass species and ðC2:85 H3:69 OÞdry;s represents dry Gliricidia
biomass species.
The drying rate is calculated by an Arrhenius rate equation [22]
[18] [23].

rd ¼ fd exp



ÀEd
εs rs YH2 O;s
RTs

(19)

The values of pre exponential factor, fd , and activation energy,
Ed , for the drying rate are obtained from Ref. [22]and are listed in
Table 2.
2.2.3.1.2. Pyrolysis. Pyrolysis is the thermal decomposition of
biomass into volatile gases and char. Pyrolysis is an important step
in gasification process because products of pyrolysis process are the
reactants of all the other chemical processes that take place in the
system. The decomposition, which is a result of pyrolysis, is a
complex series of reactions in different pathways. These pathways
may depend on heating conditions and biomass species [24].
Various researchers have developed different reaction schemes of
varying complexity [25]. These models can be classified into three
classes, they are; one step global models, single stage multi reaction
models and multi stage semi global models [24]. The applicability
of these models depends on the species of wood and heating
conditions. In the present work, a one-step global reaction scheme
is used to model the pyrolysis processes [24], [26]. The scheme is
presented in following equation [27].

C2:85 H3:69 O/aC þ bCO þ cCO2 þ dH2 þ eCH4 þ fAsh

(R2)

It is assumed that the stoichiometric coefficients are dependent
on the species of wood. This gives the overall mathematical model
the ability to analyze different wood species.
The coefficients are determined using experimental data obtained by proximate analysis and an assumed distribution of volatiles gases based on previous literature.
The coefficients for carbon and ash are directly determined by
the fraction of free carbon and ash content given by proximate
analysis. For gas species, each coefficient is determined by
following equation.

ai ¼ bi VF

(20)

Where, ai represents a, b, c etc. for different values of i. The factor

b describes the distribution of gases in the volatile fraction and VF is

the volatile fraction of wood species under interest. For present
study values for b is estimated by using data given in previous literatures [15].
The pyrolysis rate is calculated by,



ÀEp
εs rs YC2:85 H3:69 O
rp ¼ fp exp
RTs

(21)

2.2.3.1.3. Char combustion and gasification reactions. Three main
heterogeneous reactions of char are considered in the present
study. These are combustion, carbon dioxide gasification and water
gasification as follows;

C þ aO2 /2ð1 À aÞCO þ ð2a À 1ÞCO2

(R3)

C þ CO2 /2CO

(R4)

C þ H2 O /CO þ H2

(R5)

The parameter a is dependent on the fuel temperature and is
given by Eq. (22) [14] [18].



2 þ 2512exp À6420
Ts
 :

a¼ 

2 1 þ 2512exp À6420
Ts

(22)

The actual reaction rates of these reactions depend on two
factors. The kinetic rate and mass transfer rate of the reactant gas
into the surface of the porous char. Usually the reaction rate is
limited by the mass transfer process, because mass transfer rates
are much slower than the kinetic rates at higher temperatures. The
kinetic rates of above reactions can be generally expressed as follows [28];



E
Mc
rk;i ¼ Ac Ai Ts exp À i
r
RTs vi Mi i

(23)

As the solid phase is a collection of three components; unreacted biomass, char and ash, only a fraction of the solid phase
contains char. Because of this fact, the entire specific surface area of
solid phase, which is given by Eq. (8), is different to the specific
surface area of char. The specific surface area of char, Ac ,is evaluated based on the ratio of char formation to the maximum amount
of possible char generation due to total pyrolysis process. This is
expressed in equation (24).

Ac ¼

mchar
A
a:mðC2:85 H3:69 OÞinitial

(24)

In Eq. (24), mchar is the mass of char per unit volume in a given
position, a is the stoichiometric coefficient of char in pyrolysis reaction (Eq. R2) and mðC2:85 H3:69 OÞinitial is the initial mass of wood per
unit volume at the same position. A is the total specific surface area
of solid phase given by Eq. (8).
Mass transfer rate of a reactant gas to the surface of the char
particle was calculated by the following equation [29].



rm;i ¼ km;i Ad ri À ri;s

(25)

Two simplifying assumptions are used to derive Eq. (25). First, it
is noted that diffusion is anisotropic in the vicinity of biomass
particle. This effect is due to the air flow around the biomass particle. Diffusion is stronger in parallel to the air flow and minimum in
the direction of perpendicular to the air flow. In order to account for
this anisotropy with an isotropic mass transfer coefficient, it is
assumed that mass diffusion occurs only parallel to the flow, and
based on a cubic biomass particle. This assumption reduces the
specific diffusive surface area to 1/6th of total specific surface area,
as given in Eq. (26).

N. Fernando, M. Narayana / Renewable Energy 99 (2016) 698e710

1
Ad ¼ A
6

(26)

Second assumption is used to evaluate ri;s , the density of the
gasifying agent at the surface of the biomass particle. After the
gasifying agent reaches the surface of char particle, diffusion process is completed and reaction between gasifying agent and char
progresses at the kinetic rate given by Eq. (23). At the temperatures
prevailing in combustion processes, this rate is higher compared
with diffusion rate. Hence it is assumed that once the gasifying
agent reaches the particle surface, it undergoes immediate conversion and as a result ri;s ¼ 0 can be used in evaluating the
diffusion rate using Eq. (25).
The mass transfer coefficient of ith gas, km;i , is evaluated using
following correlation [18].
1

Shi ¼ 2 þ 0:1Sc 3 Re0:6

(27)

The overall reaction rates of heterogeneous reactions are obtained by evaluating the equivalent parallel resistance of the kinetic
and mass transfer rates, this is given by Eq. (28) [18];

rk;i rm;i
ri ¼

:
rk;i þ rm;i

703

Table 2
Kinetic data for evaluation of reaction rates.
Reaction

Pre-exponential factor

Activation energy

Source

R1
R2
R3
R4
R5
R6
R7
R8
R9

5.56 Â 106 sÀ1
1 Â 108 sÀ1
0.652 m sÀ1 KÀ1
3.42 m sÀ1 KÀ1
3.42 m sÀ1 KÀ1

2.32 Â 1012 (kmol/m3)À0.75sÀ1
1.08 Â 1013 (kmol/m3)À1sÀ1
5.16 Â 1013 (kmol/m3)À1sÀ1 K
12.6 (kmol/m3)À1sÀ1

87.9 kJ molÀ1
125.4 kJ molÀ1
90 kJ molÀ1
129.7 kJ molÀ1
129.7 kJ molÀ1
167 kJ molÀ1
125 kJ molÀ1
130 kJ molÀ1
2.78 kJ molÀ1

[22]
[24]
[28]
[28]
[28]
[19]
[19]
[19]
[19]

to the minimum value of kinetic rate and turbulent mixing rate
[14].There are certain areas of flow, especially near walls where
turbulence is low, in such areas reactants are not well mixed
together for reactions to proceed at kinetic rates. Limiting
assumption in Eq. (30) is used to account for this effect. Kinetic

rates play a major role in free board area and away from the walls,
where turbulence is well developed.

À
Á
ri ¼ min rk;i ; rt;i

(30)

(28)

2.3. Modelling of bed shrinkage
2.2.3.2. Homogenous reactions. Following homogenous reactions
taking place between gas phase components are considered in this
study.

CO þ 0:5O2 /CO2

(R6)

H2 þ 0:5O2 /H2 O

(R7)

CH4 þ 2O2 /CO2 þ 2H2 O

(R8)

CO þ H2 O#H2 þ CO2

(R9)

Expressions for kinetic reaction raterk , of these reactions are
obtained from literature [19] and are listed in Table 1.
Kinetic data used for evaluation of reaction rates are summarized in Table 2.
The kinetic reaction rate is limited by the turbulent mixing rate
of the gas species. The turbulent mixing rate is calculated according
to the eddy dissipation model, which is given by Eq. (29) [14];

rt;i

Yj
ε
Y
¼ 4rg min
; k
k
vj Mj vk Mk

!
(29)

where; j and k represents the reactants of reaction i.
The reaction rate for each gas phase reaction is taken to be equal

Table 1
Rate expressions for homogenous reactions.
Reaction
R6
R7

R8
R9

Rate expression


0:25
½H2 OŠ0:5
2:32 Â 1012 exp À167
RTg ½COŠ½O2 Š


1:08 Â 1013 exp À125
RTg ½H2 Š½O2 Š


5:16 Â 1013 TgÀ1 exp À130
RTg ½CH4 Š½O2 Š
1
0


 ÃÂ
Ã
½CO2 Š½H2 Š
2:78
A
@
CO H2 O À
12:6exp À RTg

À Á
0:0265exp

3968
Tg

As heterogeneous reactions of char progresses, the volume of
char particles reduces. As a result, top layers of the biomass bed
moves downwards. This motion is important to keep the combustion zone stable. When fuel is consumed in combustion zone, new
char particles from pyrolysis zone enters to the combustion zone as
a result of this bed motion. If particle movement is not there, the
combustion zone tends to propagate along the height of the
gasifier, reducing the quality of the producer gas. So it is important
that the model should be capable of predicting the bed motion.
The effect of bed motion is included into solid species equations
as a convective flow term. It is assumed that the bed motion can be
represented by a continuous velocity field of the solid phase and
this velocity, called shrinkage velocity is applied to all solid species
as in Eq. (31).
Shrinkage velocity is calculated by equating the downward
volumetric flow rate of solid phase to total reduction rate of volume
caused as a result of heterogeneous reactions of char. Shrinkage
velocity in Eqs. (5) and (6) is evaluated using following equation.

1
Us ¼
rs Ag

Z

R3;R4;R5
X

!
ri dV

(31)

i

Use of this velocity in convective terms of solid species equations cause the solid species fields of the gasifier to move downwards at the shrinking rate. This causes the solid phase to move
downwards and extend the free board region. But mathematical
equations used in free board region and solid phase region are
different. Therefore when shrinkage modelling is used there has to
be a procedure to track the interface and change the mathematical
equations above and below the interface to obtain an accurate solution. The changes of the equations are presented in graphical
form in Fig. 3.
Gas phase equations differ in two regions with respect to the gas
phase porosity, which is defined as the volume fraction of gas phase
in each computational cell. The porosity field is initialized in the
beginning of the simulation through initial conditions. Gas phase
porosity is equal to one in free board region and a variable (<1) in
packed bed. The source terms that arise as interactions with solid


704

N. Fernando, M. Narayana / Renewable Energy 99 (2016) 698e710

Fig. 3. Changes of governing equations as a result of bed shrinkage.

equation [18].

kg ¼ 4:8 Â 10À4 Tg0:717 :

(35)

The thermal conductivity of solid phase is evaluated using a
correlation developed for thermal conductivity of a quiescent bed,
with a correction for the effect of gas flow, as proposed in the
literature [18]. This correlation is presented by Eq. (36).

ks ¼ 0:8kg þ 0:5Re:Pr:kg
Fig. 4. Motion of unit step variable c in the direction of bed shrinkage.

phase are not present in free board region. Solid phase equations
are different entirely in two regions. In free board, a solid phase
does not exist and values of solid phase quantities should be zero.
The CFD solver should consider these changes as shrinkage
progresses.
This is achieved by multiplying certain terms of the general
transport equation by a new field variablec, which is a unit step
function moving along with shrinkage velocity, as illustrated in
Fig. 4.
The value of c is evaluated based on gas phase porosity; it is
assumed that a certain point (i.e a computational cell) in the solution domain belongs to free board when gas phase porosity exceeds 95%. Classification of computational cells based on porosity is
discussed in literature [15]. Hence c can be written as,

c¼

&

1 ; if εg < 0:95
0 ; if εg > 0:95

(32)

This produces a moving c field along with the packed bed as
expected.
The transport equations for gas and solid phases indicated in
Fig. 3 can be then generalized as,

vðεs rs ∅Þ
þ cV$ðεs rs ∅U s Þ ¼ cV$ðGV∅Þ þ cS∅ þ cSg;∅
vt


v εg rg ∅
vt



þ V$ εg rg ∅U g ¼ V$ðGV∅Þ þ S∅ þ cSs;∅

(33)

(34)

Depending on the value of c the solver will selectively apply
equations in packed bed and free board region as shrinkage

progresses.
2.4. Physical properties
Gas phase thermal conductivity is evaluated by following

(36)

Biomass particle diameter is used as the characteristic length in
evaluating the Reynolds number. Two approaches are used to
calculate the evolution of particle diameter in literature. These are
shrinking core model and volumetric shrinking density model [28].
In shrinking core model, particle diameter gradually reduces with
conversion. In volumetric shrinking density model particle size is
held fixed while density reduces [28]. In the present case, shrinking
density model is used and particle diameter is held constant while
density of solid phase is reduced according to Eq. (37). This
assumption is used, as the developed model is a two fluid model,
which considers the solid phase as a continuum rather than an
assembly of solid particles and the motion of the solid phase is
affected by its density rather than the particle size.

rs;t ¼ rs;tÀΔt YðC2:85 H3:69 OÞ;tÀΔt þ rs;tÀΔt Ychar;tÀΔt þ rs;tÀΔt Yash;tÀΔt
(37)
ðC2:85 H3:69 OÞ Density value is updated explicitly using Eq. (37)
during each temporal iteration.
Heat capacities of solid and gas phases are assumed to vary
according to the relations given in Eqs. (38) and (39). The correlation for gas phase heat capacity is taken from the literature [18].
Solid phase heat capacity is modelled by using Eq. (38) [22].

Cs ¼ 420 þ 2:09Ts þ 6:85 Â 104 TsÀ2

(38)

Cg ¼ 990 þ 0:122Tg À 5680 Â 103 TgÀ2

(39)

Volume fractions of solid and gas phases are calculated using
Eqs. (40) and (41).

εs ¼

mC2:85 H3:69 O

rC2:85 H3:69 O

εg ¼ 1 À εs

þ

mchar

rchar

þ

mash

rash

(40)

(41)

The binary diffusion coefficients based on diffusion of a specific
component in air, are calculated using Eq. (42) [30].


N. Fernando, M. Narayana / Renewable Energy 99 (2016) 698e710

Di;air

sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi


1
1
1
¼ 0:0018583 T 3g
þ
Mi Mair ps2i;air Ui;air

705

Table 3
Discretization schemes.

(42)

Term

Discretization scheme

V$ðrg εg U g 5U g Þ

Upwind

V$rg εg Cv;g Tg U g

Upwind

V$rs εs Cs Ts U s
V$ðrg εg Yi U g Þ

3. Numerical solution

V$ðrs εs Yi U s Þ

Upwind
Upwind
MUSCL

The open source CFD software OpenFOAM was used to develop a
numerical solution to mathematical model presented in the previous section. The equations are numerically solved using finite
volume method. Required code was developed by using Cþþ language in OpenFOAM package, including all the relevant differential
equations and procedures in the CFD model using built in tools of
OpenFOAM. The solution domain is assumed to be two dimensional
and consists of radial (x e direction) and axial (y edirection) dimensions only. The computational domain of CFD model is presented in Fig. 5. Discretization schemes used to discretize
convective and divergence terms are listed in Table 3. A schematic
of solution algorithm is presented in Fig. 6. Grid size is determined
based on the value of non-dimensional turbulent wall distance

(yþ). A value of y þ approximately equal to one is used. This results
in Dx ¼ 0:0094 m for near wall cell layer. Dy ¼ 0:0188 m is used
based on a cell aspect ratio of 1:2. All cells in the domain were set
uniform in size ðDx ¼ 0:0094 m; Dy ¼ 0:0188 mÞ to get a better
numerical resolution. Simulations were performed using a 32 core
High Performance Computer with 2.2 GHz processor speed and
64 Gb RAM. To simulate a single batch wise run, approximately 2 h
were needed in parallel mode using 32 processors for above mesh
resolution. Mesh independence is investigated by increasing the
cell number by 50% and 75% from initial value. Solid phase temperature at the same location in mesh at same time was compared
under refined meshes. It is found that deviation is less than 1%. As a
result, initial mesh is used for subsequent simulations and results
are validated through experiment.
3.1. Initial and boundary conditions
It is assumed that the gasification process is carried out in a
cylindrical reactor using air at room temperature as the gasifying
medium. This air stream is supplied at constant flow rate from
bottom of the reactor. To model the initial ignition process, a
distributed heat source similar to magnitude of heat generated by a

Producer gas outlet (Outlet boundary condiƟons applied)

Free board

Insulated Wall
(Wall boundary
condiƟons applied)

Insulated Wall
(Wall boundary

condiƟons applied)
Porous biomass packed
bed

Air inlet (Inlet boundary condiƟons applied)
Fig. 5. Computational domain of the CFD solution.

Fig. 6. Solution algorithm.

combustion reaction is applied over a bed region of 0.2 m above the
grate and removed after model is capable of continuing operation
by own heat emitted by its combustion reactions. The initial heat
source is responsible for pyrolysing a small region of packed to
generate char necessary to initiate combustion reactions. This start
up method was chosen as it closely resembles the real world
operation of a gasifier. The required time for initial ignition was
found by trial and error by simulating the system. Initially 5 min
time was applied and it was increased gradually until simulation
was successfully progressed.
The initial velocity field within the reactor is taken as zero.
Pressure is set to atmospheric pressure. The initial temperatures of
gas and solid phases are taken as 300 K. Initial compositions of
product gases are taken as zero and the inlet gas composition is
taken to be equal to that of air at room temperature and atmospheric pressure. Boundary conditions for velocity, pressure, temperature and species mole fractions are indicated in following
equations.
Inlet boundary conditions


706

N. Fernando, M. Narayana / Renewable Energy 99 (2016) 698e710

U ¼ ð0; Uin ; 0Þ

(43)

P ¼ Pin

(44)

Tg ¼ Tg;in

(45)

vTs
¼0
vz

(46)

Yi ¼ Yi;air

(47)

Wall boundary conditions

U¼

vP vTg vTs vYi
¼

¼
¼
¼0
vr
vr
vr
vr

(48)

Outlet boundary conditions;

vU vP vTg vTs vYi
¼
¼
¼
¼
¼0
vz
vz
vz
vz
vz

(49)

Table 4
Physical and chemical properties of fuel.
Species

Gliricidia

Particle size
Particle shape
Batch weight
Free carbon (dry basis)
Volatiles (dry basis)
Ash (dry basis)
Initial moisture content (dry basis)

20 mm
Cubic
28 kg
17.8%
82.16%
0.04%
20%

phase. There readings comply with gas phase temperatures at the
points, as evident from the figure. The results indicate that temperature in the combustion zone rises to a value about 1300 K, with
a peak value resulting in few centimetres above the grate. A similar
behaviour of temperature variation can be observed in experimental work of Wei Chen at el [31] for updraft gasification of
mesquite and juniper wood. Their results indicate a combustion
zone temperature of nearly 1300 K.

1200

4. Model validation

Ts-Simulation

Tg-Simulation
Experimental gas temperature

800

600

400

200
0

0.2

0.4

0.6
Height from grate (m)

Cyclone separator
Thermocouples

800
600
400
200
0

0.2

0.4

0.6
Height from grate (m)

0.8

1

1.2

1400
Ts-Simulation
Tg-Simulation
Exerimental gas temperature

Temperature (K)

1000
800
600
400
200
0

Air blower

1.2

1000

1200

Gas outlet

1

Ts-Simulation
Tg-Simulation
Experimental gas temperature

1200

Top lid
Outlet pipe

0.8

1400

Temperature (K)

Model is validated by comparing the simulation results with
data obtained from a laboratory scale updraft gasifier. The gasifier
consists of a vertical cylinder with a grate at the bottom. Biomass is
fed from the top of the gasifier through a lid, which is closed after
loading one batch of biomass. The loaded batch is ignited at the
bottom of the gasifier. Air at room temperature is supplied through
the grate by using an air blower. In this experimental facility, four
thermocouples, which record the temperature along the centre

line, were installed along the height of the gasifier. A schematic
diagram of the experimental facility is shown in Fig. 7. Simulation
results were compared against experimental data for gasification of
Gliricidia under an airflow rate of 6 m3/hr. The physical and
chemical properties of fuel are listed in Table 4. The comparison of
temperature profiles obtained from simulations with experimentally measured temperature values using four thermocouples are
presented in Fig. 8. Exit gas temperatures predicted by simulation
and experimental exit gas temperatures are displayed in Fig. 9.
Theoretical and experimental outlet gas compositions are presented in Fig. 10. It can be observed from these figures that the
model is in good agreement with experimental data.
In Fig. 8(c), which presents the temperature profiles after 2.5 h
of initial ignition, the biomass bed has reduced as a result of fuel
consumption due to heterogeneous reactions. The thermocouples
located at 60 cm and 90 cm positions do not encounter any solid

Temperature (K)

1000

0.2

0.4

0.6
Height from grate (m)

0.8

1

1.2

Grate
Ash collecƟng chamber

Fig. 7. Schematic diagram of experimental laboratory scale gasification system.

Fig. 8. Theoretical and experimental temperature profiles; (a) 45 min after ignition (b)
75 min after ignition (c) 150 min after ignition.


Temperature (K)

N. Fernando, M. Narayana / Renewable Energy 99 (2016) 698e710

900
800
700
600
500
400
300
200
100
0

707

SimulaƟon
Experimental data

45
75
120
150
180
minutes minutes minutes minutes minutes
Fig. 9. Theoretical and Experimental exit gas temperatures.

20
15
SimulaƟon
10

Fig. 11. Development of reaction zones in the solution domain.

Experimental data

5
0
CO2

CO

H2

CH4

Fig. 10. Theoretical and Experimental gas compositions after 30 min of ignition.

The following figure compares the experimental and theoretical
exit gas temperatures of the gasifier.
It can be observed that at higher temperatures, the difference
between experimental value and theoretical prediction is higher.
The CFD model predicts a higher outlet gas temperature than the
observed value. This is because the radiation losses from the gas
phase through walls and the top lid of the gasifier are not accounted
in the model. And the radiation losses become higher at higher
temperatures.
During the simulations, it is found that composition of produced
gas varies with time, during initial period, lot of raw biomass is
present in the bed and moisture levels are higher. This introduces
moisture into gas phase. Pyrolysis in top layers is not complete and
as a result low amount of char is available on the top layers to react
with carbon dioxide produced in the combustion zone. The initial
gas is therefore higher in carbon dioxide. Experimental data and
simulation results for gas composition after 30 min of initial ignition are presented in Fig. 10.
The values for gas compositions are also comparable with
experimental observations of C.Mandlet et al. [22]. Their experimental data for a fixed bed updraft gasifier operated with softwood
pellets indicate a final CO volume percentage of 22.6%, a CO2 percentage of 4.8%, H2 percentage of 4.3% and a CH4 percentage of 2.7%.
Experimentally it is found that during the process of gasification, packed bed can be separated into four zones; drying, pyrolysis,
reduction and combustion, depending on the main processes taking place in these zones. It is possible to identify the development
of these zones in the present CFD model by observing the carbon
dioxide mass fraction along the height of the gasifier. This is presented in Fig. 11.
The two CO2 hot spots in Fig. 11 can be attributed to near wall
flow stagnation. The dark blue and green interface just below the
hot spots marks the pyrolysis reaction front. Pyrolysis reactions
take place in region above this interface which generates CO2. The
produced CO2 is transported to higher regions of the bed through

convection due to gas flow. In near wall region, flow velocity is very
low. This reduces the convective transport and tends to accumulate
CO2 in near wall cells, increasing its concentration in comparison
with centre cells.
During a batch process the quality of the produced gas varies
with the time, mainly as a result of downward motion of the fuel
bed. During experiments it is observed that a stable flame cannot be
maintained approximately after 4 h of operation. Fig. 12 present the
variation of outlet gas mass fractions and Fig. 13 present the bed
movement. The packed bed location is identified by viewing the
solid phase temperature profile.
Velocity distributions within the gasifier at different times are
presented in Figs. 14 and 15.
An increase in flow velocity can be observed in free board region
according to Fig. 14. This increase is due to the release of gases from
packed bed to free board region, especially during pyrolysis. Volatiles are released to gas phase increasing its velocity and pyrolysis
zone is located in top layers of the packed bed, which can be
observed in Fig. 11. A span wise variation of velocity can be observed
in free board region, which can be clearly noticed in Fig. 15. This
variation is reduced in packed bed, mainly due to the effect of
porosity. In a batch wise simulation as in the present case, free
board region extends with time and span wise velocity variation
becomes significant. Even within the packed bed, a reduction of
flow velocity near walls can be noticed, this effect is reflected in CO2
hot spots in Fig. 11, where CO2 is accumulated due to low convective

0.35
CO2
H2
CH4

CO

0.3
0.25
Mass fraction

Volume percentage

25

0.2
0.15
0.1
0.05
0
0

2000

4000

6000

80
000
Time (s)

10000

12000

Fig. 12. Variation of gas phase component mass fractions with time.

14000


708

N. Fernando, M. Narayana / Renewable Energy 99 (2016) 698e710

Fig. 13. Bed reduction with time; (a) 1 h after ignition (b) 2 h after ignition (c) 3 h after ignition (d) 4 h after ignition. Location of interface between packed bed and free board is
marked with the black arrow.

Fig. 14. Axial velocity distribution; (a) 2 h after ignition (b) 2.5 h after ignition (c) 3 h after ignition.

0.04
Packed bed
Free boader

0.035

Axial velocity (m/s)

0.03
0.025
0.02
0.015
0.01
0.005
0

0

0.05

0.1

0.15
0.2
Spanwise distance (m)

0.25

0.3

0.35

Fig. 15. Span wise axial velocity distributions in free board and packed bed after 3 h
from ignition.

transport. Fig. 16 presents the span wise variation of solid phase
temperature at three different locations within the packed bed.
A span wise variation of temperature can be observed in near
wall area. It can be observed that temperature is lower in near wall
regions and higher in central regions. This is due to the fact that
lower heat transfer coefficient exists between solid and gas phases
in near wall region, because of low value of flow velocity in vicinity
of walls. Heat transfer coefficient between solid and gas phase is
evaluated using Eq. (9). Dimensionless numbers are evaluated
based on a characteristic particle length scale equal to initial particle size. Reynolds number for heat transfer varies in the range of

0e40. Prandlt number varies in the range 0.667e1.16 and Nusselt
number varies between 2 and 9.98, with values closer to 2 in near
wall region. Maximum temperature occurs near the central axis.
This span wise variation reduces at regions closer to the grate,
where heat is generated within bed through combustion reactions.


N. Fernando, M. Narayana / Renewable Energy 99 (2016) 698e710

1200

general experimental observations on packed bed gasification
processes. The developed model evaluates optimal air flow rate to
be 7 m3/h for maximum cumulative CO production for the studied
gasifier. In future, the presented mathematical model can be used
as a numerical tool to optimize batch wise moving bed gasification
processes. The model consists of many runtime variable input parameters such as particle size, inlet air flow rate, inlet-gas compositions and physical & chemical properties of feed-stock. The model
can be used to perform parameter studies to find the optimum
values of these parameters for a particular process. The model can
be further improved by implementing an advanced pyrolysis
scheme, including primary and secondary pyrolysis reactions
separately and tar formation reactions.

Temperature (K)

1000

800
0.3 m
0.4 m

0.5 m

600

400

200
0

0.05

0.1

0.15
Spamwise distance (m)

0.2

0.25

709

0.3

Acknowledgment
Fig. 16. Span wise temperature distribution at different locations within packed bed
after 1.5 h from ignition. Dashed line: 0.3 m from grate. Dash dot line 0.4 m from grate.
Solid line: 0.5 m from grate.

Table 5

Simulation Results for different air flow rates.

The authors are thankful to senate
versity of Moratuwa (SRC/CAP/14/06),
port for the research project and
experimental data from his research
presented mathematical model.

research committee of Unifor providing financial supMr. M. Amin for sharing
work for validation of the

Flow rate (m3/hr) Batch time (s) Peak CO composition Cumulative CO (m3)
4
5
6
7
8
9
10

21600
15800
14400
12800
11300
10800
9200

0.38
0.41

0.404
0.413
0.406
0.404
0.401

5.2
5.51
6.11
6.35
5.68
5.53
5.372

The temperature increase due to combustion reactions become
dominant than convective heat transfer contribution.
5. Optimization of air flow rate to the gasifier based on CFD
model
The external air flow to the gasifier supply fuel needed to
maintain combustion process. When air flow rate is higher, the
extent of the combustion zone increases, causing the produced fuel
gases to burn inside the reactor. This reduces the quality of the
outlet gas. When flow rates are too small, combustion rates reduce
and sufficient heat is not produced for complete cracking of
biomass in top layers of the bed. The developed CFD model is used
to evaluate optimal air flow rate for maximum cumulative carbon
monoxide production.
A series of simulations were performed for air flow rates ranging
from 4 m3/hr to 10 m3/hr. The results are summarized in Table 5.
Based on the cumulative CO production, it can be stated that, for

the particular experimental gasifier, a flow rate of 7 m3/hr maximizes the CO yield from biomass batch.
6. Conclusion and future work
A mathematical model for gasification of biomass in a batch
wise updraft packed bed reactor was developed and simulated
using open source CFD software OpenFOAM. The developed model
in this study accounts for drying, pyrolysis, reduction and combustion reactions. All three modes of heat transfer; conduction,
convection and radiation, was included in the packed bed model. It
is found by the simulation study; radiation is the main mode of heat
transfer through the biomass packed bed and critically important.
Reduction of bed volume due to heterogeneous reactions are also
considered and modelled in the simulations. The simulation results
are in good agreement with experimental data and also with

References
[1] P. McKendry, Energy production from biomass (part 1): overview of biomass,
Bioresour. Technol. 83 (1) (May 2002) 37e46.
[2] K. Umeki, T. Namioka, K. Yoshikawa, Analysis of an updraft biomass gasifier
with high temperature steam using a numerical model, Appl. Energy 90 (1)
(Feb. 2012) 38e45.
[3] P. McKendry, Energy production from biomass (part 2): conversion technologies, Bioresour. Technol. 83 (1) (May 2002) 47e54.
[4] K. Srirangan, L. Akawi, M. Moo-Young, C.P. Chou, Towards sustainable production of clean energy carriers from biomass resources, Appl. Energy 100
(Dec. 2012) 172e186.
[5] E.G. Pereira, J.N. da Silva, J.L. de Oliveira, C.S. Machado, Sustainable energy: a
review of gasification technologies, Renew. Sustain. Energy Rev. 16 (7) (Sep.
2012) 4753e4762.
[6] P. McKendry, Energy production from biomass (part 3): gasification technologies, Bioresour. Technol. 83 (1) (May-2002) 55e63.
[7] F. Guo, Y. Dong, L. Dong, C. Guo, Effect of design and operating parameters on
the gasification process of biomass in a downdraft fixed bed: an experimental
study, Int. J. Hydrogen Energy 39 (11) (Apr. 2014) 5625e5633.
[8] M.A. Masmoudi, M. Sahraoui, N. Grioui, K. Halouani, 2-D Modeling of thermokinetics coupled with heat and mass transfer in the reduction zone of a fixed

bed downdraft biomass gasifier, Renew. Energy 66 (Jun. 2014) 288e298.
[9] A.H. Mahmoudi, F. Hoffmann, B. Peters, Application of XDEM as a novel
approach to predict drying of a packed bed, Int. J. Therm. Sci. 75 (Jan. 2014)
65e75.
[10] G. Cau, V. Tola, A. Pettinau, A steady state model for predicting performance of
small-scale up-draft coal gasifiers, Fuel 152 (Jul. 2015) 3e12.
[11] D.F. Fletcher, B.S. Haynes, F.C. Christo, S.D. Joseph, A CFD based combustion
model of an entrained flow biomass gasifier, Appl. Math. Model. 24 (3) (Mar.
2000) 165e182.
[12] K.D. Patel, N.K. Shah, R.N. Patel, CFD analysis of spatial distribution of various
parameters in downdraft gasifier, Procedia Eng. 51 (2013) 764e769.
[13] S. Murgia, M. Vascellari, G. Cau, Comprehensive CFD model of an air-blown
coal-fired updraft gasifier, Fuel 101 (Nov. 2012) 129e138.
[14] Y. Wu, Q. Zhang, W. Yang, W. Blasiak, Two-dimensional computational fluid
dynamics simulation of biomass gasification in a downdraft fixed-bed gasifier
with highly preheated air and steam, Energy Fuels 27 (6) (2013) 3274e3282.
[15] K. Kwiatkowskia, P. Zukb, Biomass gasification solver based on OpenFOAM,
Comput. Phys. Commun. (2013) 1e29.
[16] H.K. Versteeg, W. Malalasekera, in: M. Harlow (Ed.), An Introduction to
Computational Fluid Dynamics: the Finite Volume Method, 2 edition., Prentice
Hall, England; New York, 2007.
[17] Y.-Q. Zhuang, X. Gao, Y. Zhu, Z. Luo, CFD modeling of methanol to olefins
process in a fixed-bed reactor, Powder Technol. 221 (May 2012) 419e430.
[18] T. Jurena, Numerical Modelling of Grate Combustion, Brno University of
Technology, Brno, 2012.
[19] H. Liu, CFD Modeling of Biomass Gasification Using a Circulating Fluidized Bed
Reactor, University of Waterloo, Ontario, Canada, 2014.
[20] ANSYS Fluent, ANSYS FLUENT Theory Guide, 15317, ANSYS Inc, Canonsburg,
PA, 2011, p. 2009.
[21] A. Nikolopoulos, I. Malgarinos, N. Nikolopoulos, P. Grammelis, S. Karrelas,

E. Kakaras, A decoupled approach for NOxeN2O 3-D CFD modeling in CFB
plants, Fuel 115 (Jan. 2014) 401e415.
[22] C. Mandl, I. Obernberger, F. Biedermann, Modelling of an updraft fixed-bed


710

N. Fernando, M. Narayana / Renewable Energy 99 (2016) 698e710

gasifier operated with softwood pellets, Fuel 89 (12) (Dec. 2010) 3795e3806.
[23] Purnomo, Model for a Downdraft Solid Fuel Combustor, The University of
Wisconsin, 1988.
[24] S. Sinha, A. Jhalani, M.R. Ravi, A. Ray, Modelling of pyrolysis in wood: a review,
SESI J. 10 (1) (2000) 41e62.
[25] B.V. Babu, A.S. Chaurasia, Heat transfer and kinetics in the pyrolysis of
shrinking biomass particle, Chem. Eng. Sci. 59 (10) (May 2004) 1999e2012.
[26] N. Fernando, M. Amin, M. Narayana, T. Jayawickrama, S. Jayasena,
A mathematical model for pyrolysis of biomass, in: Moratuwa Eng. Res. Conf.
(MERCon), IEEE Conf. Publ, 2015, pp. 1e6.
[27] M. Amin, Development of Updraft Gasifier and Performance Analysis for

Different Types of Biomass Materials, University of Moratuwa, Sri Lanka, 2015.
[28] Y. Haseli, J.A. van Oijen, L.P.H. de Goey, A detailed one-dimensional model of
combustion of a woody biomass particle, Bioresour. Technol. 102 (20) (Oct.
2011) 9772e9782.
[29] R.E. Treybal, Mass-transfer Operations, third ed., McGraw-Hill Book Company,
1980.
[30] R.B. Bird, W.E. Stewart, E.N. Lightfoot, Transport Phenomena, second ed.,
Wiley, New York, 2001.
[31] W. Chen, K. Annamalai, R.J. Ansley, M. Mirik, Updraft fixed bed gasification of

mesquite and juniper wood samples, Energy 41 (1) (May 2012) 454e461.