An Eulerian modeling approach of wood gasification in a bubbling fluidized bed reactor using char as bed material

An Eulerian modeling approach of wood gasification in a bubbling fluidized bed reactor using char as bed material

Fuel 89 (2010) 2903–2917 Contents lists available at ScienceDirect Fuel journal homepage: www.elsevier.com/locate/fuel An Eulerian modeling approac...

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Fuel 89 (2010) 2903–2917

Contents lists available at ScienceDirect

Fuel journal homepage: www.elsevier.com/locate/fuel

An Eulerian modeling approach of wood gasification in a bubbling fluidized bed reactor using char as bed material S. Gerber, F. Behrendt, M. Oevermann * Berlin Institute of Technology, School of Process Sciences and Engineering, Department of Energy Engineering, Chair for Energy Process Engineering and Conversion Technologies for Renewable Energies, Fasanenstr. 89, 10623 Berlin, Germany

a r t i c l e

i n f o

Article history: Received 16 October 2009 Received in revised form 3 March 2010 Accepted 17 March 2010 Available online 29 March 2010 Keywords: Euler–Euler modeling Multiphase flow Biomass Gasification Fluidized bed

a b s t r a c t We present an Eulerian multiphase approach for modeling the gasification of wood in fluidized beds. The kinetic theory of granular material is used to evaluate constitutive properties of the dispersed solid phase. Comprehensive models for wood pyrolysis, char gasification and homogeneous gas phase reactions are taken into account. The dispersed solid phase within the reactor is modeled as three continuous phases, i.e., one phase representing wood and two char phases with different diameters. In contrast to most other studies we investigate a fluidized bed which consists of wood and char particles without additional inert particles such as limestone or olivine. 2D simulation results for a lab-scale bubbling fluidized bed reactor are presented and compared with experimental data for product gas and tar concentrations and temperature. We investigate the influence of two different classes of parameters on product gas concentrations and temperature: (i) operating conditions such as initial bed height, wood feeding rate, and reactor throughput and (ii) model parameters like thermal boundary conditions, primary pyrolysis kinetics, and secondary pyrolysis model. Two different pyrolysis models are implemented and are compared against each other. The numerical results indicate (i) a relatively low influence of the investigated operating conditions on the main product gas components, (ii) a high sensitivity of main product gas components CO and CO2 on the thermal boundary condition, and (iii) a very strong influence of operating conditions and model parameters on the tar content in the product gas. Ó 2010 Elsevier Ltd. All rights reserved.

1. Introduction Biomass such as wood, corn crops, forestry residues, etc., is a renewable energy resource which can be used for sustainable heat and power generation. The increasing awareness of the limited availability of fossil fuels and the higher sensibility in developed as well as developing countries towards the environmental issues of pollutants from fossil fuels have substantially increased the scientific research activities related to biomass. The production of wood gas in fluidized bed reactors is regarded as one of the most promising techniques to efficiently exploit the energy from biomass. Some of the beneficial features of fluidized bed reactors are high particle heating rates and good mixing properties. An important advantage of thermochemical over bio-chemical conversion of wood is that lignin is (energetically) exploited without the need for any special treatment. Although wood gasification has been technically used since more than a century there still is a lack of fundamental under* Corresponding author. Tel.: +49 303 142 2452; fax: +49 303 142 2157. E-mail addresses: [email protected] (S. Gerber), [email protected] tu-berlin.de (F. Behrendt), [email protected] (M. Oevermann). 0016-2361/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.fuel.2010.03.034

standing of the complex interactions of fluid mechanics and thermal behavior during the thermal degradation of biomass in fluidized bed reactors. This might be connected to the fact that measuring physical important variables in a reactive gas–solid flow is still a challenge. As an example, even for non-reacting dense gas– solid flow it is still an unsolved problem to simultaneously measure the velocities of the gas phase and the particulate phase on timescales of the particle collision dynamics. Due to this lack of fundamental understanding the design of fluidized bed reactors for the combustion and gasification of biomass as well as coal is often based on empirical correlations and experiments in laboratory and pilot-scale units. However, detailed models and numerical simulations will allow to address fundamental physical questions related to biomass conversion on a level not achievable with current capabilities of measurement techniques. Ultimately, validated models and simulation tools will help to design and optimize biomass conversion processes in fluidized bed reactors of industrial size. There are numerous models available to simulate fluidized beds reactors on different levels of accuracy and modeling depth. Zone/ cell models [1–3] and equilibrium models [4] are suitable for design optimization of industrial size gasifiers but modeling of the

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interaction between fluid mechanics and chemistry relies on empirical correlations and assumptions on the flow structure. More detailed models such as Euler–Euler [5–13] and Euler–Lagrange [14–20] models are more targeted towards fundamental investigations of the fully coupled chemical and fluid mechanical aspects of fluidized beds. With current and foreseeable computer resources it does not seem feasible to use more detailed models such as Euler–Lagrange methods for the simulation of large scale fluidized bed reactors with detailed gasification models for each individual particle. As an example, in a recent study of coal combustion in a bubbling fluidized bed by Zhou et al. [17] only 20 reactive coal, 2000 inert sand particles and simulation times of 2 s are used. The authors of this paper present in Ref. [20] an Euler–Lagrange modeling approach to investigate wood gasification in a dense fluidized bed under atmospheric conditions. Although several thousand reactive particles and simulation times of 100 s real time are considered in Ref. [20], the simulated reactor is still a small laboratory scale reactor. However, Euler–Lagrange methods are extremely useful tools to investigate phenomena which rely on a description at the individual particle level and to improve closure models for continuum models. In a serious of papers Papadikis et al. [21–24] present a combination of an Euler–Euler and an Euler–Lagrange approach. Whereas the fluidized bed with inert sand particles is modeled with the continuous Euler–Euler approach, the reacting biomass particles injected into the reactor are modeled on the discrete Euler–Lagrange level. However, only one and two reacting particles are taken into account in Ref. [21,24] and simulation times are limited to 3 and 5 s of real time, respectively. Besides the cited applications for non-reacting multiphase flow Euler–Euler models have been used to study processes involving chemical reactions in fluidized beds as well. Sofialidis and Faltsi [25] investigate biomass gasification in a bubbling fluidized bed. The inert sand bed is modeled as a static isotropic porous media containing prescribed spherical volumes to account for the presence of rising bubbles. The biomass particles are modeled as Lagrangian particles. The model takes into account drying and devolatilization of biomass, heterogeneous reactions of char, and a single reaction in the gas phase converting water and methane into carbon monoxide and hydrogen. CO and H2 are neglected as gasification products. The simulated exhaust gas concentrations of a 3D gasifier agree reasonably well with measured data for H2, O2, CO2, and H2O but underpredict CO2 and overpredict CO concentrations. Lathouwers and Bellan [6,7] use an Euler–Euler approach for the simulation of biomass pyrolysis in a dense fluidized bed of a laboratory scale reactor. Solid phases for virgin biomass, an active intermediate, char, and inert sand particles are introduced with a simple pyrolysis model. Qualitative results for tar yields under different operating conditions are reported. Within the presented simulation times of 5 s a steady state is not reached and comparisons with experimental data are not included in the study. The focus of the study presented in Ref. [6,7] is on optimizing tar yields from pyrolysis – gas phase reactions and results of other products than tar are not included. Xie et al. investigate in [10] the reaction of silane under isothermal conditions with the public domain code MFIX. Their study was mainly devoted to the application of the in situ adaptive tabulation (ISAT) technique for performance issues. Yu et al. [11] present a 2D Euler–Euler model for the numerical simulation of coal gasification in a bubbling fluidized bed reactor of 2 m height and 0.22 m diameter. The model takes into account pyrolysis of coal into char, tar, and volatiles, heterogeneous char, and homogeneous gas phase reactions including simple nitrogen chemistry. Char is modeled as a single solid phase with a constant diameter. Variations in coal feeding rate, air supply, steam supply,

and temperature are investigated with good agreement between simulation results and experiments for the main product gas components CO, CH4, CO2, and H2. The gasification of coal in a lab-scale pressurized spout-fluidised bed of 1 m height and 0.1 m diameter is investigated by Deng et al. in [13] with a steady state Euler–Euler modeling approach using the kinetic theory of granular flow for the particle dynamics. Among the assumptions are a single solid phase with constant diameter, a single-step pyrolysis model without tar and a homogeneous gas phase chemistry with five reactions and five reactive species. Quantitative results for the product gas composition are presented for CO, H2, CH4, and CO2 demonstrating reasonable agreement with measurements. The influence of the operating conditions bed temperature and pressure on product gas concentrations are investigated and showed increased gasification rates with increasing pressure and temperature. Wang and Yan [26] provide an overview of different CFD studies on thermochemical biomass conversion including gasification and combustion processes in, e.g., fixed beds, furnaces, fluidized beds and wood stoves. Most of the cited work in Ref. [26] use commercial CFD codes with Euler–Lagrange modeling approaches. Related to thermal conversion processes the application of Euler–Euler models is well justified for high heating rates when pyrolysis and gasification reactions are mostly kinetically controlled. In the regime of low heating rates, e.g., large particles with inter-particle gradients, it might not be sufficient to describe the thermodynamic state of particles which belong to the same diameter class with homogeneous values for temperature, density, and concentrations. Under such conditions new modeling approaches within the Euler–Euler context have to be developed and tested in the future. In this work we present an Euler–Euler model for the simulation of wood gasification in a dense fluidized bed. Compared to most other studies we do not use an inert bed material but instead use char as the main bed material. Besides the capability to decompose tar [27,28] the use of char as a bed material has some potential advantages over traditional catalysts such as olivine or limestone: (i) there is no need for regeneration as char is a byproduct of biomass gasification and deactivated char will simply be further gasified, (ii) due its lower density compared to traditional bed materials there is a also a lower pressure loss in the reactor and fluidization occurs at lower velocities of the interstitial fluid, and (iii) as a byproduct it comes at almost no cost. From a computational point of view it is more demanding than an inert bed material as we need to model the bulk of the solid phase as a reactive material as well. In our model we introduce eight components within the gas phase and three solid phases: wood with a diameter of 4 mm and two char classes with diameters of 1.5 and 2 mm. The model takes into account gas phase chemistry, pyrolysis models, and heterogeneous gasification reactions. Two different models for the secondary pyrolysis of tar are implemented. We apply the method for the simulation of wood gasification in a bubbling fluidized bed and compare results for product gas concentrations, temperature, and tar yield with experimental data of a laboratory scale reactor. We investigate the qualitative influence of temperature boundary conditions, wood feeding rates, air supply, and varying initial bed heights on product gas concentrations and tar yields. In contrast to most other studies presented in the literature we have (in accordance with the experiments performed at our institute) a bed material which consists of wood and char only. In order to get statistically stationary results the simulation have been run for 200 s of real time. The paper is organized as follows: Section 2 summarizes the mathematical model with the governing equations for gas phase and solid phases and the models for pyrolysis, gasification, and homogeneous gas phase chemistry. In Section 3 we present simulation results for a bubbling fluidized bed reactor and discuss them

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before we close the article with some concluding remarks in Section 4.

The energy equation in MFIX is solved in form of a non-conservative temperature equation:

2. Mathematical model

g qg cpg

In this study we use the public domain code MFIX [29–31] as a base for our numerical investigations. MFIX has been developed at National Energy Technology Laboratory (NETL) for describing the hydrodynamics, heat transfer and chemical reactions in multiphase systems with a continuum approach. The code solves the unsteady balance equations for mass, momentum, energy and species for gas and multiple solids phases. Constitutive relations are modeled using granular stress equations based on kinetic theory and frictional flow theory. The details of the code can be found in the cited code documentation. Here we briefly summarize the governing equations for the gas phase and multiple solid phases before we present the implemented models for pyrolysis, heterogeneous gasification, and homogeneous gas phase reactions. 2.1. Gas phase The gas phase is modeled in the usual Eulerian way by volume averaged balance equations for species masses, momentum, and energy. The averaging introduces the volume fraction of the gas phase e in each equation which effectively lowers the local capacity of each variable and takes care of effects like, e.g., blockage. The balance equation for the mass fraction Y a , a ¼ 1; . . . ; ng , of gaseous component a can be written as ns X

@ eg qg Y a _ a;g þ _ a;sm ; w þ r  ðeg qg Y a v g Þ ¼ w @t m¼1

ð1Þ

_ a;g , w _ a;sm denote, respectively, the volume fraction where eg , qg , v g , w of the gas phase, the gas phase density, the velocity of the gas phase, the net production rate of component a due to homogeneous gas phase reactions, and the net mass exchange term between the solid phase m and the gas phase component a by, e.g., heterogeneous reactions as described below. The sum of all species balance Eq. (1) gives the balance equation for the density of the gas phase: ng X ns X @ eg qg _ a;sm : w þ r  ðeg qg v g Þ ¼ @t a¼1 m¼1

ð2Þ

The gas phase momentum equation is given by the Navier– Stokes equation with additional momentum exchange terms between the gas phase and the solid phases: ns X @ eg qg v g I mg ; þ r  ðeg qg v g v g Þ ¼ eg rp  eg ðr  sg Þ  eg qg g  @t m¼1

ð3Þ where p, sg , g, and I mg are, respectively, the pressure, the viscous stress tensor, gravity acceleration, and the moment exchange term between gas phase and the solid phase m. Here the gas phase is modeled as a Newtonian fluid with a linear stress law



2 3



s ¼ l frv g g þ frv g gT  d ðr  v g Þ ; where l is the dynamic viscosity. The momentum exchange term I mg in this study takes into account drag force, caused by velocity differences between the phases, buoyancy force, caused by the fluid pressure gradient, and momentum transfer due to mass transfer, caused by thermal degradation of biomass and char particles. The momentum transfer due to mass transfer takes place with the mean solid velocity v sm , which implies the (usual) assumption that the mass flow out a particles is spherically symmetric in a reference system moving with the particle.

  ns X   @T g cgm T sm  T g  DHg ; þ v g  rT g ¼ r  qg þ @t m¼1 ð4Þ

with the heat flux vector qg , the heat transfer coefficient cgm between gas phase and solid phase m and the heat of reaction DHg due to homogeneous gas phase reactions. 2.2. Particulate phase Within the Euler–Euler modeling approach the solid phases are treated as interpenetrating continuous phases. Each solid phase has distinct physical properties such as diameter, density, temperature, and composition. Here we assume that each solid phase has a fixed diameter. The concept of continuous interpenetrating solid phases leads for each solid phase to balance equations for mass, momentum, and energy which are formally similar to the normal gas phase equations. The dynamics of solid transport, solid–solid, and gas–solid interactions is modeled via complex expressions for the constitutive relations, e.g., the solids pressure and the solids stress tensor which are based on the kinetic theory of granular material. Balancing the mass of solid phase m, m ¼ 1; . . . ; ns , results in an equation for the solid density qsm ¼ qY sm : n

g ns X X @ esm qsm _ sml  _ a;sm ; w w þ r  ðesm qsm v sm Þ ¼ @t a¼1 l¼1

ð5Þ

where esm and v sm are the volume fraction and the velocity of the _ sml between solid phase m, respectively. The mass transfer term w solid phases m and l in the presented study is due to the conversion of solid biomass/wood into char by pyrolysis only, see Section 2.4. In addition both wood (via wood gas) and char (via gasification _ a;sm via products) produce gaseous matter with production rates w thermal degradation and heterogeneous reactions. In this work we represent the dispersed phase with three distinct solid phases: one solid phase for wood (4 mm diameter) and two solid phases for char (2 mm and 1.5 mm diameter). Each solid phase has a constant, i.e. time independent, composition (with char modeled as pure carbon). Therefore, we do not need to introduce additional equations for individual species in the solid phases. However, this approach implies that we have an instantaneous transfer of char produced from wood in the pyrolysis step into the two different char phases as we are not representing char within the wood solid phase. This type of modeling is motivated by the fact that under usual operating conditions for gasification processes conditions thermal conversion rates by pyrolysis of wood are orders of magnitude higher than gasification rates of char leading to a much shorter life time of (fresh) wood compared to the life time of char. In the limit of an infinitely fast pyrolysis step wood entering the reactor would be instantaneously converted into char and volatiles. Although, we assume a constant composition for each solid phase, we do have Arrhenius type temperature dependent conversion rates for wood and char, see Sections 2.4 and 2.5 for further details. The momentum balance for solid phase m is

@ ðesm qsm v sm Þ þ r  ðesm qsm v sm v sm Þ @t ns X ¼ esm rpsm þ r  ssm  I ml þ I mg þ m qm g;

ð6Þ

l¼1 l–m

where psm and ssm are the pressure and the stress tensor of the solid phase m, I mg is the momentum interface exchange term between

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solid phase m and the gas phase, and I ml denotes the solid–solid momentum interface exchange term between the solid phases m and l by, for example, collisions of particles. The set of governing equations for each solid phase m is complemented with an energy equation written in form of a non-conservative balance equation for the temperature for phase:

    @T sm þ v sm  rT sm ¼ r  qsm  cgm T m  T g  DHsm ; @t

2.3. Homogeneous gas phase reactions The gaseous species taken into account in this work are N2, O2, H2, CH4, CO, CO2, H2O and tar. A reaction mechanism capturing the main effects of the gas phase conversion process is given by the following four reactions:

ð1Þ

CO þ H2 O  H2 þ CO2

½35

ð2Þ

2 CO þ O2 ! 2 CO2

½36

ð7Þ

ð3Þ

2 H2 þ O2 ! 2 H2 O

½37

where qsm is the conductive heat flux within solid phase m, DHsm is the heat of reaction (pyrolysis and heterogeneous reactions) for solid phase m, and cgm ðT m  T g Þ is the heat flux between solid phase m and the gas phase with the inter-phase heat transfer coefficient cgm . Heat exchange between different solid phase is neglected in MFIX which is, according to, e.g., Lathouwers and Bellan [7], a non-critical simplification under the operating conditions considered here. The set of governing equations for the solid phases has to be closed with appropriate constitutive relations for the solids pressure, the solids frictional stress tensor, the inter-phase momentum transfer, and the inter-phase heat transfer. An established approach to model these quantities for poly-disperse gas–solid flow is the theory of granular material which relates solid transport properties to the so called granular energy. This concept is adapted from the kinetic theory of gases. In addition to the transport equations presented above we solve an equation for the granular temperature Hm in each solid phase m:

ð4Þ

CH4 þ 2 O2 ! CO2 þ 2 H2 O

½36

esm qm cpm

  Y 3 @ Hm em qm em qm J m ; þ u  rHm ¼ rjm rHm þ sm : rum þ 2 @t m ð8Þ where jm denotes the conductivity of solid phase m, Jm is the colliQ sional dissipation, and m the exchange term between solid phase m and the gas phase. For the calculations presented in this study we have used the standard models for the fluid dynamics as provided by the MFIX code. They are described in detail in Ref. [29,32]. A summary of the used settings of available switches in the solid phase equations is given in Table 1. The detailed expressions are given in Ref. [29,32]. All other settings are left to their default values. Turbulence modeling for (reactive) gas–solid flow – especially in the Euler–Euler framework – is an active research field. Here we dispense using one of the recently available turbulence models in MFIX. Preliminary tests have revealed that the influence of the turbulence models on the gasification process for grid sizes typically used for reactive Euler–Euler simulations is negligible. As we are not resolving any boundary layers with typical grid sizes used in Euler–Euler simulations we apply slip conditions for the velocity at solid walls. As we are mainly focusing on the aspect of thermal conversion, the assumptions with respect to turbulence is not a critical issue here. However, we will investigate the influence of different turbulence models on the gasification process and the fluidization behavior in a forthcoming publication.

The kinetic parameters and reaction rates for the reactions above are taken from the given references and are summarized in Table 2. The first reaction in (9) is the so called water gas shift reaction which is exothermic at standard state and modeled as a reversible reaction here. The last three reactions are exothermic oxidation reactions of H2, CH4 and CO which can assumed to be irreversible under typical gasification conditions. For the specific reactor considered in this study, Fig. 3, which has no secondary air supply and a nitrogen flooded fuel supply, oxidation reactions occur only in the very lowest part of the reactor above the air inlet. Under full rich gasification conditions oxygen is completely consumed in this region via heterogeneous reactions with char as described in Section 2.5 and to a minor part by oxidation of gaseous components as described above. That means in the mayor part of the reactor the water gas shift reaction is the only relevant gas phase reaction. Reactions of nitrogen and methanisation reactions are not considered in this study. The heat of reactions for the mechanism given in (9) are assumed to be constant. 2.4. Pyrolysis Pyrolysis is the thermal degradation of solids in the absence of an oxidizing agent. Pyrolysis is a very complex thermochemical process leading to the a huge number of different products. Due to the complex nature of the pyrolysis process, models at different levels of complexity have been developed. An overview of the available pyrolysis models with an extended literature survey can be found in the recent review article of Di Blasi [38]. Within an Eulerian modeling approach some of the more elaborate pyrolysis schemes given in the literature are not applicable. The main problem of using them emerges due to the loss of individual particle data in the context of the Euler–Euler model. In particular, within the classical Euler–Euler modeling approach with solid phases having constant diameters one has no information about the actual lifetime of the particles. However, so called mul-

Table 2 Rate expressions for the homogeneous gas phase reactions as shown in (9). Rate constants are given in the form k ¼ A expðT a =TÞ. The equilibrium constant for the water gas shift reaction is defined as K p ðTÞ ¼ 0:0265  expð3958 K= TÞ. Rate expression in mol/cm3s h i d½CO 2 ½H2  ¼ k ½CO½H2 O þ ½CO K p ðTÞ dt

Reaction no. in (9) Table 1 Summary of used settings for available switches of the solid phases in MFIX used in the presented study. For details and further information we refer to [29,32].

ð9Þ

(1) (2)

d½CO ¼ k½CO½O2 0:25 ½H2 O0:5 dt d½H2  ¼ k½H2 ½O2  dt d½CH4  ¼ k½CH4 0:7 ½O2 0:8 dt

(3) Switch

Setting

(4)

Constitutive equations Maximum packing

Modified Princeton model [8] Prescribed value eI s ¼ 0:35

Reaction no.

A

Frictional stress model

es > e es 6 e

(1)

2:780  106

(2)

3:980  1014

20,119

Drag correlation

Syamlal and O’Brien model

(3)

2:196  1012

13,127

(4)

1:580  1013

24,343

I s : I s :

Schaeffer model [33] Princeton model [34]

T a in K 1510

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ti-species pyrolysis schemes need this information to calculate the change of yield of the virtual species (usually given by at least three components, namely cellulose, hemicellulose and lignin), see [38] for details. Furthermore, the loss of individual particle data prohibits the direct application of detailed single particle models resolving the inner structure in an Euler–Euler model. Here we apply two different two-step models for the pyrolysis of biomass/wood which share a common primary pyrolysis step and differ in the secondary pyrolysis step. The first step, called primary pyrolysis, denotes the thermal degradation of wood into char, wood gas, and tar. Each of these products is a complex mixture of different species. The actual compositions of wood gas and tar strongly depend on the heating temperature [39] and the heating rate [38]. Several lumped reaction schemes for the primary pyrolysis are available in the literature, e.g., Grønli [39]. Wood gas typically consists of CO, CO2, CH4, H2, H2O and others components like alkanes and alkenes, with a composition depending on the process conditions. Despite the varying composition of the wood gas (see for example [40] for the pyrolysis of lignin) most models for engineering applications assume a constant composition, see [41,6,21–23]. For the primary pyrolysis step we apply the model of Grønli [42] which is also used in the work of Larfeldt et al. [43]. The reaction path for the primary pyrolysis is given in Fig. 1. The scheme allows for different production rates of wood gas, char, and tar with a standard Arrhenius type law of the form A expðEa =RTÞ and kinetic parameters as shown in Table 4. The composition of the wood gas is fixed and summarized in Table 3. The char from primary pyrolysis is assumed to consist of pure carbon. However, Table 5 shows data from Klose and Wölki [45] demonstrating that char actually is a mixture of mostly carbon but still contains a significant part of O, H and N (especially if one looks at mole fractions instead of mass fractions). In addition it is well known that the reactivity of char seems to depend on the contained minerals [46]. The char amounts resulting from the primary pyrolysis step are immediately transferred to equal amounts into the two solid char fractions. Primary pyrolysis is followed by secondary cracking of tar called secondary pyrolysis. The tar emerging from the pyrolysis is a complex mixture of different components like PAH. Branched aromatic components such as styrol and benzonitril can be degraded to unbranched aromatic components like benzene and naphtaline under catalytic conditions. Secondary pyrolysis is a very complex phenomena and for engineering applications simplified models with

wood

wood gas (H2O, CO, CO2, H2, CH4) char tar

Table 4 Kinetic parameters for the primary pyrolysis step. Product reaction rates are given in the form A exp Ea =RT. Product

A in 1/s

Wood gas

1:43  104

Ea in kJ/mole 88.6

Tar

4:13  106

112.7

Char

7:38  105

106.5

Table 5 Composition of beechwood char from experimental data of Klose and Wölki [45]. Component

Content in wt%

Carbon Oxygen Hydrogen Nitrogen

89.0 9.0 1.7 0.3

only few product components are typically applied. In this study we investigate the influence of two different secondary pyrolysis models with reaction paths as shown in Fig. 2. The first model is from Boroson and Howard [47]. It assumes that the tar from the primary pyrolysis step decomposes into wood gas (via reactive tar1) and inert tar, Fig. 2. The fixed compositions of inert tar and wood gas are given in Tables 6 and 7. The mass fraction value of 22% for the inert tar is taken from Seebauer [44] as Boroson and Howard to not provide a value in [47]. The production rate of inert tar and gaseous components from the tar of the primary pyrolysis step is given in form of a standard Arrhenius type law, i.e., A expðEa =RTÞ, with frequency factor A ¼ 2:3  104 s1 and activation energy Ea ¼ 80; 000 kJ= mol. The second model is taken from Rath and Staudinger [48]. The scheme assumes decomposition of tar into gaseous components as given Table 7. In contrast to the first model it introduces two reactive tar components with different rates. In the original work the production kinetics of each tar component depends on the total yield of that component. This leads to variable tar fractions depending – on a particle base – on the history of each individual particle. This is an information not available in the presented Euler–Euler framework. Therefore we use the model of Rath and Staudinger [48] in a modified way and normalized the maximum yields of the tar components and use constant tar fractions. The tar from the primary pyrolysis step is divided with fixed fractions according to Table 6. In [48] the authors recommend not using

tarinert tar

tar1

wood gas (CO, CO2, CH4, H2O, H2)

tar2

wood gas (CO, CO2, CH4, H2O, H2)

Fig. 1. Reaction path for the primary pyrolysis according to Larfeldt et al. [43] and Grønli [42].

Fig. 2. Secondary pyrolysis model. The dashed path with tar2 is for the second secondary pyrolysis model only.

Table 3 Composition of wood gas from the primary pyrolysis step according to [44]. The corresponding reaction rate is given in Table 4.

Table 6 Composition of intermediate and inert tar for secondary pyrolysis models 1 and 2 according to experimental data of Seebauer [44] and Rath and Staudinger [48]. For reaction paths see Fig. 2.

Component

Mass fraction

H2 CO CO2 CH4 H2O

0.032 0.270 0.386 0.056 0.256

Component

Tar1/wood gas Tar2/wood gas Tarinert

Mass fraction Model 1 [44]

Model 2 [48]

0.78 – 0.22

0.327 0.497 0.176

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Table 7 Wood gas composition from secondary pyrolysis for model 1 and model 2 in wt% according to Boroson and Howard [47] and Rath and Staudinger [48]. For reaction paths see Fig. 2. The mass fractions for model 1 sum up to one with the inert tar mass fraction of 0.22, Table 6. Component

CO CH4 CO2 H2O H2

Mass fraction Model 1 [47]

Model 2 [48]

Tar1

Tar1

Tar2

0.5633 0.0884 0.1110 – 0.0173

0.602 0.137 0.121 0.140 –

0.534 0.211 0.085 0.170 –

Table 9 Kinetic parameters for the heterogeneous gasification reactions as shown in (10). Reaction rate constants are given in the form k ¼ AT s exp T a =T s . Reaction no.

Rate expression in mol/(cm2 s)

(1)

d½C dt

K d ¼ Sh Dg =dp , Sh ¼ 2 þ 0:6 Re1=2 Sc1=3 Re ¼ dp jv g  v sm j=mg Dg ¼ 3:13 ðT s =1500Þ1:75 p0 =p (2) (4)

(1)

2.5. Heterogeneous reactions Whereas pyrolysis denotes a thermal conversion process which does not need reaction partners in the surrounding atmosphere, gasification is the further degradation of biomass via heterogeneous reactions with such reaction partners. Here we consider heterogeneous gasification reactions of solid char only. The modeling of heterogeneous reactions for solid char depends necessarily on the available gas phase species in the surrounding atmosphere, i.e., the applied model for the homogeneous gas phase reaction. A compatible model with the global reaction scheme for the gas phase as presented in Section 2.3 considers the following four heterogeneous reactions of char:

ð1Þ ð2Þ

C þ O2 ! CO2 C þ CO2 ! 2 CO

½51 ½52

ð3Þ

C þ H2 O ! CO þ H2

½52

ð4Þ

C þ 2 H2 ! CH4

½52

ð10Þ

Table 8 Kinetic parameters for the second secondary pyrolysis model. Reaction rates are given in the form A expðT a =TÞ. For reaction paths see Fig. 2. Component

A in 1/s

Tar1

2:300  104

T a in K 80.0

Tar2

3:076  106

66.3

tarinert

1:130  106

109.0

d½C ¼ k½CO2  dt d½C ¼ k½H2 O dt d8C ¼ k½H2  dt

(3)

Reaction no.

mass fractions for C2H4, C2H6, C3H6, and C2H2 but instead adding these fractions to the value given for CH4. The resulting composition is given in Table 7 and the kinetic parameters are summarized in Table 8. Both models for secondary pyrolysis assume the existence of an inert tar. This existence is mainly a question of temperature since almost every aromatic substance would decompose in case the temperature is just high enough. The heat of pyrolysis underlays big uncertainties. In the presented study we assume pyrolysis to be heat neutral. However, the work of Rath and Staudinger [49] shows that there might be a change in sign for the heat of pyrolysis depending of the char amount resulting form pyrolysis. It is well known that char and/or char ash has a catalytic effect on the gasification process [27,50]. However, little is known about the actual quantitative effects of the catalytic active substances on the kinetic parameters of the pyrolysis, heterogeneous reactions, and tar chemistry. We are actually not aware of any model which could be used without major modifications in an Euler–Euler framework. We do not attempt to model the catalytic effect of char in this study although we are aware of the importance of that phenomena.

1 1 ¼ ðK 1 ½O2  r þ Kd Þ Kr ¼ k

(2) (3) (4)

T a in K

A 1:04  10 3:42 3:42

3

3:42  103

11,200 15,600 15,600 15,600

Rate expressions and kinetic parameters are taken from the cited literature and are summarized in Table 9. The rate expression for the char oxidation reaction C þ O2 ! CO2 taken from [51] is a mixture of kinetic and diffusion controlled mass transfer. From the heterogeneous reactions given above the first and the last reaction are exothermic whereas the gasification of carbon with CO2 and H2O is endothermic. We note that hydrogasification under atmospheric conditions as in this study is of minor importance. As shown in a recent review article of Di Blasi [53] there is very few data in literature available to model more complex reaction mechanism of char. A complete and validated kinetic model with elementary reaction steps for gasification of char from biomass has not been published in the literature. Even for the simple model applied here, one can find a huge variety of kinetic data for each reaction, see [53] for a compilation of available rate coefficients. However, it seems to be a commonly accepted fact that gasification rates with water are higher than those of carbon dioxide, see for example [45]. In this study we do not model mechanical fragmentation of char. As it is difficult to achieve with Euler–Euler models we do not try to model the effect of internal gradients within (large) solid particles. 3. Results and discussion 3.1. Experimental background We apply the presented Euler–Euler model to the simulation of wood gasification in a laboratory scale bubbling fluidized bed operated at the Institute of Energy Engineering at Berlin Institute of Technology. The whole reactor was made out of quartz glass. Fig. 3 shows the reactor and its dimensions. The free board area and the bubbling bed zone have inner diameters of 134 mm and 95 mm, respectively. In contrast to most other published studies on biomass gasification, our reactor is operated with a bed of char only. The initial bed height in the experiments was usually 30– 40 cm. It is known that char has the ability to act as a catalytic material similar to limestone or olivine and to reduce the amount of tar in the product gas [27,50]. Compared to other bed materials such as sand, olivine, or limestone it has the additional advantage of a low pressure drop. However, due to the lack of available models we do not include the catalytic effect here. The reactor was heated from the outside using heating bands with a total length of 24 m and a heating power of 3.6 kw. In addition the reactor was insulated with a non-insulated slot of about 10 cm left over the height of the reactor in order to have a view in-

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S. Gerber et al. / Fuel 89 (2010) 2903–2917

preheated nitrogen mass flow below the fluidization velocity to heat up the system. After the heating period of approximately 60 min the reactor operates with preheated air entering the reactor with a velocity of 0.25 m/s and a temperature of 673 K. The temperature peaks seen in Fig. 4 at about 88 min operating time are due to an interrupted fuel supply. Between t = 100 min and t = 120 min the reactor runs at almost steady state conditions. We use the data from this time window to compare with numerical simulations. After 120 min of operating time several tests were performed leading to the unsteady temperature data seen in Fig. 4. After the experiments only little amounts of ash were left in the reactor which have not been quantified. Usually a small decrease in the total char mass inside the reactor was observed (5–10%). Fig. 5 shows minimum, mean, and maximal measured exhaust gas concentrations from various experiments. The concentrations are sampled values of time–averaged data from 18 different experiments with different charges and types of wood. Usually, the exhaust gas concentrations of CO are higher then those of CO2 as can be seen from Fig. 5. However, among the 18 different experiments their were also two case with higher CO2 than CO concentrations. The huge variance of wood properties (density, water content, structure, etc.) leads to big uncertainties in the modeling

Ø134

1100

Ø 95

42

fuel inlet

Ø 50

600

50

product gas outlet

0.6

air Fig. 3. Bubbling fluidized bed reactor.

mole fraction

0.5

side the operating quartz glass reactor. The reactor was operated under atmospheric conditions and wood is supplied in the lower part of the reactor with a feeding rate of 2 kg/h. Preheated air with a velocity of 0.25 m/s at a temperature of approximately 670 K enters the reactor from the bottom over the whole diameter of the reactor. Thermocouples inside the reactor monitor the temperature inside the reactor. Product gases concentrations are analyzed directly at the product gas outlet (Fig. 3) of the reactor with a combination of gas chromatography and laser mass spectroscopy, see [54] for details. Fig. 4 shows typical experimental temperature data over time at different heights above the inlet in the reactor for a complete experiment. The reactor is started with a bed of pure char and a

min mean max

0.4 0.3 0.2 0.1 0.0

CO

CO2

H2

CH4

C2 H2

1000

temperature [K]

900 800 700 600 500

inlet 150mm 325mm 500mm 700mm 900mm

400 300 200 20

40

60

Cn Hm

N2

Fig. 5. Experimental data of the exhaust gas composition sampled from 19 different experiments with different wood charges.

1100

0

C2 H6

80

100

120

140

160

180

time [min] Fig. 4. Experimental temperature data over time for a complete run of the reactor at different heights.

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S. Gerber et al. / Fuel 89 (2010) 2903–2917

of wood gasification and should be kept in mind when comparing numerical results against experimental data. 3.2. Computational setup of the base case The computational setup for the base case follows the experimental setup described in Section 3.1. Besides the base case we investigate in Section 3.4 the influence of certain parameter variations on quantities like tar yield and product gas concentrations. For the discretization of the reactor shown in Fig. 3 we use a two-dimensional setup with a grid size of 0.7 cm in radial and 0.71 cm in axial direction, which results in 2644 cells in total. A 3D calculation and a comparison with the 2D results of this study will be presented in a forthcoming publication. As we are not resolving the boundary layer and the application of wall functions for dense polydispers gas–solid flow is questionable, we apply slip boundary conditions at all walls for the velocity. This seems to be a reasonable assumption as the focus of our investigations is primarily on the gasification process which is not much influenced by the velocity boundary condition. At the air inlet we prescribe a velocity of 25 cm/s with a temperature of 670 K. The fuel inlet velocity is set to 0.035 cm/s with a wood density of 585 kg/cm3 and an assumed volume fraction of 0.65 corresponding to the wood supply of 2 kg/h in the experiment. As we do not explicitly model the drying process of wood we assume that the wood entering the reactor contains no water and add the assumed wood moisture of 10 wt% at the fuel inlet in form of a gaseous water inflow with a velocity of 7.9 cm/s. Both water and wood temperature are estimated to have a temperature of T = 150 °C. The pressure of all simulations and experiments is atmospheric. Three solid phases are used to model the particulate phase within the continuum model approach: spherical wood particles with a diameter of 4 mm and two classes of char particles with diameters of 1.5 and 2 mm. These diameters correspond to the most prevalent diameters observed in the experiment. The molar mass of wood was set to a representative value of 128 g/mol and that of char to 12 g/mol. Char is assumed to have a density of 450 g/cm3. For the base case we apply model 2 for the secondary pyrolysis of tar. The temperature boundary condition has a strong influence on the process of pyrolysis and gasification of biomass and char. For the base case we apply a constant prescribed temperature at the reactor walls. According to the temperature stratification with height of the reactor we apply different temperatures in the lower (below the conus, called zone I) and the upper part (above the

qwood

vH O 2

T H2 O Y H2 O pg

0.035 cm/s 423 K 585 kg/m3 7.9 cm/s 423 K 1 1 atm

Air inlet

vg Tg pg Walls T zone 1 T zone 2

v vs

Fig. 6 shows snapshots of the gas void fraction eg from the beginning of the simulation where bubbles start to evolve and rise in the reactor. Wood is fed from the left just below the big bubble in the first picture. Bubble formation and growth under the operating conditions here is mainly due to the pyrolysis of wood into gaseous components and – to a minor part – by heterogeneous gasification reactions and exothermic gas phase oxidation reactions at the bottom of the reactor. At the bottom of the reactor we observe the formation of relatively small bubbles. At the left wall of the reactor above the wood supply a bubble is formed by pyrolysis of fresh wood. The bubble moves along the wall of the reactor and grows especially in the axial direction before it leaves the bed. The snapshots of the gas phase void fraction in Fig. 7 represent a typical time period of bubble formation, growth, coalescence, and rise during steady state conditions of the reactor. We

Table 11 Initial conditions for the base case. Initial conditions Tg p Y N2

1020 K 1 atm 1 0.325 0.325 0.0 1020 K 35 cm

T s;char1 ; T s;char2 hbed

Fuel inlet T wood

3.3. Results for the base case

es;char1 es;char2 es;wood

Table 10 Boundary conditions for the base case.

v wood

conus, called zone II). The different zones were introduced in order to model the different heat transfer in regions with particle wall contacts (lower part, bed) and without (freeboard). The prescribed temperature values corresponding to the different parts of the reactor wall are given in Table 10. As there are no experimental data available for the wall temperatures the chosen values for the lower and upper part have to be regarded as educated guesses. The uncertainty in the thermal boundary condition has motivated the investigation of different numerical temperature boundary conditions in Section 3.4.2. For the initial conditions inside the reactor we assume a zero velocity, a gas phase composition of pure nitrogen, no wood, equal volume fractions of char with 1.5 and 2 mm, and a temperature of 1020 K for both char and gas phase. A summary of initial and boundary conditions and material properties of the solid phases is provided in Tables 10–12. All thermodynamic data of the gas phase are evaluated as polynomial functions of temperature. With the used grid resolution typical compute times for a simulation of 200 s of real time are in the order of several days using four AMD Opteron cores running at 2.4 GHz clock speed.

25 cm/s 670 K 1 atm 970 K 570 K Slip condition [55]

Table 12 Material properties and references for the solid phases. Property

Value

Reference

qwood qchar

585 kg/m3 450 kg/m3 4 mm 2 mm 1.5 mm 2380 J/kg K 1600 J/kg K 0.158 W/mK 0.107 W/mK

[56] [56] [56] [56]

dwood dchar 1 dchar 2 cp; wood cp; char kwood kchar

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S. Gerber et al. / Fuel 89 (2010) 2903–2917

Fig. 8. Volume fractions of the two char phases (dchar1 ¼ 2 mm, dchar2 ¼ 1:5 mm). Fig. 6. Snapshots of the gas phase volume fraction simulation.

eg at the beginning of the

Fig. 7. Snapshots of the gas phase volume fraction

shows snapshots of the char volume fractions for both char classes. Here we observe some segregation effects – the char class with the bigger diameter has the maximum density at the bottom of the reactor while the smaller particles show a maximum at the top of the bed. The relatively low segregation effect indicates an efficient and strong mixing within the reactor which has been observed in the experiments as well. The particle fluidization of the investigated reactor belongs to Geldart group D [57]. Characteristic features of Geldart group D particles are that bubbles coalesce rapidly and grow large (which is apparent in Fig. 7) and that bubbles rise with a lower velocity than the fluid velocity inside the bubble. The second feature can be observed in Fig. 9 which shows a snapshot of gas phase velocity

eg at steady state.

observe again the formation of small bubbles due gas expansion by exothermic oxidation reactions above the air inlet and the emergence of big gas bubbles above the wood occupying periodically almost the whole cross-section area of the (small) laboratory scale reactor. Bi- and polydispers gas–solid flow in fluidized beds typically show segregation effects which is strongly influenced by the bubble characteristics. With equal densities smaller particles tend to float on top of bigger particles. Here the two char classes represent a bidispers system with equal densities of the particles. Fig. 8

Fig. 9. Gas flow through a bubble.

S. Gerber et al. / Fuel 89 (2010) 2903–2917

1050

temperature [K]

vectors and gas phase volume fraction at the upper part of the bed. Inside the bubbles (red color) we see high velocities. The flow structure is such that the gas leaving one bubble changes it flow direction towards the next bubble. This leads to local zones with vanishing gas phase velocities between bubbles, i.e., in dense solid areas. A major part of the gas flow passes the bed through bubbles which is also the path with the least resistant. The figures also demonstrates the ability of Euler–Euler models to capture fundamental features of gas–solid flow. (The velocity vectors on the left wall pointing out of the domain and pretending a flux across a solid wall are an artifact of the visualization software which can display velocity vectors with data on the nodes of the grid only. However, the actual velocity data are located in cell centers of the (relatively coarse) grid representing cell averaged values which have been interpolated to the nodes of the grid.) Product gas concentrations at the outlet of the reactor versus time are shown in Fig. 10. At the beginning of the simulation the reactor is filled with pure nitrogen and the onset of gasification and pyrolysis reactions leads to a fast decreasing nitrogen concentration and increasing concentrations of all other gaseous components. At steady state conditions nitrogen oscillates around a value of 51.1%. The mole fractions of the main gasification products H2 and CO increase continuously until the simulation reaches a statistically steady state at approximately 180 s. The increase in these two gasification products goes along with a decrease in the main gasification agents H2O and CO2. The mean values at steady state for H2 and CO are 7.6% and 13.9%, respectively. The value of the CH4 mole fractions is more or less constant at 3.6% since the considered reaction kinetics for this gasification products is quite small. All mean values given before are averaged over the last 20 s of the simulation. Fig. 11 shows gas phase temperature data versus time at the outlet of the reactor. From the temperature plot is becomes more evident that the steady state is reach only after a relatively long time of approximately 180 s. Typically for a fluidized bed we observe a strong oscillating signal. Compared to the experimental data shown in Fig. 4 we observe higher amplitudes and a higher frequency of the signal. However, the inertia of the thermocouple acts as a high pass filter for the signal damping frequency and amplitude. The time-averaged temperature value for the last 20 s of the simulation is about 969 K. Both plots – outlet concentrations and temperature – use spatial averaged data at the outlet crosssection. The temperature plot highlights the inertia of a fluidized bed and the need for long real time simulations in order to get

1000 950 900 850 800

0

50

100

150

200

time [s] Fig. 11. Gas phase temperature versus time at the outlet of the reactor for the base case.

steady state results. We note that most results we found in the literature do not present simulation times much longer than 5 s of real time, see the cited literature in the introduction. On such short time scales one usually does not realize transients on long time scales such as observed here. Only with initial conditions already close to the steady state one can expect to reach statistically steady state results after short real time simulation times. Figs. 12 and 13 show the total amount of wood mass and charcoal masses within the reactor as a function of time. The wood mass in Fig. 12 reaches a statistically steady state after approximately 180 s. It can be observed from Fig. 13 that the total amount of charcoal mass does not reach a steady state within the 200 s of simulated real time, although the effect is quite small. The total mass of char with 2 mm diameter slightly increases whereas the

woodmass [g]

2912

0.45 0.40 0.35 0.30 0.25 0.20 0.15 0.10 0.05 0.00

wood mass

0

50

100

150

200

time [s] H2 CH4

N2 1.0

0.6 0.10 0.4 0.05

0.2

52.0

char coal mass [g]

0.8

0.15

mole fraction N2 [-]

0.20

mole fraction [-]

Fig. 12. Total amount of wood mass within the reactor versus time for the base case.

H2 O

CO CO2

51.5

char1, d=2 mm

51.0

char2, d=1.5 mm

50.5 50.0 49.5 49.0 48.5

0.00

0

20

40

60

80

0.0 100

time [s] Fig. 10. Product gas mole fractions versus time at the outlet of the reactor for the base case.

48.0

0

50

100

150

200

time [s] Fig. 13. Total amount of char masses within the reactor versus time for the base case.

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S. Gerber et al. / Fuel 89 (2010) 2903–2917

total mass of 1.5 mm char slightly decreases. During primary pyrolysis of wood the modeling approach converts wood into equal amounts mass of char with diameters of 1.5 mm and 2 mm. Having equal densities and a conversion rate proportional to the surface area one should expect higher conversion rates, i.e. less amounts of total mass, for the char class with the smaller diameter. However, Fig. 13 indicates the opposite. A possible explanation for this effect is a combination of segregation and a temperature stratification within the bed. Fig. 8 indicates some stratification with the smaller char particles tend to float on top of the bigger ones. However, the lower part of the reactor with exothermic oxidation reactions above the air inlet has a higher temperature level than the upper part of the reactor. The strong temperature dependence of the gasification reactions then leads to the effect observed in Fig. 13. However, for the base case under consideration here the char amounts within the reactor are the only variables which show a still transient behavior at the end of the simulation. This is due to much longer timescales of char conversion compared to gas phase reactions and pyrolysis reaction. To reach steady state conditions for char conversion within computationally reasonable times one would need to have already initial conditions already close to the steady state – which is certainly difficult to achieve. This is true for any physical or chemical process with characteristic timescale in the order or even longer than the simulation time. We remark that our presented char conversion model with only two char classes is certainly incomplete and not able to capture the real physics of a polydispers system in a detailed way. There might also be important char conversion paths we are not modeling at this point at all. As an example, Miccio et al. [58] states that a significant part of the carbon conversion of a gasification reactor occurs in the free board with char particles having diameters in the micrometer scale. We are currently investigating more sophisticated fluid mechanical models for the conversion of char within fluidized beds. Fig. 14 shows the tar concentrations in g=m3 (Norm) at the outlet of the reactor. Both the inert and the reactive tar reach a statistically steady state at the end of the simulation. During the transient heating up phase of the reactor we observe first a build up of inert and reactive tar during the first 20 s of the simulation which is then followed by degradation of the reactive tar into gaseous components when the temperatures within the reactor are high enough for an effective conversion by secondary pyrolysis. Table 13 summarized steady state concentrations of some components in the product gas for the base case and compares them with experimental data and a recently presented Euler–Lagrange model of the authors [20]. The results for the base case of the Euler–Euler model are for all concentrations within the spread of experimental data. In the Euler–Lagrange model we observe higher

tar content in g/Nm3

80

tarinert tar1

70 60 50

Table 13 Time-averaged product gas concentrations at the outlet of the reactor for the base case at steady state. EE = Euler–Euler, EL = Euler–Lagrange. The results for the Euler– Lagrange simulation are taken from [20] and the experimental data were obtained at our institute by Neubauer. X

CO CO2 H2 CH4

Experiment EL

Min

Max

0.139 0.158 0.076 0.036

0.18 0.13 0.10 0.04

0.13 0.13 0.07 0.02

0.21 0.17 0.11 0.06

CO than CO2 values. The measured temperatures at the outlet of the reactor were in a range between 759 and 921 K. This is less than the calculated mean value of 969 K. However, the parameter variations presented in the next section lead to averaged product gas temperatures in the range from 840 to 1260 K, see Table 15 for a summary. Averaged temperatures for the base case at different heights in the reactor are displayed in Table 14 together with some results of an Euler–Lagrange simulation [20] and experimental data. The Euler–Euler simulation has a temperature drop of approximately 150 K between 500 and 900 mm height in the reactor. This corresponds well with the observed temperature drop in the experiments. However, compared to the experimental data we see higher temperatures in the simulation and the temperature drop is located between 500 and 900 mm height whereas in the experiments it is observed at a higher position between 700 and 900 mm. The difference in the location of the temperature drop is probably due to the fact that we use only two solid char classes with 1.5 and 2 mm diameter leading to none or very little char particles in the upper part of the reactor. In the experiments we have a true particle size distribution with smaller char particles entrained in the freeboard and influencing the temperature above the bed. We are currently refining our model towards multiple solid char phases for a more realistic representation of the actual particle size distribution. In comparison to the Euler–Euler results the Euler–Lagrange simulation features lower temperature levels in the reactor and a temperature drop of approximately 90 K. 3.4. Results for parameter variations The presented model relies on several sub-models such as primary and secondary pyrolysis models, heterogeneous gasification reactions, or fluid mechanical properties in the Euler–Euler approach. Most of these models are still under scientific investigation on their own and it is a priory not obvious how they perform in a global model and how sensitive the global model is against variations in the sub-models. In the following sections we investigate the influence of some important parameters on the product gas composition. Taking the base case presented above as the reference solution to compare against we change only one parameter at a time.

Table 14 Time-averaged temperature data at different heights in the reactor for the base case at steady state. EE = Euler–Euler, EL = Euler–Lagrange. The results for the Euler– Lagrange simulation are taken from [20] and the experimental data were obtained at our institute by Neubauer.

40 30 20

Simulation

10 0

Simulation EE

0

50

100

150

200

time [s] Fig. 14. Tar content in the products gas versus time at the outlet of the reactor.

h = 500 mm h = 700 mm h = 900 mm

Experiment

EE

EL

Min

Max

1103 961 957

883 793 792

909 888 759

1070 K 1033 K 921 K

S. Gerber et al. / Fuel 89 (2010) 2903–2917

60 50

0.4

40

0.3

30

0.2

20

0.1

10

0.0

N2

H2

CH4 CO CO2 H2O tarinert tar1

tar2

tar content in g/Nm3

mole fraction

0.5

1300

0

Fig. 15. Product gas composition and tar content for secondary pyrolysis models 1 and 2.

3.4.1. Secondary pyrolysis model The first variation is not a parameter variation but we present here the results for the second secondary pyrolysis model. In Fig. 15 we compare product gas concentrations obtained with base case settings with results for the second secondary pyrolysis model. The figure shows the substantial influence of the secondary pyrolysis on the composition of the product gas. For example, the H2 concentration for the base case is more than twice the concentration obtained with the secondary pyrolysis model 2. Opposite to the base case model 2 produces higher CH4 than H2 concentrations. Compared to the gaseous components the influence of the secondary pyrolysis model on tar yields is weaker. The pyrolysis models share the same model for the primary pyrolysis leading to comparable levels of inert tar content in the product gas. According to Table 6 we get more tar2 than tar1 in model 2 which has in addition a lower activation energy for the degradation into gaseous components, i.e., a higher reactivity than tar1. This leads to lower tar2 yields than tar1 yields and to the almost complete conversion of tar2 into gaseous components. From the results of Fig. 15 it becomes apparent that the pyrolysis model is a critical sub-model. Given the modeling uncertainties and the huge parameter range of available pyrolysis kinetics found in the literature, see Di Blasi for a recent summary [38] , it becomes apparent that modeling pyrolysis is a critical issue for a global model and requires further investigation. 3.4.2. Variations of the thermal boundary conditions The kinetics for homogeneous gas phase reactions, pyrolysis, and heterogeneous gas phase reactions all exhibit a strong temperature dependence. Therefore, we expect a significant influence of the used temperature boundary condition – especially for the small laboratory scale reactor considered here. In order to investigate and quantify the influence of the temperature boundary condition on the product gas composition we perform simulations with prescribed temperatures and prescribed heat transfer coefficients (HTCs). In addition to an ideal adiabatic wall the chosen heat exchange coefficients are 0.4, 2, and 10 W=m2 K. Fig. 16 shows temperature data at the reactor outlet versus time for different heat transfer coefficients. The simulations with less heat transfer across the walls of the reactor do not reach a steady state within the 200 s of the simulation. This is mainly due to the fact that we did not change the initial conditions (bed and gas phase) in the reactor leading to relatively long transients. The graphs in Fig. 16 confirm the expected trend of higher temperatures with decreasing heat transfer coefficients and maximum temperatures for the adiabatic case.

temperature [K]

base case secondary pyrolysis model 2

1200

adiabatic

2 mW2K

1100

0.4 mW2K

10 mW2K

1000 900 800 700

0

50

100

150

200

time [s] Fig. 16. Temperature at the reactor outlet for adiabatic walls and different outer heat exchange coefficients (heat exchange coefficient in W=ðm2 KÞ).

With increasing temperatures within the reactor we expect higher reaction rates for pyrolysis, gasification, and homogeneous gas phase reactions. Under the gasification conditions investigated here we are in the fuel rich regime and the available oxygen from the supplied air is almost completely consumed at the first few centimeters of the reactor. Higher gasification rates with increasing temperature levels within the reactor can be observed via increasing concentrations of the gasification products CO, H2, and CH4 in Fig. 17. In comparison CO2 shows the opposite trend. This is related to the fact that the amount of CO2 from homogeneous gas phase reactions is limited by the available amount of supplied oxygen and that CO2 is consumed with increasing rates at higher temperatures in the second reaction of (10). The tars evolving from the primary pyrolysis show a more complex trend with changing heat transfer coefficient as depicted in Fig. 17. Tar1 is the reactive tar component which shows a lower contents with higher temperatures (lower heat transfer). For the adiabatic case and h = 0.4 W/ (m2K) we have almost no reactive tar in the product gas. This trend is obviously connected to the higher rates of the secondary pyrolysis reaction with increasing temperatures. The inert tar, however, shows a non-monoton behavior with variation of the heat transfer coefficient. For increasing heat transfer coefficients in the low and medium range we observe an increase of inert tar and a sharp drop for the highest value of h = 10 W/(m2K). This indicates a preference of the primary pyrolysis model towards gaseous components relative to tar for lower temperature levels and the other way around for higher temperature levels within the reactor. For the second approach of the testing the sensitivity of the global model with respect to variations in the thermal boundary condition we vary the prescribed wall temperatures by 50 K.

0.6

100 base case adiabatic

0.5

mole fraction

0.6

0.4

h = 0.4

W m2 K

h= 2

W m2 K

h = 10

0.3

80

60

W m2 K

40 0.2 20

0.1 0.0

N2

H2

CH4

CO

CO2

H2O

tar1 tarinert

tar content in g/Nm3

2914

0

Fig. 17. Gas composition and tar content for different heat fluxes compared to base case (heat exchange coefficient in W/(m2K)).

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S. Gerber et al. / Fuel 89 (2010) 2903–2917

0.6

60 base case base − 50 K Twall = Twall

50

base + 50 K Twall = Twall

40

0.3

30

0.2

20

0.1

10

0.0

N2

H2

CH4

CO

CO2

H2O

tar1 tarinert

0

Fig. 18. Gas composition and tar content for different boundary temperatures compared to base case.

The trend of higher gasification rates and therefore higher contents of the gasification products for higher boundary temperatures is obvious from Fig. 18. Higher boundary temperatures and the accompanying higher temperatures in the bed increase the heating rates for wood. In accordance with the observations above for the preference of inert tar versus gaseous components and char production in the primary pyrolysis step we see higher inert tar contents with increasing temperatures in Fig. 18. For the reactive tar from the secondary pyrolysis we obtain lower contents for higher temperatures. Higher temperatures mean higher tar conversion rates and therefore decreasing levels of reactive tar in the product gas. The influence of the wall temperature on the gaseous components in the product gas is relatively weak within the conducted variations.

3.4.3. Variations of the initial bed height Another parameter of interest is the initial bed height. In the variations we change the initial bed height by 25% in both directions. Product gas concentrations and tar yield are depicted for the base case and the variations in Fig. 19 whereas the influence of the initial bed height on the gaseous components in the product gas is relatively weak we observe a strong influence on the reactive tar component tar1. With decreasing bed height we have increasing temperatures in the reactor. This leads to slightly increasing levels of inert tar with decreasing bed height although the influence is small. In the pyrolysis models used in this study the secondary cracking of tar into gaseous components is modeled as a

0.6

3.4.4. Variations in fuel/air ratio and reactor throughput Varying the fuel to air ratio by changing the fuel mass flow by 29% results in the gas yields shown in Fig. 20. The higher the fuel input is the more products gases and tar evolve. This trend was expected for the product gases but not necessarily for tar as tar production rates show a higher temperature dependence than wood gas, see Table 4. Keeping the wall temperatures of the reactor and the supplied oxygen (for exothermic reactions) constant one should expect lower temperatures as the amount of available heat for heating up the wood is limited. However, Table 15 indicates a slightly higher temperature level with increasing fuel supply. Modeling pyrolysis as a heat neutral conversion process and heterogeneous gasification reactions as endothermic a potential explanation for this behavior could be a shift in the relative importance of the different homogeneous gas phase reactions. With increasing wood feeding rates we also increase the H2O mass stream entering the reactor through the wood inlet and for this reason the released heat by the slightly exothermic water gas shift reaction. Another aspect influencing the results is the thermal boundary condition. Keeping the wall temperatures independent of the wood feeding rate implicitly increases the effective heat flux. We are currently investigating the quantitative influence of these effects. The previous variations of different parameters generally show the trend of increasing inert tar and decreasing reactive tar yields with increasing temperatures. However, for the variations in the fuel inlet we see the influence of another parameter, namely the amount of available wood in the reactor. Table 15 shows increasing temperature levels with increasing fuel feeding rates. As expected,

60

0.6

70

base case hbed = 1.25 hbase bed hbed = 0.75 hbase bed

0.4

40

0.5

mole fraction

50

tar content in g/Nm3

0.5

mole fraction

base case

0.4

30

0.2

20

0.1

10

0.1

0

0.0

N2

H2

CH4

CO

CO2

H2O

tar1 tarinert

Fig. 19. Gas composition and tar content for different initial bed heights compared to base case.

9 7

m˙ base fuel

60

m˙ fuel =

5 7

m˙ base fuel

50 40

0.3

0.0

m˙ fuel =

0.3 30 0.2

20

tar content in g/Nm3

0.4

tar content in g/Nm3

mole fraction

0.5

pure gas phase process (the tar from the primary pyrolysis step is assumed to be gaseous). That means secondary pyrolysis is mainly influenced by the temperature level and the residence times of reactive tar in the gas phase of the reactor. A decreasing bed height leads to an increasing freeboard area and therefore to longer residence times in the gas phase above the bed. With decreasing bed height we also decrease the total char surface area leading to a lower total heat flux from the gas phase to support the endothermic char gasification reactions and therefore to higher temperature levels of the gas phase. Both effects support each other and lead to the strong influence of the bed height on the reactive tar content in the product gas as seen in Fig. 19. In addition the increasing temperatures with decreasing bed height lead to higher amounts of tar from primary pyrolysis (seen in the amount of inert tar in Fig. 19) which is converted into inert tar and gaseous components during the secondary pyrolysis step.

10 N2

H2

CH4

CO

CO2

H2O

tar1 tarinert

0

Fig. 20. Product gas compositions and tar contents for different fuel to air ratios compared to base case.

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S. Gerber et al. / Fuel 89 (2010) 2903–2917

Table 15 Product gas temperatures at the outlet of the reactor for all cases. The temperature values shown in the table are time-averaged data of the last 20 s of each simulation (t = 180  200 s). Case

Temperature in K

Base case Secondary pyrolysis model 2 T wall ¼ T base wall  50 K

969 981 933

T wall ¼ T base wall þ 50 K Adiabatic h ¼ 0:4 mW2 K

1260 1209

h ¼ 2 mW2 K

1071

1002

h ¼ 10 mW2 K

840 base

966

hbed ¼ 0:75 hbed _ base _ fuel ¼ 97 m m fuel

base

987

_ base _ fuel ¼ 57 m m fuel base _ through _ through ¼ 87 m m

957

base _ through ¼ 67 m _ through m

935

hbed ¼ 1:25 hbed

4. Conclusions

975 995

for the inert tar in Fig. 20 we observe higher yields for higher wood feeding rates and temperatures. For the reactive tar we also see increasing yields with increasing wood feeding rates. This shows the dominant effect of the available fuel for the tar yields over the influence of the accompanying temperature increase. Varying the throughput of the reactor while keeping the fuel to air ratio constant, i.e. changing the supply of wood and air supply at the same time, yields product gas concentrations and tar contents as shown in Fig. 21. Variations in the reactor throughput by 14% have an almost negligible influence on the product gas composition. This is quite reasonable as we do not change the wood to air ratio by increasing the throughput of the reactor and, in addition, the composition of the wood gas emerging from primary and secondary pyrolysis steps is fixed. For typical gasification conditions the composition of the product gas is mainly influenced by the production of wood gas in the primary and secondary pyrolysis step – the influence of the heterogeneous char reaction is small due to the comparably lower reaction rates. However, the exothermic oxidation of char has still an impact on the temperature level in the reactor. Looking at the product gas temperatures in Table 15 we see that changing the throughput has a noticeable influence on the temperature – with increasing throughput we get higher temperatures for the product gas. This can be traced back to the influence of the highly exothermic oxidation reaction of carbon (first reaction of (10)). Under (oxygen deficient) gasification conditions the bed material of char (plus a small amount of wood) is

0.6

60 base case

0.4

m˙ through =

8 7

m˙ base through

m˙ through =

6 7

m˙ base through

50 40

0.3

30

0.2

20

0.1

10

N2

H2

CH4

CO

CO2

H2O

tar1 tarinert

tar content in g/Nm3

mole fraction

0.5

0.0

effectively an unlimited fuel resource. Increasing the oxygen mass flux by increasing the throughput of the reactor increases the amount of released heat from the combustion/oxidation reaction of char leading to higher temperatures with higher throughput. The reaction rate of carbon oxidation shows an almost linear response to variations of the oxygen supply: varying the throughput by 14% we see a variation in the reaction rate for carbon oxidation of þ14% and 13%, respectively. Compared to the reaction rate of the carbon oxidation the sum of all other exothermic gasification reaction rates is more than two orders of magnitude smaller.

0

Fig. 21. Gas composition and tar content for different reactor throughput compared to base case.

In this article we presented an Euler–Euler modeling approach for the simulation of wood pyrolysis and char gasification in a bubbling fluidized bed reactor. Detailed models for homogeneous gas phase reactions, pyrolysis models, and heterogeneous gasification reactions were implemented. We investigated the influence of two different classes of parameters on product gas concentrations and temperature: (i) operating conditions such as initial bed height, wood feeding rate, and reactor throughput and (ii) model parameters like thermal boundary conditions, primary pyrolysis kinetics, and secondary pyrolysis model.We observed a strong and non-trivial influence of almost all parameters on the tar yield. Furthermore, the bed temperature is one of the most influential parameters whereas the initial bed height and the throughput have only a minor effect on the product gas concentrations. For the limited available experimental data we saw reasonable agreement between main product gas concentrations and the product gas temperature. Most studies with Euler–Euler or Euler–Lagrange models we found in the literature are limited to simulation times of about a few seconds. Although we performed our simulations for real times of 200 s we did not achieve steady state conditions for all parameter variations. For sensitivity studies it is quite advantageous to chose the initial conditions close to the steady state to minimize initial transients and compute times. Compared to most other studies we found in the literature we presented results for a bed material which consists of wood and char only. Char has reported to act as a catalyst capable of reducing tar. We will investigate this by comparative studies of the presented model with calculations of fluidized beds with inert sand, olivine, and limestone particles. For the investigated bed of char and wood we observed that heat release by oxidation of char is two orders of magnitude higher heat release via gas phase oxidation/combustion reactions. The presented results indicate the need for further research activities in several directions. Especially the pyrolysis model has been identified as a critical sub-model which influences the composition of the product gas quite significantly. Although we have testes only one model for the primary pyrolysis and two models for the secondary pyrolysis we observed already huge variations in the product gas compositions for both models. The available kinetic parameters in the literature for pyrolysis kinetics vary over many magnitudes. It has to be expected that they will lead to even bigger variations than the presented ones. Another aspect not taken into account in this study are catalytic effects of char and char ash on tar yields. Tar is one of the major problems in operating fluidized bed reactors and little is known about tar chemistry under such conditions. Good quantitative models for tar chemistry would be a substantial step forward for the predictive capabilities of numerical methods like the presented one.

S. Gerber et al. / Fuel 89 (2010) 2903–2917

Furthermore, the used modeling of a truly polydispers bed with only one wood and two char classes has severe limitations. We are currently improving the modeling using multiple solid phases with mass transfer between solid phases to model shrinkage. The results will be presented in forthcoming publication. Further research will also be targeted on detailed investigations of the fluid mechanic/ fluidization – chemistry interaction. Acknowledgments We gratefully acknowledge the Deutsche Bundestiftung Umwelt (DBU) and the Max-Buchner Stiftung for the financial support of this work through grants for S. Gerber. We thank York Neubauer for providing us the experimental data shown in this article. References [1] Jennen T, Hiller R, Kneke D, Weinspach P-M. Modeling of gasification of wood in a circulating fluidized bed. Chem Eng Technol 1999;22(10):822–6. [2] Hamel S, Krumm W. Mathematical modeling and simulation of bubbling fluidized bed gasifiers. Powder Technol 2001;120:105–12. [3] Ross DP, Yan H, Zhong Z, Zhang D. A non-isothermal model of a bubbling fluidised-bed coal gasifier. Fuel 2005;84:1469–81. [4] Li XT, Grace JR, Lim CJ, Watkinson AP, Chen HP, Kim JR. Biomass gasification in a circulationg fluidized bed. Biomass Bioenergy 2004;26(2):171–93. [5] Lathouwers D, Bellan J. Modeling and simulation of bubbling fluidized beds containing particle mixtures. Proc Combustion Inst 2000;28:2297–304. [6] Lathouwers D, Bellan J. Modelling of dense gas–solid reactive mixtures applied to biomass pyrolysis in a fluidized bed. Int J Multiphase Flow 2001;27:2155–87. [7] Lathouwers D, Bellan J. Yield optimization and scaling of fluidized beds for tar production from biomass. Energy Fuels 2001;15:1247–62. [8] Agrawal K, Loezos PN, Syamlal M, Sundaresan S. The role of meso-scale structures in rapid gas–solid flows. J Fluid Mech 2001;445:151–85. [9] O’Brien, TJ, Syamlal M, Guenther C. Computational fluid dynamics simulations of chemically reactive fluidized bed processes. In: 3rd international conference on CFD in the minerals and process industries. CSIRO, Melbourne, Australia; 2003. p. 469–74. [10] Xie N, Battaglia F, Fox RO. Simulation of multiphase reactive flows in fluidized beds using in situ adaptive tabulation. Combust Theory Model 2004;8:195–209. [11] Yu L, Lu J, Zhang X, Zhang S. Numerical simulation of the bubbling fluidized bed coal gasification by the kinetic theory of granular flow (KTGF). Fuel 2007;86:722–34. [12] Xi N, Battaglia F, Pannala S. Effects of two- versus three-dimensional computational modeling of fluidized beds, Part I, hydrodynamics. Powder Technol 2008;108:1–13. [13] Deng Z, Xiiao R, Jin B, Huang H, Shen L, Song Q, et al. Computational fluid dynamics modeling of coal gasification in a pressurized spout-fluid bed. Energy Fuels 2008;22:1560–9. [14] Rong D, Horio M. DEM simulation of char combustion in a fluidized bed. In: 3rd international conference on CFD in the minerals and process industries. CSIRO, Melbourne, Australia; 1999. p. 469–74. [15] Kaneko Y, Shiojima T, Horio M. DEM simulation of fluidized beds for gas-phase olefin polymerization. Chem Eng Sci 1999;54:5809. [16] Zhou H, Flamant G, Gauthier D. DEM-LES of coal combustion in a bubbling fluidized bed. Part I: gas-particle turbulent flow structure. Chem Eng Sci 2004;59:4193–203. [17] Zhou H, Flamant G, Gauthier D. DEM-LES of coal combustion in a bubbling fluidized bed. Part II: coal combustion at the particle level. Chem Eng Sci 2004;59:4205–15. [18] Limtrakul S, Boonsrirat A, Vatanatham T. DEM modeling and simulation of a catalytic gas–solid fluidized bed reactor: a spouted bed as a case study. Chem Eng Sci 2004;59:5225–31. [19] Gräbner M, Ogriseck S, Meyer B. Numerical simulation of coal gasification at circulating fluidised bed conditions. Fuel Process Technol 2007;88:948–58. [20] Oevermann M, Gerber S, Behrendt F. EulerGLagrange/DEM simulation of wood gasification in a bubbling fluidized bed reactor. Particuology 2009;7(4):307–16. [21] Papadikis K, Bridgewater AV, Gu S. CFD modelling of the fast pyrolysis of biomass in fluidised bed reactors, Part A: Eulerian computation of the momentum transport in bubbling fluidised beds. Chem Eng Sci 2008;63:4218–27. [22] Papadikis K, Bridgewater AV, Gu S. CFD modelling of the fast pyrolysis of biomass in fluidised bed reactors, Part B: Heat, momentum and mass transport in bubbling fluidised beds. Chem Eng Sci 2009;64:1036–45.

2917

[23] Papadikis K, Gu S, Bridgewater AV, Gerhauser H. Application of cfd to model fast pyrolysis of biomass. Fuel Process Technol 2009;90:504–12. [24] Papadikis K, Gu S, Bridgewater AV. CFD modelling of the fast pyrolysis of biomass in fluidized bed reactors: modelling the impact of biomass shrinkage. Chem Eng J 2009;149:417–27. [25] Sofialidis D, Faltsi O. Simulation of biomass gasification in fluidized beds using computational fluid dynamics approach. Thermal Sci 2001;5(2):95–105. [26] Wang Y, Yan L. CFD studies on biomass thermochemical conversion. Int J Mol Sci 2008;9:1108–30. [27] El-Rub ZA. Biomass char as in-situ catalyst for tar removal in gasification systems. PhD thesis, University of Twente, The Netherlands; 2008. [28] Hosokai S, Kumabe K, Ohshita M, Norinaga K, Li C-Z, Hayashi JI. Mechanism of decomposition of aromatics over charcoal and necessary condition for maintaining its activiy. Fuel 2008;87:2914–22. [29] Syamlal M, Rogers W, O’Brien T. MFIX Documentation – Theory Guide; 1993. . [30] Syamlal M. MFIX Documentation – Numerical Technique; January 2002. . [31] Benyahia S, Syamlal M, O’Brien TJ. Summary of mfix Eqs. 2005-4; 2007. . [32] Benyahia S, Syamlal M, O’Brien T. Summary of MFIX equations. National Energy Technology Laboratory; July 2007. . [33] Schaeffer DG. Instability in the evolution equations describing incompressible granular flow. J Differ Equations 1987;66:19–50. [34] Srivastava A, Sundaresan S. Analysis of a frictional-kinetic model for gasparticle flow. Powder Technol 2003;129(1–3):72–85. [35] De Souza-Santoz ML. Comprehensive modelling and simulation of fluidized bed boilers and gasifiers. Fuel 1989;68(12):1507–21. [36] Dryer FL. High Temperature Oxidation of CO and CH4 in a Turbulent Flow Reactor. PhD thesis, Princeton University, Aerospace and Mechanical Sciences Department, Princeton, New Jersey, USA; 1972. [37] Mitani T, Williams FA. Studies of cellular flames in hydrogen–oxygen–nitrogen mixtures. Combust Flame 1980;39:169–90. [38] Di Blasi C. Modeling chemical and physical procsesses of wood and biomass gasification. Progr Energy Combust Sci 2008;34:47–90. [39] Grønli MG.A theoretical and experimental study of the thermal degradation of biomass. PhD thesis, NTNU Trondheim, Trondheim Norway; 1996. [40] Nunn TR, Howard JB, Longwell JP, Peters WA. Product compositions and kinetics in the rapid pyrolysis of milled wood lignin. Ind Eng Chem Process Des Dev 1985;24:844–52. [41] Wurzenberger J, Wallner S, Raupenstrauch H. Thermal conversion of biomass and particle modeling. AIChE J 2002;48:2398–411. [42] Grønli MG, Melaaen MC. Mathematical model for wood pyrolysis-comparison of experimental measurements with model predictions. Energy Fuels 2000;14:791–800. [43] Larfeldt J, Leckner B, Melaaen MC. Modelling and measurements of the pyrolysis of large wood particles. Fuel 2000;79:1637–43. [44] Seebauer V. Experimentelle Untersuchungen zur Pyrolyse von Kohle und Holz. PhD thesis, Graz University of Technology; 1999. [45] Klose W, Wölki M. On the intrinsic reaction rate of biomass char gasification with carbon and steam. Fuel 2005;84:885–92. [46] Mermoud F, Salvador S, Van de Steene L, Golfier F. Influence of the pyrolyis heating rate on the steam gasification rate of large wood char particles. Fuel 2006;85:1473–82. [47] Boroson MJ, Howard J. Product yields and kinetics from vapor phase cracking of wood pyrolysis. AIChE J 1989;35:120. [48] Rath M, Staudinger G. Cracking reactions of tar from pyrolysis of spruce wood. Fuel 2001;80:1379–89. [49] Rath J, Wolfinger MG, Steiner G, Krammer G, Barontini F, Cozzani V. Heat of wood pyrolysis. Fuel 2003;82:81–91. [50] Neubauer Y, Behrendt F. Gasification of wood in a fluidized bed of char – influence of feed conditions on the tar content in the product gas. In: 17th European biomass conference and exhibition, Hamburg; 2009. [51] Ross IB, Davidson JF. The combustion of carbon particles in a fluidized bed. Chem Eng Res Des 1982;60a:108–14. [52] Hobbs ML, Radulovic PT, Smoot LD. Modeling fixed-bed coal gasifiers. AIChE J 1992;38(5):681–702. [53] Di Blasi C. Combustion and gasification rates in lignocellulosic chars. Progr Energy Combust Sci 2009;35:121–40. [54] Neubauer Y, Behrendt F. Application of laser mass spectrometry for a fast and detailed online tar analysis in biomass gasification, In: 15th European biomass conference and exhibition, Berlin; May 2007. p. 850–4. [55] Johnson PC, Jackson R. Frictional collisional constitutiv relations for the granular materials with application to plane shearing. J Fluid Mech 1987;176:67–93. [56] Ahuja P, Kumar S, Singh PC. A model for primary and heterogeneous secondary reactions of wood pyrolysis. Chem Eng Technol 1996;19:272–82. [57] Geldart D. Types of gas fluidization. Powder Technol 1973;77:285–93. [58] Miccio F, Moersch O, Spliethoff H, Hein K. Generation and conversion of carbonaceous fine particles during bubbling fluidised bed gasification of biomass fuel. Fuel 1999;78:1473–81.