Two dimensional numerical computation of a circulating fluidized bed biomass gasifier

Computers and Chemical Engineering 48 (2013) 234–250 Contents lists available at SciVerse ScienceDirect 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 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 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 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: afsingungor@hotmail.com (A Gungor) 0098-1354/$ – see front matter © 2012 Elsevier Ltd All rights reserved http://dx.doi.org/10.1016/j.compchemeng.2012.09.012 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 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 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 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 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 The conservation of mass and momentum equations and the constitutive relations used in hydrodynamic model are given in Table 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 +ˇ 16 Reg 0.0791 Reg0.25 r Rb 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 (Ji et al., 2009) In the last step, the char (char = − 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 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) G(ε) = + ∂∂zv ∂v ∂r + ∂z ∂v ∂z + ∂v ∂r εi + ∂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 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 ) C D ε 2.65 dp i ˇ= ∂u ∂r + ∂u ∂z = rz zr = − ∂u ∂z zz =2 − ∂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= P Ru T εi 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 The conservation of mass and momentum equations for each phase and the constitutive relations + ∂u ∂z + ∂u ∂r + CD = 0.44 Rep ≥ 1000 + ∂∂rv ∂v ∂z (1 + 0.15Rep 0.687 ) Rep < 1000 = zr 24 Rep = rz − ∂v ∂z =2 zz CD = + ∂∂rv ∂v ∂z + ∂∂zv ∂v ∂r − ∂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 Kinetic parameters in Eq (1) + gεp,i (j = Biomass particles, Bed material) Solids stress modulus 238 dp = (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 × 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 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 r2 = 10 exp CC6 H6 O (mol/m3 s) − 10 RT CC6 H6 O (mol/m3 s) 14 r3 = 1.7 × 10 16 r4 = 2.0 × 10 exp − 3.5×10 RT exp − 4.43×10 RT r5 = 0.655 × 10 T exp 11 r6 = 2.4 × 10 exp − 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)) 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 × 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 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 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 10 − 9650 T − 9650 T Gerun et al (2008) (mol/m s) CC1.3H CH−0.4 CH0.2O 6 CC0.5H O CO2 6 (mol/m s) (mol/m s) CC−0.1 C 1.85 H6 O2 CC0.5 H CO2 10 Morf et al (2002) 3 (mol/m s) (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 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 “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 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 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 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 The proximate and ultimate analyses of biomass fuels used in experiments are given in Table KEQ = 0.0265 exp r = 2.196 × 1018 exp − 13,127 T 2H2 + O2 → 2H2 O CO2 CH2 (mol/m3 s) (mol/m3 s) − 3968 T CCO CH O− CCO CH 2 KEQ r = 2.78 × 106 exp − 1510 T CO + H2 O → CO2 + H2 (water-gas-shift) CCO CO0.25 CH0.5O (mol/m3 s) r = 3.98 × 1020 exp − 20,129 T 2CO + O2 → 2CO2 Homogeneous reaction 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 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 −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− O ˚ C+ Heterogeneous reaction ˚ − CO2 Kinetic equations Chemical reactions Table 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) 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 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 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 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 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 shows the predicted and experimental values of the axial pressure drop for FCC particles for conditions of Table 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 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 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 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 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 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 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 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 Schematic of MW rice husk gasification and power generation system (Yin et al., 2002) U =3.06 m /s 1.6 Experiment (G =30.96 kg/m s) 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 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 which also plots Karmakar and Datta’s (2010) experimental results The measurement conditions of experimental data used for the comparison are shown in Table 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 Experiment Model 0 2000 4000 6000 8000 Pressure drop (Pa/m) Fig Comparison of model simulation results with Andreux et al.’s (2008) experimental data in form and magnitude where the maximum error values 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 Operating parameters of the experimental data referred to in this study Run number Sawdust species Run Hemlock Run Mixed pine bark–spruce Run Hemlock Run Spruce–pine–fir mixture Run 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 14.700 186.000 789.000 1.190 0.337 117.300 10.100 125.000 701.000 1.190 0.218 88.100 11.700 142.000 718.000 1.190 0.340 80.700 10.500 177.000 766.000 1.190 0.402 55.200 4.200 135.000 805.000 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 2 Operational bed velocity (m/s) Fig 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 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 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 As the figures display, numerical results are in good agreement with experiments, both in form and magnitude where the maximum error values 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 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 not 1000 900 Pressure drop (Pa) 800 Experiment 700 Model 600 500 400 300 200 100 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 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 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 = 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 where the maximum error values 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 from the opposite side, with the thermocouple tip withdrawn 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 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 0.6 0.8 900 800 500 700 400 Height above primary air inlet (m) 600 Experiment Model 500 400 0.2 0.4 Radial position r/R (-) Fig Comparison of model simulation results with Li et al.’s (2004) experimental data for axial and radial temperature profiles (axial location: 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 20 16 12 Carbon dioxide composition (%) (a) 40 30 Experiment Model 20 10 40 6 30 20 Height above distributor (m) Height above distributor (m) (d) 40 20 16 30 Experiment Model 20 10 40 30 20 Methane composition (%) Carbon dioxide composition (%) 10 (b) 12 Experiment Model 20 6 16 12 10 4 Height above distributor (m) Height above distributor (m) Fig 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) Carbon dioxide composition (%) Hydrogen composition (%) (a) Experiment Model 20 16 12 Experiment Model 0 0.2 0.4 0.6 0.8 0.2 Radial position r/R (-) (d) 20 16 12 Experiment Model 0.4 0.6 0.8 Radial position r/R (-) Methane composition (%) Carbon monoxide composition (%) (b) 247 Experiment Model 0 0.2 0.4 0.6 0.8 0.2 Radial position r/R (-) 0.4 0.6 0.8 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 12 650 700 750 800 850 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 0.2 0.3 0.4 0.5 16 12 Experiment Model 0.2 0.6 0.3 0.4 0.6 (d) 30 20 10 Experiment Model 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 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 12 700 750 800 20 16 12 Model 700 850 Experiment 750 Temperature (ºC) (b) 850 800 850 (d) 20 20 Methane composition (%) Carbon monoxide composition (%) 800 Temperature (ºC) 16 12 Experiment Model 700 750 800 Temperature (ºC) 850 16 Experiment Model 12 700 750 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) 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 References Abdullah, M Z., Husain, Z., & Yin Pong, S L (2003) Analysis of cold flow fluidization test results for various biomass fuels Biomass and Bioenergy, 24, 487–494 Alauddin, Z A B Z., Lahijani, P., Mohammadi, M., & Mohamed, A R (2010) Gasification of lignocellulosic biomass in fluidized beds for renewable energy development: A review Renewable and Sustainable Energy Reviews, 14, 2852–2862 Andreux, R., Petit, G., Hemati, M., & Simonin, O (2008) Hydrodynamic and solid residence time distribution in a circulating fluidized bed: Experimental and 3D computational study Chemical Engineering and Processing, 47, 463–473 Arnavat, M., Bruno, J C., & Coronas, A (2010) Review and analysis of biomass gasification models Renewable and Sustainable Energy Reviews, 14, 2841–2851 Brereton, C M H., Grace, J R., & Yu, J (1988) Axial gas mixing in a circulating fluidized bed In P Basu, & J F Large (Eds.), Circulating fluidized bed technology II (pp 307–314) New York: Pergamon Press Brown, D., Fuchino, T., & Maréchal, F (2006) Solid fuel decomposition modelling for the design of biomass gasification systems Computer Aided Chemical Engineering, 21, 1661–1666 Brown, D., Gassner, M., Fuchino, T., & Marechal, F (2009) Thermo-economic analysis for the optimal conceptual design of biomass gasification energy conversion systems Applied Thermal Engineering, 29, 2137–2152 Brown, M D., Baker, E G., & Mudge, L K (1986) Deactivation of catalysts in biomass gasification PNL-SA-13931, Pacific Northwest Laboratory Cao, Y., Wang, Y., Riley, J T., & Pan, W P (2006) A novel biomass air gasification process for producing tar-free higher heating value fuel gas Fuel Processing Technology, 87, 343–353 Chaiprasert, P., & Vitidsant, T (2009) Promotion of coconut shell gasification by steam reforming on nickeledolomite American Journal of Applied Sciences, 6, 332–336 Chen, Y., & Xiaolong, G (2006) Dynamic modeling and simulation of a 410 t/h pyroflow CFB boiler Computers and Chemical Engineering, 31, 21–31 Corella, J., & Sanz, A (2005) Modeling circulating fluidized bed biomass gasifiers A pseudo-rigorous model for stationary state Fuel Processing Technology, 86, 1021–1053 Doherty, W., Reynolds, A., & Kennedy, D (2009) The effect of air preheating in a biomass CFB gasifier using ASPEN Plus simulation Biomass and Bioenergy, 33, 1158–1167 Fiaschi, D., & Michelini, M (2001) A two-phase one-dimensional biomass gasification kinetics model Biomass and Bioenergy, 21, 121–132 Gerun, L., Paraschiv, M., Vijeu, R., Bellettre, J., Tazerout, M., Gobel, B., et al (2008) Numerical investigation of the partial oxidation in a two-stage downdraft gasifier Fuel, 87, 1383–1393 Gomez-Barea, A., & Leckner, B (2010) Modeling of biomass gasification in fluidized bed Progress in Energy and Combustion Science, 36, 444–509 Goo, J H., Seo, M W., Park, D K., Kim, S D., Lee, S H., Lee, J G., et al (2008) Hydrodynamic properties in a cold-model dual fluidized-bed gasifier Journal of Chemical Engineering of Japan, 41, 686–690 Groppi, G., Tronconi, E., Forzatti, P., & Berg, M (2000) Mathematical modeling of catalytic combustors fuelled by gasified biomasses Catalysis Today, 59, 151–162 Gungor, A (2008a) Predicting axial pressure profile of a CFB Chemical Engineering Journal, 140, 448–456 Gungor, A (2008b) Two-dimensional biomass combustion modeling of CFB Fuel, 87, 1453–1468 Gungor, A., & Eskin, N (2007) Hydrodynamic modelling of a circulating fluidized bed Powder Technology, 172, 1–13 Huang, Y., Turton, R., Park, J., Famouri, P., & Boyle, E J (2006) Dynamic model of the riser in circulating fluidized bed Powder Technology, 163, 23–31 Huilin, L., Yurong, H., & Gidaspow, D (2003) Hydrodynamic modelling of binary mixture in a gas bubbling fluidized bed using the kinetic theory of granular flow Chemical Engineering Science, 58, 1197–1205 Jand, N., & Foscolo, P U (2005) Decomposition of wood particles in fluidized beds Industrial and Engineering Chemistry Research, 44, 5079–5089 Jess, A (1996) Catalytic upgrading of tarry fuel gases: A kinetic study with model components Chemical Engineering and Processing, 35, 487–494 Ji, P., Feng, W., & Chen, B (2009) Production of ultrapure hydrogen from biomass gasification with air Chemical Engineering Science, 64, 582–592 Jiradilok, V., Gidaspow, D., Breault, R W., Shadle, L J., Guenther, C., & Shi, S (2008) Computation of turbulence and dispersion of cork in the NETL riser Chemical Engineering Science, 63, 2135–2148 250 A Gungor, U Yildirim / Computers and Chemical Engineering 48 (2013) 234–250 Kaiser, S., Löffler, G., Bosch, K., & Hofbauer, H (2003) Hydrodynamics of a dual fluidized bed gasifier Part II Simulation of solid circulation rate, pressure loop and stability Chemical Engineering Science, 58, 4215–4223 Karmakar, M K., & Datta, A B (2010) Hydrodynamics of a dual fluidized bed gasifier Advanced Powder Technology, 21, 521–528 Kaushal, P., Abedi, J., & Mahinpey, N (2010) A comprehensive mathematical model for biomass gasification in a bubbling fluidized bed reactor Fuel, 89, 3650–3661 Kirubakaran, V., Sivaramakrishnan, V., Nalini, R., Sekar, T., Premalatha, M., & Subramanian, P (2009) A review on gasification of biomass Renewable and Sustainable Energy Reviews, 13, 179–186 Lee, H S., Lee, Y., Park, S S., Chae, H J., Jeong, S Y., & Lee, D H (2010) Hydrodynamic characteristics of cold-bed circulating fluidized beds for the methanol to olefins process Korean Journal of Chemical Engineering, 27(4), 1328–1332 Li, C., & Suzuki, K (2009) Tar property, analysis, reforming mechanism and model for biomass gasification – An overview Renewable and Sustainable Energy Reviews, 13, 594–604 Li, X T., Grace, J R., Lim, C J., Watkinson, A P., Chen, H P., & Kim, J R (2004) Biomass gasification in a circulating fluidized bed Biomass and Bioenergy, 26, 171–193 Liu, H., & Gibbs, B M (2003) Modeling NH3 and HCN emissions from biomass circulating fluidized bed gasifiers Fuel, 82, 1591–1604 Lundberg, H., Morris, M., & Rensfelt, E (1997) Biomass gasification for energy production Presented at the ALTERNER-Programme Biomass Event, Finland, 1–5 September Lv, P M., Xiong, Z H., Chang, J., Wu, C Z., Chen, Y., & Zhu, J X (2004) An experimental study on biomass air-steam gasification in a fluidized bed Bioresource Technology, 95, 95–101 Mahishi, M R., & Goswami, D Y (2007) An experimental study of hydrogen production by gasification of biomass in the presence of a CO2 sorbent International Journal of Hydrogen Energy, 32, 2803–2808 Mansaray, K G., Ghaly, A E., Al-Taweel, A M., Hamdullahpur, F., & Ugursal, V I (1999) Air gasification of rice husk in a dual distributor type fluidized bed gasifier Biomass and Bioenergy, 17, 315–332 McKendry, P (2002) Energy production from biomass (part 3): Gasification technologies Bioresource Technology, 83, 55–63 Milne, T A., Evans, R J., & Abatzoglou, N (1998) Biomass gasifier “tars”: Their nature, formation, and conversion NREL, US, DoE, Report No NREL/TP-570-25357 Morf, P., Haslerb, P., & Nussbaumer, T (2002) Mechanisms and kinetics of homogeneous secondary reactions of tar from continuous pyrolysis of wood chips Fuel, 81, 843–853 Narvaez, I., Orio, A., Aznar, M., & Corella, J (1996) Biomass gasification with air in an atmospheric bubbling fluidized bed Effect of six operational variables on the quality of the produced raw gas Industrial and Engineering Chemistry Research, 35, 2110–2120 Ngo, S I., Nguyen, T D B., Lim, Y., Song, B., Lee, U., Choi, Y., et al (2011) Performance evaluation for dual circulating fluidized-bed steam gasifier of biomass using quasi-equilibrium three-stage gasification model Applied Energy, 88, 5208–5220 Nguyen, T D B., Ngo, S I., Lim, Y., Lee, J W., Lee, U., & Song, B (2012) Three-stage steady-state model for biomass gasification in a dual circulating fluidized-bed Energy Conversion and Management, 54, 100–112 Nguyen, T D B., Seo, M W., Lima, Y., Song, B., & Kim, S (2012) CFD simulation with experiments in a dual circulating fluidized bed gasifier Computers and Chemical Engineering, 36, 48–56 Petersen, I., & Werther, J (2005) Three-dimensional modeling of a circulating fluidized bed gasifier for sewage sludge Chemical Engineering Science, 60, 4469–4484 Pfeifer, C., Rauch, R., & Hofbauer, H (2004) In-bed catalytic tar reduction in a dual fluidized bed biomass steam gasifier Industrial and Engineering Chemistry Research, 43, 1634–1640 Pröll, T., & Hofbauer, H (2008) H2 rich syngas by selective CO2 removal from biomass gasification in a dual fluidized bed system-process modelling approach Fuel Processing Technology, 89, 1207–1217 Radmanesh, R., Chaouki, J., & Guy, C (2006) Biomass gasification in a bubbling fluidized bed reactor: Experiments and modeling AIChE Journal, 52, 4258–4272 Ramzan, N., Ashraf, A., Naveed, S., & Malik, A (2011) Simulation of hybrid biomass gasification using Aspen plus: A comparative performance analysis for food, municipal solid and poultry waste Biomass and Bioenergy, 35, 3962–3969 Reuge, N., Cadoret, L., Coufort-Saudejaud, C., Pannala, S., Syamlal, M., & Caussat, B (2008) Multifluid Eulerian modeling of dense gas–solids fluidized bed hydrodynamics: Influence of the dissipation parameters Chemical Engineering Science, 63, 5540–5551 Rhodes, M J., Wang, X S., Cheng, H., & Hirama, T (1992) Similar profiles of solids flux in circulating fluidized bed risers Chemical Engineering Science, 47(7), 1635–1643 Sadaka, S S., Ghaly, A E., & Sabbah, M A (2002) Two phase biomass air-steam gasification model for fluidized bed reactors: Part II Model sensitivity Biomass and Bioenergy, 22, 463–477 Sanz, A., & Corella, J (2006) Modeling circulating fluidized bed biomass gasifiers Results from a pseudo-rigorous 1-dimensional model for stationary state Fuel Processing Technology, 87, 247–258 Scala, F., & Chirone, R (2006) Combustion and attrition of biomass chars in a fluidized bed Energy and Fuels, 20, 91–102 Schuster, G., Löffler, G., Weigl, K., & Hofbauer, H (2001) Biomass steam gasification – An extensive parametric modeling study Bioresource Technology, 77, 71–79 Shen, L., Gao, Y., & Xiao, J (2008) Simulation of hydrogen production from biomass gasification in interconnected fluidized beds Biomass and Bioenergy, 32, 120–127 Skoulou, V., Koufodimos, G., Samaras, Z., & Zabaniotou, A (2008) Low temperature gasification of olive kernels in a 5-kW fluidized bed reactor for H2 -rich producer gas International Journal of Hydrogen Energy, 33, 6515–6524 Smoot, L D., & Smith, P J (1985) Coal combustion and gasification New York: Plenum Press Sommariva, S., Grana, R., Maffei, T., Pierucci, S., & Ranzi, E (2011) A kinetic approach to the mathematical model of fixed bed gasifiers Computers and Chemical Engineering, 35, 928–935 Taghipour, F., Ellis, N., & Wong, C (2005) Experimental and computational study of gas–solid fluidized bed hydrodynamics Chemical Engineering Science, 60, 6857–6867 Tsuo, Y P., & Gidaspow, D (1990) Computation of flow patterns in circulating fluidized beds AIChE Journal, 36, 885–896 Vejahati, F., Mahinpey, N., Ellis, N., & Nikoo, M B (2009) CFD simulation of gas–solid bubbling fluidized bed: A new method for adjusting drag law Canadian Journal of Chemical Engineering, 87, 19–30 Wang, Q., Luo, Z., Li, X M., Fang, M., & Ni Cen, K (1999) A mathematical model for a circulating fluidized bed (CFB) boiler Energy, 24, 633–653 Wang, Q., Luo, Z., Ni, M., & Cen, K (2003) Particle population balance model for a circulating fluidized bed boiler Chemical Engineering Journal, 93, 121–133 Wang, Y., & Yan, L (2008) Review CFD studies on biomass thermochemical conversion International Journal of Molecular Sciences, 9, 1108–1130 Wein, J W (1992) Das expansionsverhalten von gas/feststoff-wirbelschicten bei hoheren gasgeschwindigkeiten, Ph.D Thesis, TU Hamburg-Harburg, Germany Westbrook, C., & Dryer, F L (1984) Chemical kinetic modeling of hydrocarbon combustion Progress in Energy and Combustion Science, 10, 1–57 Wurzenberger, J C., Wallner, S., Raupenstrauch, H., & Khinast, J G (2002) Thermal conversion of biomass: Comprehensive reactor and particle modeling AIChE Journal, 48, 2398–2411 Yin, X L., Wu, C Z., Zheng, S P., & Chen, Y (2002) Design and operation of a CFB gas cation and power generation system for rice husk Biomass and Bioenergy, 23, 181–187 Zenz, F A., & Weil, N A (1958) A theoretical–empirical approach to the mechanism of particle entrainment from fluidized beds AIChE Journal, 4, 472–479