Two dimensional numerical computation of a circulating fluidized bed biomass gasifier

Computers and Chemical Engineering 48 (2013) 234–250

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Computers and Chemical Engineering
journal homepage: www.elsevier.com/locate/compchemeng

Two dimensional numerical computation of a circulating fluidized
bed biomass gasifier
Afsin Gungor ∗ , Ugur Yildirim
Department of Mechanical Engineering, Faculty of Engineering, Akdeniz University, 07058 Antalya, Turkey

a r t i c l e

i n f o

Article history:
Received 20 December 2011
Received in revised form 10 July 2012
Accepted 21 September 2012
Available online 3 October 2012
Keywords:
Fluidized bed
Simulation
Biomass
Gasification

a b s t r a c t
A two dimensional model for an atmospheric CFB biomass gasifier has been developed which uses the
particle based approach and integrates and simultaneously predicts the hydrodynamic and gasification
aspects. Tar conversion is taken into account in the model. The model calculates the axial and radial

distribution of syngas mole fraction and temperature both for bottom and upper zones. The proposed
model addresses both hydrodynamic parameters and reaction kinetic modeling. Results are compared
with and validated against experimental data from a pilot scale air blown CFB gasifier which uses different
types of biomass fuels given in the literature. Developed model efficiently simulates the radial and axial
profiles of the bed temperature and H2 , CO, CO2 and CH4 volumetric fractions and tar concentration
versus gasifier temperature. The minimum error of comparisons is about 1% and the maximum error is
less than 25%.
© 2012 Elsevier Ltd. All rights reserved.

1. Introduction
In order to have environment friendly hydrogen, it must be produced by renewable methods. A number of ways and a variety
of resources for producing renewable hydrogen are being investigated. Of all the renewable resources, biomass holds the greatest
promise for hydrogen production in the near future (Mahishi
& Goswami, 2007). Bio-chemical and thermo-chemical processes
are used for the recovery of energy from biomass. Bio-chemical
process involves bio methanization of biomass. Thermo-chemical
processes are combustion, pyrolysis and gasification. Gasification is
economical at all capacities from 5 kWe onwards. Therefore, there
is a constant and consistent interest in the production of energy
from biomass through gasification (Kirubakaran et al., 2009). Gasification is a robust proven technology that can be operated either
as a simple, low technology system based on a fixed-bed gasifier,
or as a more sophisticated system using fluidized-bed technology (McKendry, 2002). In the past decades, significant efforts have
been directed towards the development of biomass gasifiers to
replace traditional combustion systems (Brown, Gassner, Fuchino,
& Marechal, 2009).
Fluidized-bed gasifiers provide excellent mixing and gas/solid
contact, causing high reaction rates and conversion efficiencies.
Further, there is the possibility of addition of catalysts to the bed
material to influence product gas mole fraction and reduce its tar
content (Schuster, Löffler, Weigl, & Hofbauer, 2001).

∗ Corresponding author. Tel.: +90 532 397 30 88; fax: +90 242 310 63 06.
E-mail address: (A. Gungor).
0098-1354/$ – see front matter © 2012 Elsevier Ltd. All rights reserved.
/>
The circulating fluidized bed (CFB) is a natural extension of the
bubbling bed concept, with cyclones or other separators employed
to capture and recycle solids in order to extend the solids residence
time. The riser of a CFB gasifier operates in either the turbulent or
fast fluidization flow regime. CFB gasification is now undergoing
rapid commercialization for biomass. Fundamental and pilot studies are, nevertheless, required for scale-up, as well as to fill gaps in
understanding the underlying principles (Li et al., 2004).
Design and operation of a gasifier requires understanding of
the effect of various operational parameters on the performance of
the system. The simulation of the gasifier can provide a quantitative tool for gaining insight into and understanding the integrated
process. It is very useful for the analysis, evaluation, and design
of the process. Researchers have done a lot of work with regard
to modeling of fluidized beds in biomass gasification. Hydrodynamics, heat transfer, and reaction kinetics play crucial role on the
gasification performance of a CFB biomass gasifier. Hydrodynamic
models based on the fundamental laws of conservation of mass,
momentum, energy, and species conversion have enabled us to
give better understanding of the fluidized beds and to be useful
to enhance the process performance (Vejahati, Mahinpey, Ellis,
& Nikoo, 2009). As the computational capacity increased, computational fluid dynamics (CFD) had become an advanced tool in
modeling hydrodynamics, and it is now considered as a standard
tool for the simulation of single-phase flows. However, CFD still
needs verification and validation for modeling multiphase flow systems such as fluidized beds. Further improvements regarding the
flow dynamics and computational models may be required to make
CFD more suitable for fluidized bed reactor modeling and scale-up
(Nguyen, Ngo, et al., 2012; Vejahati et al., 2009).

A. Gungor, U. Yildirim / Computers and Chemical Engineering 48 (2013) 234–250

Nomenclature
A
Vi∗
Ar
C
Cp
D
db
db0
dbm
Ei
ER
hp
k0,i
kbe
M
N
Re
rj
T
Tp
u0
ub
umf
Vi
z

cross section area of the gasifier (m2 )
the ultimately attainable yield of volatile matter for
the gaseous component i (kg/kg biomass)
Archimedes number
gas concentration (mol/m3 )
specific heat (J/mol K)
bed diameter (m)
bubble diameter (m)
initial bubble size (m)
the limit size of bubble expected in a bed (m)
apparent activation energy for component i (kJ/mol)
equivalence ratio
heat exchange coefficient between particle and the
emulsion phase (J/m2 s K)
per-exponential factor (1/s)
exchange coefficient between bubble and emulsion
phase per unit volume of bubble phase (1/s)
number of components
number of reactions
Reynolds number
reaction rate of j reaction (mol/m3 s)
temperature (K)
temperature of biomass particle (K)
superficial velocity (m/s)
bubble velocity (m/s)
minimum fluidization velocity (m/s)
instantaneous yield of volatile matter for the
gaseous component i (kg/kg biomass)
axial coordinate of the reactor (m)

Greek letters
˛s
specific particle surface area (m2 /m3 )
εb
void fraction of the bubble phase
εmf
void fraction in the dense phase at minimum fluidization conditions
stoichiometric coefficient of component i of reaction
ij
j
Subscripts
b
bubble phase
e
emulsion phase
p
particle

Dealing with gas–solid hydrodynamics, two different
approaches are generally used to apply CFD modeling to the
gas–solid fluidized beds: (1) Eulerian–Lagrangian model (so-called
Lagrangian model) and (2) Eulerian–Eulerian model (Eulerian
model) (Gungor & Eskin, 2007; Nguyen, Ngo, et al., 2012).
Lagrangian models solve the Newtonian equations of motion for
each individual particle, taking into account the effects of particle
collisions and forces acting on the particle by the gas (Gungor &
Eskin, 2007). The Lagrangian model is normally limited to a relatively small number of particles because of computational expense
(Taghipour, Ellis, & Wong, 2005). Eulerian models consider all
phases to be continuous and fully interpenetrating. The equations

employed are a generalization of the Navier–Stokes equations for
interacting continua. Regarding the continuum representation of
the particle phases, Eulerian models need additional closure laws
to describe the rheology of particles. An extension of the classical
kinetic theory of gases to the dense particle flow is most commonly
used (Reuge et al., 2008). The Eulerian model makes it possible to

235

be applied to multiphase flow processes containing a large volume
fraction of solid particles (Huilin, Yurong, & Gidaspow, 2003).
Researchers such as Gungor and Eskin (2007), Gungor (2008a)
and Jiradilok et al. (2008) paid attention to modeling and simulation
of the hydrodynamic characteristics of fluidized bed systems. They
studied particles and gas flow behaviors in the riser section of a CFB
using the kinetic theory for the particulate phase.
Recently a 2D CFD simulation is carried out to study hydrodynamics of a cold-mode dual fluidized bed gasifier including the
riser and gasifier using a commercial CFD code (Fluent Inc., USA)
(Nguyen, Ngo, et al., 2012). Experiments were also conducted on a
pilot-scale DFB in the cold mode. The solid circulation rate and solid
holdup obtained from CFD simulation are compared with those
measured by experiment. In addition, hydrodynamics of the hot
mode is predicted at a given temperature profile along the riser
and gasifier measured by experiment.
Modeling and simulation of biomass gasification may be also
divided into three categories: (1) thermodynamic equilibrium
models (Pröll & Hofbauer, 2008; Shen, Gao, & Xiao, 2008), (2) kinetic
rate models (Corella & Sanz, 2005; Petersen & Werther, 2005), and
(3) neural network models (Brown, Fuchino, & Maréchal, 2006).
In the kinetic rate models, initial conditions and kinetic parameters are not well known because of a variety of feedstock (Corella

& Sanz, 2005). The neural network models as a kind of black-box
models have achieved high prediction accuracy. However, it is hard
to obtain physical meaning from these models, and the scale-up and
adaption abilities of the neural network models are restricted. The
kinetic models predict the progress of product composition with
respect to the residence time in a gasifier, whereas the equilibrium
models provide the maximum yield of a desired product which
is achievable from a gasification system (Li et al., 2004). Although
kinetic rate models are considered as a rigorous approach, equilibrium models are valuable because they can predict thermodynamic
limits which are used to design, evaluate and improve the process (Karmakar & Datta, 2010). The equilibrium models have been
used for preliminary study on the influence of the most important
process parameters.
In their review study Gomez-Barea and Leckner (2010) stated
that, Sanz and Corella (2006) have presented a whole model for CFB
biomass gasifiers. Such model is 1D and for steady state. The model
has a semirigorous character because of the assumptions that had
to be introduced by lack of accurate knowledge in some parts of the
modeling. It must be noted that most common fluidization models
for fluidized bed gasifier are 1D models, but 3D models (Petersen
& Werther, 2005) also fit into this category. Therefore, no matter
if the fluidization model is formulated in one, two or three dimensions, it still needs input from fluid-dynamic knowledge computed
by ‘external’ correlations. CFD for fluidized bed gasifiers are relatively new, and in spite of offering promising expectation, much
has to be added. Because of the considerable computational times
required for CFD computations, especially when chemical reactions are involved, fluidization models are still the most common
approach (Gomez-Barea & Leckner, 2010).
Ngo et al. (2011) investigate the biomass gasification with
the steam agent in a bench-scale CFB gasifier and develop a
quasi-equilibrium three-stage gasification (qETG) model for the
prediction of process performance in dual CFB. The qETG model
is divided into three main stages: (1) pyrolysis of volatiles in

biomass, (2) solid–gas reactions between biomass char and gasifying reagents (carbon dioxide or steam) in the fluidized bed, and
(3) gas-phase reactions among the gaseous species in the free
board of the gasifier. At each stage, empirical models are established based on the experimental data to calculate the gaseous
components. Especially, the deviation from equilibrium reaction
is taken into account in the third stage by a non-equilibrium factor. The model is first validated by the experiment data conducted


236

A. Gungor, U. Yildirim / Computers and Chemical Engineering 48 (2013) 234–250

in the bench-scale CFB gasifier with pine woodchips, and the data
taken from the literature. The effects of gasification temperature
and steam to fuel ratio on product gas composition and yield
were also experimentally investigated for steam gasification of
pine woodchips in a bench-scale CFB gasifier with external heat
supplier.
In order to avoid complex processes and develop the simplest
possible model that incorporates the principal gasification reactions and the gross physical characteristics of the reactor, have
developed models using the process Simulator Aspen Plus. Aspen
Plus is a problem-oriented input program that is used to facilitate
the calculation of physical, chemical and biological processes. If
more sophisticated block abilities are required, they can be developed as FORTRAN subroutines (Arnavat, Bruno, & Coronas, 2010).
Recently, Ramzan, Ashraf, Naveed, and Malik (2011) have developed a steady state simulation model for gasification using Aspen
Plus. The model can be used as a predictive tool for optimization
of the gasifier performance. The gasifier has been modeled in three
stages. In first stage moisture content of biomass feed is reduced.
In second stage biomass is decomposed into its elements by specifying yield distribution. In third stage gasification reactions have
been modeled using Gibbs free energy minimization approach. In
the simulation study, the effect of the operating parameters like

temperature, equivalence ratio (ER), biomass moisture content and
steam injection on syngas composition, high heating value (HHV)
and cold gas efficiency has been investigated.
The most recent study was conducted by Nguyen, Ngo, et al.
(2012) and it concluded a three-stage steady state model developed for biomass steam gasification in a dual CFB to calculate the
composition of producer gas, carbon conversion, heat recovery, cost
efficiency, and heat demand needed for the endothermic gasification reactions. The model was divided into three stages including
biomass pyrolysis, char–gas reactions, and gas-phase reaction. At
each stage, an empirical equation was estimated from experimental data to calculate carbon conversion and gaseous components.
The parametric study of the gasification temperature and the steam
to fuel ratio was then carried out to evaluate performance criteria
of a 1.8 MW DFB gasifier using woodchips as a feedstock for the
electric power generation (Nguyen, Ngo, et al., 2012).
Tar conversion is usually not modeled at all or modeled as one
or two lumped species reacting by oxidation, thermal cracking, or
reforming with H2 O. Fields for further research have been identified
as devolatilization and conversion of tar and char are recognized as
the processes that require major modeling efforts. Formulation of
a model naturally occupies the main attention of a modeler. However, validation is a necessary step before a model can be safely
applied. Unfortunately, very poor detailed experimental data are
available on this argument, and then it is difficult to verify the
general validity of the proposed mathematical models (Cao, Wang,
Riley, & Pan, 2006; Pfeifer, Rauch, & Hofbauer, 2004).
The performance of CFB biomass gasifier may greatly depend on
the movement of solids and gas in the riser. Modeling of solids and
gas mixing can identify the best arrangement for design and operation of the gasifier (Gomez-Barea & Leckner, 2010). The primary
goal of this study has been to improve previous biomass gasification kinetics and hydrodynamic models for CFB biomass gasifiers.
In this study, a two dimensional model for an atmospheric CFB
biomass gasifier has been developed which uses the particle based
approach and integrates and simultaneously predicts the hydrodynamic and gasification aspects. Tar conversion (tar formation

and thermal tar cracking) is taken into account in the model. The
model calculates the axial and radial distribution of syngas mole
fraction and temperature both for bottom and upper zones. The
proposed model addresses both hydrodynamic parameters and
reaction kinetic modeling. The model results are compared with
and validated against experimental data from a pilot scale air blown

CFB gasifier which uses different types of biomass fuels given in the
literature.
2. Model
The two-phase fluid dynamics is of great importance for the
design and operation of the CFBs. Because of containing complex gas–solid flow and gas-phase reactions, modeling of CFBs is
rather difficult. The fluid dynamics of this gas–solid two-phase
flow is very complex and strongly dominated by particle-to-particle
interactions. Furthermore, the numerous homogeneous and heterogeneous catalytic gas-phase reactions and their kinetics for the
description of the gasification phenomena and the tar formation
and destruction are not completely known. The present CFB model
can be divided into three major parts: a submodel of the gas–solid
flow structure; a reaction kinetic model for gasification; and a convection/dispersion model with reaction.
2.1. Hydrodynamic structure
In the present study, gasifier hydrodynamic is modeled taking
into account previous work (Gungor, 2008a). The model addressed
in this paper uses a particle-based approach that considers 2D
motion of single particles through fluids. According to the axial
solid volume concentration profile, the riser is axially divided into
the bottom zone and the upper zone.
Most of the models in the literature do not completely take
account of the performance of the bottom zone, consider the bottom zone as well-mixed distributed flow with constant voidage,
and use generally lumped formulation (Gungor, 2008a). In lumped
formulation, the contribution of the net flow has no meaning, since

solids and gas are lumped into a single component, so no distinction is made between the gas and solids. However, in CFB biomass
gasifier, the reacting gas environment in the wall and core has been
found to be different (Gomez-Barea & Leckner, 2010; Li et al., 2004).
Another point of consideration is that the particle size distributions in the wall layer and the dilute zone of the transport zone
are known to be different. So, an extension by consideration of two
phases in the freeboard (instead of lumping the gas and solid as
in the model developed above), formulated with an explicit distinction between gas and solids and with some exchange of gas,
could be necessary. In addition, different particle size distributions
in the wall and core zones might need to be accounted for (GomezBarea & Leckner, 2010). From this point of view, in this study, the
bottom zone is modeled in detail as two-phase flow that is subdivided into a solid-free bubble phase and a solid-laden emulsion
phase. A single-phase back-flow cell model is used to represent the
solid mixing in the bottom zone. A two-phase model is used for
gas phase material balance. In the upper zone core-annulus solids
flow structure is established. It is assumed that the particles move
upward axially and move from core to the annulus region radially.
Thickness of the annulus varies according to the bed height. In the
annulus region, the particle has a zero normal velocity. The pressure
drop through the bottom zone is equal to the weight of the solids
in this region and is considered only in the axial direction. In the
upper zone, pressure drop due to the hydrodynamic head of solids is
considered in the axial direction while pressure drop due to solids
acceleration is also considered in the axial and radial directions.
The solids friction and gas friction components of pressure drop
are considered as boundary conditions in momentum equations
for solid and gas phases, respectively in the model. Solids friction
is defined as the frictional force between the solids and the wall,
whereas the gas friction is the frictional force between the gas and
the wall. The hydrodynamic model takes into account the axial and
radial distribution of voidage and velocity, for gas and solid phase,

A. Gungor, U. Yildirim / Computers and Chemical Engineering 48 (2013) 234–250

237

Table 1
Hydrodynamic parameters of the bubbling fluidized bed.
Parameters

Equations

Minimum fluidization velocity

umf =

Bubble diameter

db = dbm − (dbm − db0 )e−0.3z/D

[(33.72 + 0.0651Ar)

Cdp

dbm = 0.9394[ D2 (u0 − umf )]

0.8

− 33.7]

0.4

db0 = 0.8716[A0 (u0 − umf )]

0.4

ub = V˙ b +

Bubble velocity

gDb

V¯ b = ϕ(U0 − Umf ) (ϕ = 1.45Ar−0.18 , 102 < Ar < 104 )

⎧
⎨ 0.63

0.71

=

⎩

(0.1m < D ≤ 1.0m)

2.0

(1.0m < D)

V˙ b
ub

Bubble fraction

εb =

Mass transfer coefficient for solids exchange

kbe,s =

3(1−εmf )umf

Mass transfer coefficient for gas exchange

kbe,g =

11
db

Thickness of the annulus

ı
D

1−ε
εp
ε¯ p

Cross-sectional average solids concentration

fg =

Wall friction factor of solid phase

fs =

2.2. Kinetic model
The overall process of biomass gasification in the bubbling fluidized bed can be divided into four steps. The first step is drying,
where the moisture of biomass evaporates. The second step where
volatile components in biomass evaporate is called devolatilization. In the model, volatiles are entering the gasifier with the fed
biomass particles. It is assumed that the volatiles are released along
the riser at a rate proportional to the solid mixing rate. The degree
of devolatilization and its rate increase with increasing temperature (Li & Suzuki, 2009). This is followed by pyrolysis, the step
where the major part of the carbon content of biomass is converted
into gaseous compounds. Biomass pyrolysis generates three different products in different quantities: gas, tar, and char. In the
kinetic model it is assumed that the biomass decomposed directly
to each product i by a single independent reaction pathway (Ji,
Feng, & Chen, 2009; Radmanesh, Chaouki, & Guy, 2006). The rate of
formation of a product i in yield Vi at time t is given by
dVi
= k0,i e−(Ei /RTp ) (Vi∗ − Vi )
dt

(1)

where k0,i and Ei are the pre-exponential factor and the apparent activation energy for component i, respectively. The quantity
Vi∗ is the ultimately attainable yield of component i. Table 3 lists
the parameter values for each species. The parameters are adopted
from the literature (Ji et al., 2009).
As mentioned above, how to prevent the tar formation will be
the key for the biomass gasification. Tar is defined, according to

the International Energy Agency’s tar protocol, as organic components/contaminants with molecular weight greater than benzene.
The chemical formula for tar is CHx Oy . The parameters (x; y) are

H
D

0.21

H−h
H

0.73

= exp[˛(h − hbot )]

=1−

Wall friction factor of gas phase

pressure drop for gas phase, and solids volume fraction and particle
size distribution for solid phase. The hydrodynamic parameters of
the CFB are listed in Table 1. The conservation of mass and momentum equations and the constitutive relations used in hydrodynamic
model are given in Table 2. Further details on the model are given
elsewhere (Gungor, 2008a).

(1−εb )εmf db

= 0.55 · Re−0.22

ε−εmf

Axial profile of the solid fraction along the upper zone

(D < 0.1m)

√
2.0 D

ˇ
2

+ˇ

16
Reg
0.0791
Reg0.25

r
Rb

2

1.3 ≤ ˇ ≤ 1.9

Reg ≤ 2100
2100 < Reg ≤ 100, 000

0.0025

v

temperature and heating rate dependent. In this work, phenol is
used to represent the tar from primary pyrolysis as discussed in
the paper by Gerun et al. (2008). It is well-known that the in-bed
additives or catalysts deeply affect the kinetics of the tar elimination. On the other hand, the thermal cracking of tar, also called
secondary pyrolysis, has a significant effect on the final gas mole
fraction, because more than half of the primary pyrolysis products
accounts for tar. The reactions and the reaction kinetics for the tar
cracking are presented in Table 4 (Ji et al., 2009).
In the last step, the char (char = 1 − total devolatilization) is
partly gasified with steam and converted into gaseous products.
The amount of unreacted char is a function of gasification conditions, such as temperature and biomass particle residence time
in the gasifier. All homogeneous and heterogeneous reactions and
their reaction rates using in the model are given in Table 5.

2.3. Particle based approach
The importance of particle based approach is clearly explained
by Sommariva, Grana, Maffei, Pierucci, and Ranzi (2011). They
stated that the selection of particle size used needs a particular
attention, due to the variability of product yields depending on particle size. These differences could be attributed mainly at a different
biomass composition, even if also intra-particle resistances, which
strong depend on particle shape, could play a definite role.
It is a well-known fact that small particle size biomass significantly increases the overall energy efficiency of the gasification
process, but it also has a negative effect on the gasification plant
cost. It has been estimated that for a 5–10 MWe gasification plant,
about 10% of the output energy is required for the biomass particle
size reduction. On the other hand, an increase in biomass particle
size reduces the pre-treatment costs, but the devolatilization time
increases, and thus the gasifier size increases (Mahishi & Goswami,

2007).
The non-uniformity of the biomass particles will influence gasification reaction rate. However, due to intense mixing caused by
the fluidized sand, temperature longitudinally does not vary much


A. Gungor, U. Yildirim / Computers and Chemical Engineering 48 (2013) 234–250

= 10−8.76·ε+5.43
∂
∂(1−ε)

2

G(ε) =

+ ∂∂zv
∂v
∂r
1
3
2

+

∂z

∂v
∂z

+

2
∂v
∂r

2
εi
+
2
∂u
∂z

Component

Log (k0,i ) (1/s)

Ei (kJ/mol)

Vi∗ (kg/kg
biomass)

Total devolatilization
Total gas
H2
CH4
CO
CO2
H2 O

8.30
2.88

6.17
13.00
11.75
5.39
6.71

133.01
49.37
114.18
251.21
220.66
97.99
103.01

0.9690
0.4760
0.0016
0.0241
0.2164
0.0308
0.0804

and are almost similar, indicating that the irregular shapes and
size of biomass particles do not effect the temperature (Alauddin,
Lahijani, Mohammadi, & Mohamed, 2010). On the other hand, Lv
et al. (2004) observed that the producer gas yield, LHV and carbon
conversion were improved as the biomass particle size decreased.
It was explained that small biomass particles contribute to large
surface area and high heating rate which in turn produce more
light gases and less char and condensate. Therefore, the yield and

composition of the producer gas improved while using the small
particle biomass. Yet another explanation is that for small particle
sizes the pyrolysis process is mainly controlled by reaction kinetics; as the particle size increases, the product gas resultant inside
the particle is more difficult to diffuse out and the process is mainly
controlled by gas diffusion (Chaiprasert & Vitidsant, 2009). Similar
results were obtained by Jand and Foscolo (2005) who studied the
effect of wood particle size (5–20 mm) in a FB.
Since the particle size distribution is known to have a strong
influence on the hydrodynamics and gasification performance, the
model also considers the particle size distribution and the attrition
phenomena. Particles in the bottom zone include particles coming
from the solid feed and recirculated particles from the separator.
Particles in the model are divided into n size groups in the model
and mean particle diameter of different-sized particles considers
as follows:

∂u
∂r

|u − v|
Energy equation

Cεi (1−εi ) 1
3
C
4 D ε 2.65 dp
i

ˇ=

∂u
∂r

+
∂u
∂z

=
rz

zr

=

2
3

−
∂u
∂z
zz

=2

2
3

−
∂u
∂r

=2
rr

Gas–solid friction coefficient

∂u
∂r

+
∂u
∂z

∂u
∂z

+
∂u
∂r

−
=
+

− uCεi cv ∂T
− uCεi cv ∂T
+ εp,i cp ∂T
− u εp,i cp ∂T
− u εp,i cp ∂T
= R − Q˙ wall +

Cεi cv ∂T
∂t
∂r
∂z
∂t
∂r
∂z

C=

1 P
Ru T

εi

2

Ideal gas equation
− ˇ(u − v)
∂( zr εi )
∂r

−

− ˇ(u − v)
∂( rz εi )
∂z

−
∂( rr εi )

∂r

−

∂( zz εi )
∂z
∂(Pε )
− ∂z i

∂(Pεi )
∂r

=−
∂(Cuεi u)
∂r

+
∂(Cuεi )
∂t

∂(Cuεi )
∂t

Momentum equation

out
in

∂(Cuεi u)
∂z

(j = gaseous species)
n˙ j εi + R˙ g,j + J˙ g,j
n˙ j εi −
=
dt

d(Cj εi )

Continuity equation

Gas phase

Table 2
The conservation of mass and momentum equations for each phase and the constitutive relations.

2

+

∂u
∂z

2

+

1
3

∂u
∂r

+

CD = 0.44 Rep ≥ 1000

+ ∂∂rv
∂v
∂z

(1 + 0.15Rep 0.687 ) Rep < 1000

=
zr

24
Rep

=
rz

2
3

−
∂v
∂z

=2

zz

CD =

+ ∂∂rv
∂v
∂z

+ ∂∂zv
∂v
∂r
2
3

−
∂v
∂r

=2
rr

∂r

∂(G(ε)εp,i )

+ ˇ(u − v) −
−
=
+

∂(G(ε)εp,i )

+ ˇ(u − v) −
∂( rz εp,i )
∂z

−

∂( zr εp,i )
∂r
∂( zz ε )
− ∂z p,i

∂( rr εp,i )
∂r

=−
∂r

∂( vεp,i v)
∂z

∂( vεp,i v)

+
∂t

∂( vεp,i )

d( j εp,i )

dt

∂( vεp,i )
∂t

˙ j εp,i + R˙ s,j + J˙ s,j
m
out

˙ j εp,i −
m
in

=

Solid phase

Table 3
Kinetic parameters in Eq. (1).

+ gεp,i

(j = Biomass particles, Bed material)

Solids stress modulus

238

dp =

1

(2)

n
x /dpi
i=1 i

In the fluidized beds, particle attrition takes place by surface
abrasion, i.e. particles of a much smaller size break away from the
original particle. The upper limit size of the fines produced is in
the range 50–100 ␮m (Wang, Luo, Li, Fang, & Ni Cen, 1999). The
attrition rate for the bottom zone is calculated as follows (Wang
et al., 1999):
Ra = ka (U0 − Umf )

Wb
dp

(3)

For the upper zone, attrition rate is defined in terms of gas and
solid velocities:
Ra = ka (u − v)

Wb
dp

(4)

where ka is the attrition constant and is obtained varying in the
range 2–7 × 10−7 with a superficial gas velocity of 4–6 m/s and a
circulating solids mass flux from 100 to 200 kg/m2 s (Wang, Luo,
Ni, & Cen, 2003). In the model, the attrition constant value is taken
as 3 × 10−7 for the biomass particles in the model calculations in
both bottom zone and upper zone (Scala & Chirone, 2006). In the
model, the attrition constant value is taken as 1.9 × 10−7 for the
attrition constant of the inert bed particles (Gungor, 2008a).
Weight fraction of particles after attrition is considered as follows:
xa =

ka (u − v)
dpi

(5)


A. Gungor, U. Yildirim / Computers and Chemical Engineering 48 (2013) 234–250

239

Table 4
Reactions for the thermal tar cracking (Ji et al., 2009).
Chemical reaction

Kinetic equations

C6 H6 O → CO + 0.4C10 H8 + 0.15C6 H6 + 0.1CH4 + 0.75H2

r1 = 107 exp − 10

RT

C6 H6 O + 3H2 O → 4CO + 2CH4 + 2H2
C10 H8 → 7.38C + 0.275C6 H6 + 0.97CH4 + 1.235H2
C6 H6 + 2H2 O → 1.5C + 2.5CH4 + 2CO
C6 H6 O + 4O2 → 3H2 O + 6CO
C6 H6 + 4.5O2 → 6CO + 3H2 O
C10 H8 + 7O2 → 4H2 O + 10CO

7

r2 = 10 exp

5

CC6 H6 O (mol/m3 s)

5
− 10
RT

CC6 H6 O (mol/m3 s)

14

r3 = 1.7 × 10

16

r4 = 2.0 × 10

exp

5
− 3.5×10
RT

exp

5
− 4.43×10
RT

3

r5 = 0.655 × 10 T exp
11

r6 = 2.4 × 10

exp
3

5
− 1.2552×10
RT

r7 = 0.655 × 10 T exp

In the model, particles are considered as spherical. Particles are

discretized into 10 groups totally. The particle size distribution
depends on attrition in the bed. As mentioned above the Sauter
mean diameter is adopted as average particle size (Eq. (2)).
3. Numerical solution
The model allows dividing the calculation domain into m × n
control volumes, in the radial and the axial directions and in the
core and the annulus regions respectively. In this study the calculation domain is divided into 8 × 50 control volumes in the radial
and the axial directions and in the core and the annulus regions
respectively. With the cylindrical system of coordinates, a symmetry boundary condition is assumed at the column axis. At the walls,
a partial slip condition is assumed for the solid and the gas phases.
Tsuo and Gidaspow (1990) had successfully applied the two-fluid
model with effective solid viscosity based on a solid stress modulus to describe core annular flow behavior in a riser. For two-phase
flow, two friction coefficients are obtained, one for the gas and one
for the solid. Modified Hagen–Poiseuille expression is used for wall
friction factor of gas phase and Konno’s correlation is used for wall
friction factor of solid phase in the model (Table 1) (Gungor, 2008b;
Huang, Turton, Park, Famouri, & Boyle, 2006). The temperature has
been evaluated by a thermal balance along each of the control volumes which the fluidized bed has been divided (Table 1). In the
gasifier, temperatures of product gas, bed material and biomass
particles are assumed to be equal. The product gases of biomass
gasification are H2 , CO, CO2 , CH4 , H2 O, C10 H8 , and C6 H6 ; tar is taken
into account as C6 H6 O in the model. Particles are spherical and of
uniform size and the average diameter remain constant during the
gasification, based on the shrinking core model. The set of differential equations governing mass, momentum and energy for the gas
and solid phases are given in Table 2, and are solved with a computer code which is written in FORTRAN and should be modular
to allow users to update component modules easily as new findings become available. The combined Relaxation Newton–Raphson
methods are used for solution procedure. The backward-difference
methods are used for the discretization of the governing equations.
Flow chart of the numerical solution for biomass gasification is
shown in Fig. 1.

The inputs for the model are the dimensions; biomass feed rate
and particle size, biomass properties, air ratio, steam to biomass
ratio, air to biomass ratio, and the superficial velocity. The simulation model calculates the axial and radial profiles of product gases,
gasifier temperature and tar concentrations in the gasifier.
4. Model validation
The 2D hydrodynamic model presented in a previous paper
(Gungor, 2008a) has been used to predict the hydrodynamic behavior of CFB biomass gasifier. Firstly, hydrodynamic model simulation

Gerun et al. (2008)

CC1.6 H CH−0.5
2
10 8

− 9650
T

− 9650
T

Gerun et al. (2008)

3

(mol/m s)

CC1.3H CH−0.4 CH0.2O
2
6 6
2

CC0.5H O CO2
6 6

(mol/m s)
3

(mol/m s)

CC−0.1
C 1.85
6 H6 O2
CC0.5 H CO2
10 8

Morf et al. (2002)
3

3

(mol/m s)
3

(mol/m s)

Morf et al. (2002)
Jess (1996)
Smoot and Smith (1985)
Jess (1996)

performance is tested against four published data sets (Abdullah,

Husain, & Yin Pong, 2003; Andreux, Petit, Hemati, & Simonin, 2008;
Karmakar & Datta, 2010; Lee et al., 2010) with regard to the bed
pressure drop and the solid mass flux variation by the operational bed velocity, and the axial pressure drop profile and the
solid holdup along the bed height. Measurement conditions of the
experimental data are given in Table 6.
Secondly, developed 2D model of biomass gasification for CFB
is validated in this study. The comparison data are obtained from
a pilot scale CFB biomass gasifier, which were published in the literature (Li et al., 2004). To test and validate the model presented
in this paper, the same input variables in the tests are used as the
simulation program input in the comparisons.
Schematic diagram of pilot scale CFB biomass gasifier is shown in
Fig. 2. “The gasifier employs a riser of 6.5 m high and 0.10 m in diameter, a high-temperature cyclone for solids recycle and ceramic
fiber filter unit for gas cleaning. Air was supplied as the oxidant
and fluidizing agent after passing through a start-up burner near
the bottom of the riser. Hot gas leaving the burner and pre-heated
air were mixed to preheat the bed and, if needed, to maintain the
suspension temperature at the desired level. The temperatures of
both the primary and secondary air could be varied by adjusting
the total air supply and the fraction of each stream. The start-up
burner preheated the gasifier to 400–550 ◦ C before coal or biomass
fuel could be fed to the riser to further raise the temperature to
the desired level. The system was then switched to the gasification
model” (Li et al., 2004).
Feed particles underwent moisture evaporation, pyrolysis and
char gasification primarily in the riser. The fast fluidization flow
regime was maintained at the operating temperature, with a typical superficial velocity between 4 and 10 m/s, corresponding to
an air flow of 40–65 N m3 /h, and solids feed rate of 16–45 kg/h
for typical sawdust. The solids throughput was estimated to be
0.7–2.0 kg/m2 s. Coarser particles in the gas were captured by a
high-temperature cyclone immediately downstream of the riser.

The solids captured in the cyclone were recycled to the bottom of
the riser through an air-driven loop seal. Hot gas leaving the cyclone
at a temperature of 600–800 ◦ C was cooled by a two-stage waterjacketed heat exchanger and a single-stage air preheater before
entering the filter unit (Li et al., 2004).
Comparison data are obtained from gasification test results of
four sawdust species of whose ultimate analyses and other relevant
properties are given in Table 7. Each sawdust was dried before being
charged to the hoppers. Bed ash collected from a previous run was
used as the starting bed material for each new run, with silica sand
making up for loss of solids. In some runs, fly ash collected from the
outlet product stream was pneumatically re-injected into the bottom of the riser. The air used for re-injecting fly ash was included
when calculating the air ratio. The carbon content of the bed materials and re-injected fly ash was accounted for in the overall mass
and energy balance.


Wurzenberger et al. (2002)

Groppi, Tronconi, Forzatti, and Berg (2000)

Wurzenberger, Wallner, Raupenstrauch, and Khinast (2002)

Wurzenberger et al. (2002)

Morf et al. (2002)

Westbrook and Dryer (1984)

Mansaray et al. (1999)

Westbrook and Dryer (1984)

A. Gungor, U. Yildirim / Computers and Chemical Engineering 48 (2013) 234–250

Gerun et al. (2008) and Morf et al. (2002)

240

The operating pressure in the system was maintained at
∼1:05 bar, slightly higher than atmospheric. The air ratio, a defined
as the ratio of the actual air supply to the stoichiometric air
required for complete combustion, is one such measure. The tar
yield is expressed as the mass of tar per unit volume of raw gas,
in g/N m3 . The operating temperature was maintained in the range
700–850 ◦ C, while the sawdust feed rate varied from 16 to 45 kg/h.
It must be noted that, the CFB used in the experiments mentioned above is small-scale pilot unit. A more detailed description
of the experiment is given in the literature (Li et al., 2004). The
considered parameters and computation conditions are given in
Table 8.
Finally, a sensitivity analysis is carried out by using two published data sets from the literature (Li et al., 2004; Yin, Wu, Zheng,
& Chen, 2002). Schematic diagram of pilot scale CFB biomass gasifier which was used in Yin et al.’s (2002) experiments is shown in
Fig. 3. The proximate and ultimate analyses of biomass fuels used
in experiments are given in Table 7.

KEQ = 0.0265 exp

r = 2.196 × 1018 exp − 13,127
T
2H2 + O2 → 2H2 O

2

CO2 CH2 (mol/m3 s)

(mol/m3 s)
− 3968
T

CCO CH O− CCO CH
2
2
2
KEQ

r = 2.78 × 106 exp − 1510
T
CO + H2 O → CO2 + H2 (water-gas-shift)

4

2

CCO CO0.25 CH0.5O (mol/m3 s)
r = 3.98 × 1020 exp − 20,129
T
2CO + O2 → 2CO2

2
4

5

Homogeneous reaction

2

0.7 0.8
CCH
CO (mol/m3 s)

1.7 −0.8
CCH
CH (mol/m3 s)

r = 1.58 × 1019 exp − 24,343
T
CH4 + 2O2 → CO2 + 2H2 O

k3 = 1.53 × 10

k2 = 1.11 × 10 exp − 3548
Tp

k1 pH O
2

k1 = 4.93 × 103 exp − 18,522
Tp

C + CO2 → 2CO (Boudouard)

r = 3.3 × 1010 exp − 2.4×10
RT

25,161
Tp

exp

(1/s)

1+k2 pH O +k3 pH O
2
2
−9

r=
C + H2 O → CO + H2 (water-gas)

CCO2 (mol/m2 s)
r = 4364 exp

− 29,844
Tp

kg dp
Dg

Sh =

= 2ε + 0.69

Rep
ε

1/2

Sc 1/3

kcd =
kc =

(kg/s)
Ru T/Mc
(1/kcr )+(1/kcd )

r = d2 kc CO2 (mol/s)
CO +
2
˚

→ 2−
1
O
˚ 2

C+

Heterogeneous reaction

2

˚

− 1 CO2

Kinetic equations
Chemical reactions

Table 5
Heterogeneous and homogeneous reactions in the biomass gasifier.

CH4 + H2 O → CO + 3H2 (steam reforming)

(kg/m2 s kPa)
12·Sh·˚·Dg
dp ·Rg ·T

kcr = 8710 · exp

−1.4947×108
Ru ·T

(kg/m2 s kPa)

5. Results and discussion
As for the hydrodynamic aspect of results derived from this
study, the simulation results could be listed as follows. In this study,
the hydrodynamic model simulation results of the bed pressure
drop and the solid mass flux variation by the operational bed velocity, and the axial pressure drop profile and the solid holdup along
the bed height are tested against four published data sets (Abdullah
et al., 2003; Andreux et al., 2008; Karmakar & Datta, 2010; Lee et al.,

2010).
The axial solid holdup distribution along the riser obtained from
the hydrodynamic model simulation results for two different solid
circulation rates is presented in comparison with Lee et al.’s (2010)
experimental data in Fig. 4. It must be noted that, in the hydrodynamic model used in this study, for the axial profile of the solid
fraction along the upper zone, Zenz and Weil’s (1958) expression
which was further confirmed by Wein (1992) has been used as
given in Table 1. In that equation, the decay coefficient, ˛, which
is a parameter to express the exponential decrease of solid flux
or solid fraction with height is taken into account as described by
Chen and Xiaolong (2006). To calculate the cross-sectional average solids concentration, the relationship suggested Rhodes, Wang,
Cheng, and Hirama (1992) is used in the model as given in Table 1.
It is observed that the solid fraction is high at the bottom zone
and is low at the upper zone, due to the particle accumulation
in the bottom zone of the riser during operation, which is also
reported in the literature (Goo et al., 2008; Jiradilok et al., 2008;
Nguyen, Seo, Lima, Song, & Kim, 2012b). The solid holdup along the
riser is related to the stability of the solid circulation. An increase
in the solid circulation rate results in an increase of axial solid
holdup distribution along the riser, as seen in Fig. 4. Since the
gas flow is not sufficient to entrain all the solids entering into the
riser at a high solid circulation rate, the solid particles begin to
accumulate at the bottom zone of the riser which forms a dense
phase. The higher the solid circulation rate the larger the accumulation amount of the particles at the bottom zone of the riser. An
increase in the axial solid holdup along the riser is observed, as
the solid circulation rate increases (Lee et al., 2010). As the figure shows, the hydrodynamic model predicts reasonably well the
axial solid holdup distribution for two different solid circulation
rates.
Fig. 5 shows the predicted and experimental values of the axial
pressure drop for FCC particles for conditions of Table 6. Generally, the change in the pressure gradient with height in CFB riser is

small. In the riser, the pressure gradient is always negative because
the gas phase losses pressure head to accelerate and to suspend


A. Gungor, U. Yildirim / Computers and Chemical Engineering 48 (2013) 234–250

Fig. 1. Flow chart for the numerical solution of the CFB biomass gasifier model.

241


242

A. Gungor, U. Yildirim / Computers and Chemical Engineering 48 (2013) 234–250

Table 6
Measurement conditions of the experimental data referred to in this study.
Author(s)

Particle
type

Bed temperature T
(◦ C)

Bed
diameter D
(m)

Bed height

H (m)

Superficial
velocity U0
(m/s)

Particle
diameter dP
(␮m)

Particle
density
(kg/m3 )

Andreux et al. (2008)
Lee et al. (2010)
Abdullah et al. (2003)
Karmakar
and
Datta
(2010)

FCC
FCC
Rice husk
Sand I
Sand II
Sand III
Sand IV

25
25
25
25

0.110 × 0.110
0.009
0.060
0.050

9.00
1.90
0.12
5.95

7.00
2.20–3.90
0–1.02
4.43–4.45

70.0
82.4
1500.0
147.0
211.0
334.0
416.0

1400.0
2436.0

630.1
2650.0

Table 7
Ultimate analysis of biomass fuels.
Fuel type

Hemlock

Spruce–pine–fir mixture

Mixed pine bark–spruce

Mixed

Carbon (wt%)
Hydrogen (wt%)
Oxygen (wt%)
Nitrogen (wt%)
Sulphur (wt%)
Ash (wt%)
Moisture content (wt%)
Higher heating value (MJ/kg)
Stoichiometric air (N m3 /kg)
Dry bulk density (kg/m3 )
Mean particle diameter (mm)

51.80
6.20
40.60

0.60
0.38
0.40
8.80–15.00
20.30
5.36
128.00
0.92

50.40
6.25
41.60
0.62
0.34
0.70
10.00
19.80
5.20
119.00
0.82

49.10
7.26
39.50
0.25
0.50
3.34
10.10
21.10
5.46

347.00
0.38

48.90
7.86
40.30
0.21
0.07
2.69
4.20–6.70
21.70
5.56
465.00
0.43

the particles. The absolute values of the pressure gradient decrease
steadily with increasing distance from the riser entrance and then
gradually approach a constant value as clearly shown in Fig. 5. In the
model, calculation of total pressure drop also considers the pressure drop due to distributor plate at the primary gas entrance in the
bottom zone. The basic assumption is that the hydrostatic head of
solids contributes to the axial pressure drop. The suspension density is related to the pressure drop through the axial distance which
also shows coherence with the above figure. The high pressure drop
at the bottom zone is due to the effect of solid feeding in that zone
as clearly seen from Fig. 5. The pressure drop then decreases along
the height of the riser due to the decrease in solid concentration.
The model results are in fair agreement with experimental data of

Fig. 5. Similar results are also observed in the studies of Nguyen,
Ngo, et al. (2012) and Karmakar and Datta (2010).
In the CFB gasifier, the solid circulation of the hot bed-materials

plays a critical role, since the heat carried by the solid material
heated from the combustor is supplied to the gasification endothermic reactions. For the given system, an increase in the solid
circulation rate will reduce difference of temperatures between
the gasification and combustion zones. On the other hand, a higher
solid flux between the riser and gasifier can convey more unreacted char from the gasification to combustion zone which reduces
the required amount of additional fuel (Kaiser, Löffler, Bosch, &
Hofbauer, 2003). The solid circulation rate is affected by several
operating parameters such as gas velocities to the loop-seal, the

Fig. 2. Schematic diagram of the CFB gasifier experimental setup (Li et al., 2004).


A. Gungor, U. Yildirim / Computers and Chemical Engineering 48 (2013) 234–250

243

Fig. 3. Schematic of 1 MW rice husk gasification and power generation system (Yin et al., 2002).

9
8

U =3.06 m /s

1.6

Experiment (G =30.96 kg/m s)

7

Experiment (G =46.62 kg/m s)

1.2

Model

0.8

0.4

0.0
0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

Solid holdup (-)
Fig. 4. Comparison of model solid holdup predictions with Lee et al.’s (2010) experimental data for different solid circulation flux values.

riser, and the gasifier. However, it is controlled mainly by the

gas velocities in the riser (Goo et al., 2008). The effect of the bed
operational velocity on the solid mass flux is presented in Fig. 6
which also plots Karmakar and Datta’s (2010) experimental results.
The measurement conditions of experimental data used for the
comparison are shown in Table 6. The gas introduced into the gasifier provides momentum to the upward transportation of the solid
particles. So, an increase in bed operational velocity leads to the
increase of the solid flux across the gasifier which as a result leads
to an increase in the solid circulation rate. As the figures display,
numerical results are in good agreement with experiments, both

Height above distributor (m)

Height above distributor (m)

2.0

6

Experiment
Model

5
4
3
2
1
0
0

2000

4000

6000

8000

Pressure drop (Pa/m)
Fig. 5. Comparison of model simulation results with Andreux et al.’s (2008) experimental data.

in form and magnitude where the maximum error values do not
exceed 0.06. The hydrodynamic behavior is also confirmed in an
experimental study reported by Goo et al. (2008) and Nguyen, Seo,
et al. (2012).

Table 8
Operating parameters of the experimental data referred to in this study.
Run number
Sawdust species

Run 1
Hemlock

Run 2
Mixed pine
bark–spruce

Run 3
Hemlock

Run 4
Spruce–pine–fir
mixture

Run 5
Mixed

Sawdust consumption (kg)
Moisture content (%)
Total air supplied (N m3 )
Total steam injection (kg)
Mean temperature (◦ C)
Primary air pressure (bar)
Air ratio

120.800
14.700
186.000
0
789.000
1.190
0.337

117.300
10.100
125.000
0
701.000
1.190
0.218

88.100
11.700
142.000
0
718.000
1.190
0.340

80.700
10.500
177.000
0
766.000
1.190
0.402

55.200
4.200
135.000
0
805.000
1.190
0.460


244

A. Gungor, U. Yildirim / Computers and Chemical Engineering 48 (2013) 234–250

14
Sand I
Sand II

Solid mass flux (kg/m2 s)

12

Sand III
Sand IV

10

Model

8
6
4
2
0
1

2

3

4

5

6

7

Operational bed velocity (m/s)
Fig. 6. Comparison of model simulation results with Karmakar and Datta’s (2010)
experimental data with regard to the operational bed velocity for different sizes of
particles.

Fig. 7 shows the bed pressure drop versus superficial velocity
for a bed height of 120 mm for rice husk which is compared with
Abdullah et al.’s (2003) experimental data for conditions of Table 6.
An increase in bed operational velocity leads to the increase of the
solid flux across the gasifier and in turn leads to an increase in
the solid circulation rate as mentioned above. The basic assumption is that the hydrostatic head of solids contributes to the axial
pressure drop. The suspension density is related to the pressure
drop through the axial distance. Thus as the bed operational velocity increases, the pressure drop increases as shown in Fig. 7. As
the figures display, numerical results are in good agreement with
experiments, both in form and magnitude where the maximum
error values do not exceed 0.09.
To test and validate the model presented in this paper, the radial
and axial profiles of the bed temperature and H2 ; CO; CO2 and CH4
volumetric fractions and tar concentration versus gasifier temperature are compared using the same input variables in the tests as
the simulation program input. Detailed listing of the model input
variables are given in Table 8. All species are dry-gas molar contents.
Temperature is crucial for the overall biomass gasification process. The gasification temperature not only affects the product yield
but also governs the process energy input. High gasification temperature (800–850 ◦ C) produces a gas mixture rich in H2 and CO
with small amounts of CH4 and higher hydrocarbons but do not
1000
900

Pressure drop (Pa)

800
Experiment

700

Model

600
500
400
300
200
100
0
0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Operational bed velocity (m/s)
Fig. 7. Comparison of model pressure drop predictions versus operational bed
velocity for a bed height of 120 mm for rice husk with Abdullah et al.’s (2003)
experimental data.

always favor gas heating value. Too high a temperature lowered
gas heating value.
According to the study of Mahishi and Goswami (2007), at low
temperatures, solid carbon and CH4 are present in the product gas.
In actual gasifiers solid carbon is carried away to the catalytic bed
and is deposited on the active catalyst sites thereby de-activating
the catalyst. It is necessary to ensure that the product gas is free
of any solid carbon. As temperature increases, both carbon and
methane are reformed. At about 1000 K both are reduced to very
small amounts (≤0.04 mol) and in the process get converted into
CO and H2 . This explains the increase in hydrogen moles from 900
to 1030 K. At about 1030 K, the H2 yield reaches a maximum value
of about 1.33 mol. At still higher temperatures, the H2 yield starts
reducing. This is attributed to the water-gas shift (WGS) reaction.
According to Le-Chatelier’s principle, high temperature favors
reactants in an exothermic reaction thus explaining the increase

in CO and reduction in H2 (and CO2 yield) at higher temperature.
Hence, gasification temperature of about 1030 K gives the highest equilibrium hydrogen yield with negligible solid carbon in the
product gas.
Fig. 8 presents the comparison of axial and radial temperature profiles at different operational conditions and also shows the
model predictions and Li et al.’s (2004) experimental data. The axial
profile of suspension temperature at constant r/R = 1 is shown in
Fig. 8(a). The temperature difference across most of the riser height
was less than 100 ◦ C, consistent with normal CFB reactors. The measured temperature at the bottom of the riser was 600–700 ◦ C for all
test runs. The coarser particles settled at the bottom and cooled
there. However, intense solids recycle minimized the temperature
gradient. Sadaka, Ghaly, and Sabbah (2002) also reported similar
results. The model predicted the average bed temperature under
the various operating conditions with a high accuracy as shown in
Fig. 8 where the maximum error values do not exceed 0.08.
The transport and thermodynamic properties of the gas and
its mole fraction vary from one place to another. Therefore, the
advantage of dealing with the hydrodynamic, transport and thermodynamic properties is that the gas mole fractions can be
predicted at a fairly good accuracy without the assumption that
they are constant throughout the bed. Although most of the conversion takes place in the bottom zone, it is known that char conversion
continues in the upper zone as well. Fig. 8(b) indicates that there
could be as much as a 45 ◦ C difference between the core and wall
region of the riser. Later measurements from Run 2 from the opposite side, with the thermocouple tip withdrawn 2 mm from the
wall, showed improved symmetry and less than a 15 ◦ C center-towall temperature difference. This temperature uniformity indicates
extensive radial mixing and radial heat transfer in the riser, facilitating both homogeneous and heterogeneous reactions. As it is seen
from the figure, a model is presented which satisfactorily predicts
the axial and radial temperature profiles and gives good agreement
with experimental data.
The gas mole fraction profiles along the reactor axis are shown
in Fig. 9. The lower part of the riser mainly provided pyrolysis of
returning particles and evaporation of moisture from fresh particles. For ER = 0.38, a major rise in CO2 content was observed

over the 0.9–2.0 m height interval where the partial oxidation of
pyrolysis products resulted in a simultaneous decrease in the concentrations of CO and other combustible species. Gasification of
char continued along the remainder of the riser, raising the CO and
H2 contents again. Although a cross-over of CO and CO2 contents
occurred for ER = 0.38, this crossover was not repeated for higher
air ratio (ER = 0.46). The concentration of CH4 never approached its
equilibrium level due to the limited gas residence time in the riser.
As shown in these figures, at the bottom of the gasifier, the
concentration of CO2 is lower due to the existence of a large number of solid carbon, while it increases along the height of the


A. Gungor, U. Yildirim / Computers and Chemical Engineering 48 (2013) 234–250

245

(b) 1000
(a)

900
1000

900

700

800

600

Temperature (ºC)

Temperarure (ºC)

800

700

600

500
1000

0.2

0.4

0.6

0.8

1

0.6

0.8

1

900
800

500

700
400
0

1

2

3

4

5

6

7

Height above primary air inlet (m)

600

Experiment
Model

500
400

0

0.2

0.4

Radial position r/R (-)
Fig. 8. Comparison of model simulation results with Li et al.’s (2004) experimental data for axial and radial temperature profiles (axial location: 5 m for radial temperature
profiles).

gasifier with the decrease of solid carbon and the volatile combustion. The amount of CH4 is higher in the bottom zone of the
gasifier as a result of devolatilization and it does not change in the
higher region due to the exhaustion of O2 . It must be noted that usually, when O2 concentration is high, solid carbon–O2 combustion
reaction is dominant. Thus, only solid carbon–O2 heterogeneous
reaction is considered for combustor. But solid carbon–H2 O and
solid carbon–CO2 reactions are also important in the biomass gasification process, especially after the exhaustion of O2 (Wang &
Yan, 2008). High concentration of CO in the bottom zone can be
explained as the large heat of solid carbon–O2 combustion reaction is released. Both solid carbon–H2 O and solid carbon–CO2 are
endothermic reactions. Therefore, rates of these two reactions are
higher in the low region. For the upper zone of the gasifier, it can
be seen that H2 concentration increased with temperature and the
content of CH4 showed an opposite trend as Fig. 9(a) and (d) clearly
shows. According to Le Chatelier’s principle, higher temperatures
favor the reactants in exothermic reactions and favor the products
in endothermic reactions. Therefore steam reforming the endothermic reaction is strengthened with increasing temperature, which
resulted in an increase of H2 concentration and a decrease of CH4
concentration as was also observed by Lv et al. (2004) and Kaushal,
Abedi, and Mahinpey (2010). The reason to have small variation in
methane percentage could possibly be the main source of methane

in the product gas is via devolatilization (Kaushal et al., 2010). The
content of CO is mainly determined by Boudouard reaction and it
is an exothermic reaction. Higher temperature is not favorable for
CO production, so the content of CO decreased with temperature.
Lv et al. (2004) also reported similar results. Fig. 9(a) shows better
agreement between simulation prediction and experimental data
for hydrogen production in the temperatures of 750–805 ◦ C. Simulation results for carbon monoxide and carbon dioxide in Fig. 9(b)
and (c) display good qualitative prediction of experimental data in
the whole range. Also, simulation results in Fig. 9(d) show good

accuracy for methane production. It can be seen that the minimum
error of comparisons is about 1% and the maximum error is less
than 17%. This implies that the present 2D numerical simulation is
reasonable and the validity of the present model is verified.
Two serial effects then limit the gasification speed: chemical
kinetics and mass transfer. On the whole, it can be concluded that
only the initial gasification reactions steps are substantially influenced by the mass transfer effects, while at the top of the reactor
only the chemical kinetic plays a dominant role on the overall
reaction mechanisms (Fiaschi & Michelini, 2001). The gas concentrations in a CFB combustor depend on the radial position. Radial
dispersion inside the reactor helps to see wall effects on the hydrodynamics of the fluidized bed reactor. In a CFB operating in the fast
fluidization flow regime, particles tend to migrate outwards toward
the wall, driven by fluid–particle interactions and boundary effects,
and descend along the wall, while dilute upflow is maintained in the
inner core (Brereton, Grace, & Yu, 1988). As a result of the higher
concentration of particles in the wall region, there is a reducing
region there, with augmented CH4 , H2 and CO concentrations as
shown in Fig. 10. Due to the lack of O2 , the concentration of CO
is high in the particle laden wall region. Because of WGS reaction,
the H2 concentration in this region is also quite high. Another reason of the high concentration of CO along the wall region is the
dominant effect of Boudouard and water-gas reactions in the high

density solid particles in the wall. High concentration of H2 in the
wall region mainly results from water-gas reaction. Because of high
devolatilization rate of biomass particles in the wall region, CH4
concentration is high. In the center of the bed, because of elevated
O2 concentration, CO is converted into CO2 and thus results in the
CH4 combustion. Radially, due to the WGS reaction, as CO concentration shows a sharp decrease, the CO2 concentration displays an
increase. However, CH4 and H2 show a decreasing trend. At bed
height of 5.9 m, bed temperature profile shows that there is adequate amount of O2 which results in CH4 and H2 combustion. Thus,


246

A. Gungor, U. Yildirim / Computers and Chemical Engineering 48 (2013) 234–250

(c)

20

Hydrogen composition (%)

16
12

Experiment
Model

8
4
20

1

2

3

4

5

6

16
12
8

Carbon dioxide composition (%)

(a)

40
30
Experiment
Model

20
10
40

1

0

4

5

6

2

3

4

5

6

30
20

1

2

3

4

5

0

6

1

Height above distributor (m)

Height above distributor (m)

(d)

40

20
16

30
Experiment
Model

20
10
40

1

2

3

4

5

6

30
20

Methane composition (%)

Carbon dioxide composition (%)

3

10

4

(b)

2

12

Experiment
Model

8
4
20
1

2

3

4

5

6

2

3

4

5

6

16
12
8

10

4
0

1

2

3

4

5

6

Height above distributor (m)

0

1

Height above distributor (m)

Fig. 9. Comparison of model simulation results with Li et al.’s (2004) experimental data for axial gas compositions.

the amount of CH4 and H2 is significantly low in this region as they
get closer to the bed center. As shown in Fig. 10, the results show
a good agreement of these calculated radial gas phase components

with experiments. It can be observed that the calculation errors of
H2 , CO2 and CH4 molar fraction are less than 1% and CO2 results are
within the 15% range.
Although biomass can be quite easily converted to hydrogen or
syngas, the process is problematic because of tar formation. Tar is
defined, according to the International Energy Agency’s tar protocol, as organic components/contaminants with molecular weight
greater than benzene. Tar deposits are an economical bottleneck
to gasification, as they induce frequent equipment shutdowns for
maintenance and repair, sometimes even the need for duplicate
equipments for gas cleaning, to avoid complete process shutdowns.
Tar concentrations can vary widely according to gasifier types and
operating conditions. For fluidized bed gasifiers they can range from
10 to 50,000 mg/N m3 (Morf, Haslerb, & Nussbaumer, 2002). Tar
can be physically removed, or chemically converted into lighter
gas species by thermal or catalytic reactions.
Fig. 11 shows the simulation results compared with experimental data for tar concentrations versus gasifier temperatures in the
range of 700–900 ◦ C. The model predicted the tar concentrations
with lower but reasonable accuracy (the maximum error is less than

25%) as shown in Fig. 11. Modeling tar formation generally requires
experimental measurements, which are difficult to retrieve, due
to the complexity and absence of unified standards for sampling
methods (Milne, Evans, & Abatzoglou, 1998). It must be noted that
this was also verified from the actual experimental data where long
residence times and high temperatures drastically reduced the tars
in the product stream (Brown, Baker, & Mudge, 1986). This effect
is clearly seen in the figure which proves the model validity and
this agrees with experimental results of the TPS pilot-plant (CFB
biomass gasifier) Lundberg, Morris, and Rensfelt (1997) and Liu and
Gibbs (2003) modeling study. It must be noted that there are no unified parameters for each heterogeneous reaction in the literature.

Although the closest operation condition is taken into account to
select them, errors cannot be avoided. It must be noted that the
kinetic parameters for the primary pyrolysis model are from the
other literature. Because the biomass has a high volatile content
which may have a significant impact in the simulation, the compositions of volatile species after devolatilization should be analyzed
for the specified fuels and heating conditions. And the product tar of
the primary pyrolysis takes part in the following secondary pyrolysis or tar cracking. It may lead to the inaccuracy of tar concentration.
It can be seen that the minimum error of comparisons is about 8.7%
and the maximum error is less than 25%.


A. Gungor, U. Yildirim / Computers and Chemical Engineering 48 (2013) 234–250

(c)

6

Carbon dioxide composition (%)

Hydrogen composition (%)

(a)

4

2
Experiment
Model

0

20

16

12

8
Experiment
Model

4

0

0

0.2

0.4

0.6

0.8

0

1

0.2

Radial position r/R (-)

(d)

20

16

12

8
Experiment
Model

4

0.4

0.6

0.8

1

Radial position r/R (-)

Methane composition (%)

Carbon monoxide composition (%)

(b)

247

0

5

4

3

2
Experiment

1

Model

0

0

0.2

0.4

0.6

0.8

1

0

0.2

Radial position r/R (-)

0.4

0.6

0.8

1

Radial position r/R (-)

Fig. 10. Comparison of model simulation results with Li et al.’s (2004) experimental data for radial gas compositions.

and Zabaniotou (2008) also studied the effect of ER variation as one
of the most important operation parameters on the quality of the
producer gas. The model predictions about the influence of ER on
syngas composition at the gasifier exit are shown in Fig. 12 which
also plots the experimental results of Li et al. (2004). ER is varied
from 0.19 to 0.27 through changing the air flow rate and keeping
20

16

Tar yield (g/Nm3)

In order to provide the most accurate data for the optimization
of the gasification process, carrying out a sensitivity analysis is also
very crucial. In this study, the effects of the operational parameters such as gasifier temperature and equivalence ratio on syngas
composition of an atmospheric biomass CFB gasifier has been investigated and has been also validated with published experimental
data in the literature (Li et al., 2004; Yin et al., 2002) for sensitivity analysis. It must be noted that all species are dry-gas molar
contents.
Equivalence ratio is defined as the ratio of the amount of air
actually supplied to the gasifier and the stoichiometric amount
of air. High degree of combustion occurs at high ER which supplies more air into the gasifier and improves char burning to
produce CO2 instead of combustible gases such as CO, H2 , CH4
and Cn Hm . In biomass gasification, the ER varies from 0.10 to 0.30
(Morf et al., 2002). Studies have shown that too small ER is also
unfavorable for biomass gasification as it lowers the reaction temperature (Mansaray, Ghaly, Al-Taweel, Hamdullahpur, & Ugursal,
1999). Narvaez, Orio, Aznar, and Corella’s (1996) study showed that
ER was varied in the range of 0.25–0.45 to find the optimum ER. It
was observed that increasing the ER reduced the amount of H2 , CO,
CH4 and Cn Hm . Maximum H2 concentration of 10% was obtained at
ER of 0.26. It was also concluded that the gas yield was in a direct
relationship with ER. Similar trends were obtained by Li et al. (2004)
who investigated the co-gasification of biomass and coal while the
ER was in the range of 0.31–0.47. Skoulou, Koufodimos, Samaras,

Experiment
Model

12

8

4

0
650

700

750

800

850

Temperature (oC)
Fig. 11. Comparison of model simulation results with Li et al.’s (2004) experimental
data for tar concentrations versus gasifier temperatures.


248

A. Gungor, U. Yildirim / Computers and Chemical Engineering 48 (2013) 234–250

(c)
20

20

Carbon dioxide
composition (%)

Hydrogen composition (%)

(a)
16
Experiment
Model

12
8
4
0
0.2

0.3

0.4

0.5

16
12
8
Experiment
Model

4
0

0.2

0.6

0.3

0.4

0.6

(d)
30

20

10
Experiment
Model

0
0.2

0.3

0.4

0.5

0.6

Methane composition (%)

(b)
Carbon monoxide
composition (%)

0.5

ER (-)

ER (-)

20
16
Experiment

12

Model

8
4
0
0.2

0.3

0.4

0.5

0.6

ER (-)

ER (-)

Fig. 12. Effects of equivalence ratio on syngas composition at gasifier output (a) hydrogen composition, (b) carbon monoxide composition, (c) carbon dioxide composition,
and (d) methane composition. (Model simulation results are compared with Li et al.’s (2004) experimental data.)

the other conditions constant. As more oxygen (high ER) is supplied it is observed that the H2 and CO yields reduce and that of
CO2 increases. This is due to the oxidation of H2 and CO to H2 O
and CO2 . At low values of ER, small amounts of C(s) and CH4 are
formed in the gasifier, both of which get oxidized as more air is
supplied. The model results of the final gas composition show a

similar tendency to the experimental data, as seen in Fig. 12. Similar
results are also observed in the studies of Mahishi and Goswami
(2007).
Bed temperature is a key operation parameter which affects
both the heating value and producer gas composition. At very
low temperature (400 ◦ C) the carbon present in the biomass is

(a)

(c)
Carbon dioxide composition (%)

Hydrogen composition (%)

20

16
Experiment
Model

12

8

4

0
700

750

800

20

16

12

Model

4

0

700

850

Experiment

8

750

Temperature (ºC)

(b)

850

800

850

(d)
20

20

Methane composition (%)

Carbon monoxide composition (%)

800

Temperature (ºC)

16

12
Experiment
Model

8

4

0
700

750

800

Temperature (ºC)

850

16
Experiment
Model

12

8

4

0
700

750

Temperature (ºC)

Fig. 13. Effects of gasifier temperature on syngas composition at gasifier output (a) hydrogen composition, (b) carbon monoxide composition, (c) carbon dioxide composition,
and (d) methane composition. (Model simulation results are compared with Yin et al.’s (2002) experimental data.)


A. Gungor, U. Yildirim / Computers and Chemical Engineering 48 (2013) 234–250

not utilized completely so the production of syngas is not good
but with increasing temperature more carbon is oxidized and the
rate of conversion increases. At low temperatures both unburnt
carbon and methane are present in the syngas but as the temperature increases carbon is converted into carbon monoxide
in accordance with Boudouard reaction. The Boudouard reaction
is endothermic; therefore as the temperature rises, so does the
amount of carbon dioxide reacted with char to produce carbon
monoxide. The methanation reaction is exothermic, which means
as temperature increase the production of CH4 decreases, which
in turn leaves more H2 in the gas. This results in increasing the
operating temperature of the gasifier that favors the production
of hydrogen and carbon monoxide. The CH4 is reduced by the
steam-methane reforming reaction. This reaction is endothermic

meaning the forward reaction is favored as temperature increases.
Hence, CH4 decreases while H2 and CO increase. The same tendency is observed in the study of Doherty, Reynolds, and Kennedy
(2009). This is in accordance with gasifier chemistry. According to
Boudouard reaction as the gasifier temperature increases the mole
fraction of carbon monoxide increases and that of carbon dioxide decreases. The water-gas reaction is endothermic; water gas
reaction suggests that high temperature increases the production
of both carbon monoxide and hydrogen. According to methanation reaction the mole fraction of methane in syngas decreases
and that of hydrogen increases with the increase in temperature.
Fig. 13 shows the effects of gasifier temperature on (a) hydrogen
composition, (b) carbon monoxide composition, (c) carbon dioxide composition and (d) methane composition of the final dry
gas product. The model results of the final gas composition show
a similar tendency to the experimental data, as seen in Fig. 13.
The maximum error is observed in methane composition which
is about 0.18. Similar results are also observed in the studies of
Ramzan et al. (2011), Mahishi and Goswami (2007) and Shen et al.
(2008).

6. Conclusions
The primary goal of this study has been to improve previous
biomass gasification kinetics and hydrodynamic models for CFB
biomass gasifiers. A two dimensional model for an atmospheric
CFB biomass gasifier has been developed which uses the particle based approach and integrates and simultaneously predicts
the hydrodynamic and gasification aspects. Tar conversion (tar
formation and thermal tar cracking) is taken into account in the
model. Phenol is used to represent the tar from primary pyrolysis. The model calculates the axial and radial distribution of syngas
mole fraction and temperature both for bottom and upper zones.
The proposed model addresses both hydrodynamic parameters
and reaction kinetic modeling. The validation with experimental data for different operational conditions from the literature
has been carried out, showing, on the whole, quite encouraging
results. The minimum error of comparisons is about 1% and the

maximum error is less than 25%. This implies that the present 2D
numerical simulation is reasonable and the validity of the present
model is verified. In this way, the whole model could be regarded
as a start to be supported and improved by especially dedicated
test campaigns. However, to improve the simulation results, some
modifications should be considered. The model predicted the tar
concentrations with lower but reasonable accuracy (the maximum error is less than 25%). Detailed experimental data about
the influence of operating conditions on the formation of tar along
with the kinetics studies is needed to obtain a thorough evaluation.
The effects of the operational parameters such as gasifier
temperature and ER on syngas composition of an atmospheric

249

biomass CFB gasifier has been investigated and has been also
validated with published experimental data in the literature for
sensitivity analysis.
Acknowledgments
Financial support from the Scientific and Technical Research
Council of Turkey (TUBITAK 109M167) is sincerely acknowledged.
The author also expresses their thanks to Canan Gungor for contribution to this work.
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