1-s2.0-S0016236114010539-main

Fuel xxx (2014) xxx–xxx

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Fuel

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Towards a hybrid Eulerian–Lagrangian CFD modeling of coal gasificationin a circulating fluidized bed reactor

http://dx.doi.org/10.1016/j.fuel.2014.10.0580016-2361/� 2014 Elsevier Ltd. All rights reserved.

⇑ Corresponding author. Tel.: +48 32 2372974; fax: +48 32 2372872.E-mail address: [email protected] (A. Klimanek).

Please cite this article in press as: Klimanek A et al. Towards a hybrid Eulerian–Lagrangian CFD modeling of coal gasification in a circulating fluidizreactor. Fuel (2014), http://dx.doi.org/10.1016/j.fuel.2014.10.058

Adam Klimanek ⇑, Wojciech Adamczyk, Anna Katelbach-Wozniak, Gabriel Wecel, Andrzej SzlekInstitute of Thermal Technology, Silesian University of Technology, Konarskiego 22, 44-100 Gliwice, Poland

h i g h l i g h t s

�We developed a CFD model of coal gasification in a circulating fluidized bed reactor.� Eulerian–Lagrangian approach was used to simulate the fluidized bed hydrodynamics.� Gasification with air and air/steam mixture was considered.� Fourteen heterogeneous and homogeneous reactions were considered.� Results of the simulations coincide well with the measured syngas composition.

a r t i c l e i n f o

Article history:Received 28 July 2014Received in revised form 10 October 2014Accepted 22 October 2014Available online xxxx

Keywords:Coal gasificationCirculating fluidized bedEuler–LagrangeDDPM

a b s t r a c t

Numerical model of coal gasification in circulating fluidized bed (CFB) using Eulerian–Lagrangianapproach is presented in this paper. The Dense Discrete Phase Model (DDPM) model of ANSYS FLUENTis used to simulate the flow of the particulate phase in the coal gasifier. The coal particles, with a sizedistribution, are tracked in the fluid velocity field including coupling between the phases. Kinetic Theoryof Granular Flow is utilized to model the particles’ interactions. The analyzed CFB comprises a small scaleexperimental facility in which coal is gasified in air and air/steam mixture. The reactor is composed of abarrel like bottom part with developed internal recirculation of the solid phase and 3.74 m high riser sec-tion. The homogenous gas phase reactions are modeled using the finite rate and eddy dissipation models.The heterogeneous reactions on coal particles surface are modeled using the finite rate chemistry. A totalnumber of 14 reactions are considered. Results of the simulations were compared with experimentaldata.

� 2014 Elsevier Ltd. All rights reserved.

1. Introduction

Circulating fluidized beds (CFBs) are frequently used in industryfor variety of processes, for example combustion, gasification,heterogeneously catalyzed reactions, etc. Modeling of the CFBs ischallenging due to complexity of the hydrodynamic behavior ofthe particle laden flow, which is further augmented by the heatand mass transfer as well as multiple species and reactions occur-ring in reactors and boilers. The fluidized medium is composed ofparticles usually of various sizes and frequently of various materi-als, e.g. coal and sand. Modeling of industrial CFBs by means ofcomputational fluid dynamics tools faces further challenges dueto large geometrical scales of these facilities and inherent longcomputational times of these transient flows. Several approaches

can be used to model gas-particle flows. The methods differ bythe covered spatial and temporal scales of the flow phenomena[1]. The associated computational effort increases quickly withincreasing spatial and temporal resolution of the models andtherefore not all approaches can be applied to pilot scale and largeindustrial facilities, which however depends on the availablecomputational resources. The mathematical models coveringscales larger than the particle size, namely the Eulerian–Eulerian(multi-fluid) and hybrid Eulerian–Lagrangian are nowadays mostfrequently used for this purpose. In the Eulerian–Eulerian approachboth the solid and the fluid phase are treated as interpenetratingcontinua. Use is made of the Kinetic Theory of Granular Flow(KTGF) ([2,3]) which allows determination of the solid stressesby many closure terms in the set of governing equations of themodel. The solid phase is represented by its density and a charac-teristic diameter. In reacting flows frequently the particle size dis-tribution (PSD) is of importance. If the PSD needs to be taken into

ed bed

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5.46

0.97

3.74

0.38

0.14

0.22

0.2

gasifying agent inlet

coalinlet

syngas outlet

0.65

Fig. 1. Geometry of the model (dimensions in meters).

2 A. Klimanek et al. / Fuel xxx (2014) xxx–xxx

account, a couple of solid phases of the same medium with distinctrepresentative diameters can be included. This however increasessubstantially the computational effort with increasing number ofphases. An alternative approach is to apply one of the methodsof moments QMOM ([4]) or DQMOM ([5]). The Eulerian–Eulerianapproach is frequently applied to gas–solid flow and specificallyto circulating fluidized beds modeling of small and medium scalefacilities [6–12]. Due to tremendous computational resourcesrequired to simulate large industrial facilities, there are only afew reports of this modeling approach in the literature [13]. Inthe hybrid Eulerian–Lagrangian approach groups of particles(parcels) are tracked in a Lagrangian frame of reference. The parti-cle–particle interactions are not simulated directly, as in discreteelement method (DEM), but are accounted for by means of KTGFin the Eulerian frame, where the particles are projected. Animportant feature of the approach is that the particle size distribu-tion is naturally incorporated in the model. The hybrid Eulerian–Lagrangian approach is used in this study to simulate gasificationof coal in a circulating fluidized bed reactor. The coupling betweenthe complex hydrodynamics of the fluidized bed and the chemicalreactions is realized within the commercial code ANSYS FLUENT14.0. The Eulerian–Lagrangian model in this code is called DenseDiscrete Phase Model (DDPM). The DDPM approach is still underdevelopment and just a few studies applying this method can befound in the literature [14–18]. In this study we apply it for gasifi-cation modeling, where the mechanisms of heterogeneous andhomogeneous reactions are included. An alternative Lagrangianapproach is the multi-phase-particle-in-cell (MP-PIC) method[19–21], which has recently been also applied to modeling of coalgasification in a large bubbling bed reactor [22].

2. The numerical model

2.1. Geometry and mesh

The modeled reactor is installed at the Institute for ChemicalProcessing of Coal in Poland [23] where measurements of the ana-lyzed cases have been done. The reactor has been used in the pastfor coal pyrolysis and gasification and recently it has been pro-posed to apply it to gasification with carbon dioxide [24–26]. Thereactor encompasses a barrel like bottom part, where internalrecirculation of the solid phase develops, a riser section and anexpanding section at the top from which the gases are directedto the solids separator. Geometry of the model with its maindimensions is presented in Fig. 1 and more details of the test facil-ity can be found in [26,27]. Coal is introduced at the side of the bar-rel part and the gasifying agent at the very bottom of the reactorthrough a grid inlet. The unreacted char can be circulated to thebarrel like part of the reactor, however in the investigated casesit was not. Two meshes were generated and examined: mesh 1built of 53.7 thousand elements and mesh 2 composed of 90.8thousand elements. Both quadrilateral and tetrahedronal elementswere used to create the mesh. Simulations were done for bothmeshes to verify the sensitivity of the results to the mesh density.

2.2. Governing equations

The DDPM formulation is based on governing equations similarto those of the multi-fluid approach. Additionally, the particleequation of motion is solved for each parcel and the parcelproperties are projected to the Eulerian grid. This provides meansolids velocity and volume fraction in each grid cell. Thereforethe continuity and the momentum equations of the solid phaseare not solved in the Eulerian frame. The particle–particle interac-tions are determined by the KTGF on the Eulerian grid and are

Please cite this article in press as: Klimanek A et al. Towards a hybrid Eulerian–reactor. Fuel (2014), http://dx.doi.org/10.1016/j.fuel.2014.10.058

introduced back to the particle equation of motion as a source.The continuity, momentum, energy and species transport equa-tions for the gaseous phase f are given by Eqs. (1)–(4)

@

@tef qf

� �þr � ef qf uf

� �¼ Smas ð1Þ

@

@tef qf uf

� �þr � ef qf uf uf

� �¼ �efrpþr � sf þ ef qf g

þ KDPM us � uf� �

þ Smom ð2Þ@

@tef qf hf

� �þr � ef qf uf hf

� �¼ ef

@p@tþ sf : ruf �r � qf

�r � ef

Xm

k¼1

hf ;kJk

" #þ Sf ;rad þ Sf ;rea þ Sen ð3Þ

@

@tef qf Yf ;k

� �þr � ef qf uf Yf ;k

� �¼ r � ef Jk þ ef Rf ;k þ Ssp ð4Þ

where q is the density, e is the volume fraction, u is the velocity vec-tor, p is the pressure, sf is the shear stress tensor, g is the gravita-tional acceleration vector, s is the solid phase index and KDPM isthe interphase exchange coefficient due to drag calculated for solidsvolume fraction in the Eulerian frame. h is the specific enthalpy, k isthe species index, Yf ;k stands for the mass fraction of species k;m isthe number of species in the gaseous phase, Jk is the diffusion flux ofspecies k and q is the heat flux. The source term Sf ;rad accounts forthe contribution to the energy equation due to radiation and Sf ;rea

defines the energy released in homogeneous reactions. The Rf ;k

source represents the net rate of production of homogeneous spe-cies k. The Smas; Smom; Sen and Ssp are sources due to exchange ofmass, momentum, energy and species between the continuousphases and the discrete phase, respectively. The energy source Sen

includes the enthalpy transfer due to convection, chemical reactionsand radiation between the phases. The momentum source termSmom determines the exchange of momentum between the phases.The set of Eqs. (1)–(4) is supplemented with the particle equationof motion given by Eq. (5). The particle equation of motion which

Lagrangian CFD modeling of coal gasification in a circulating fluidized bed


Table 2Measured and included in the simulation volatiles species.

Measured Mole fraction Included Mole fraction

CH4 0.176 CH4 0.176CO2 0.036 CO2 0.036CO 0.077 CO 0.077H2 0.385 H2 0.385H2O 0.172 H2O 0.172H2S 0.012 TAR 0.154NH3 0.047C2H6 0.043C6H6 0.040C10H8 0.012

A. Klimanek et al. / Fuel xxx (2014) xxx–xxx 3

equates the particle inertia with the forces acting on a particle,reads

dup

dt¼ FDðuf � upÞ þ

gðqp � qf Þqp

�rpqp�r � rs

qpþ aother ð5Þ

where subscript p denotes the particle properties in the Lagrangianframe, qp is the particle density, rs is the solids stress tensor due toparticle–particle interactions predicted by the KTGF and FDðuf � usÞis the particle acceleration due to drag. Both the solid stress tensoras well as the drag coefficient FD are calculated in the Eulerianframe. The term �rp=qp defines the particle acceleration due topressure gradients and aother represent acceleration due to addi-tional forces that can be included, e.g. virtual mass force, lift force,etc. It should be stressed here that the acceleration due to pressuregradients and due to additional forces contribute to the source termSmom in the momentum Eq. (2). Furthermore, the drag coefficient FD

and the interphase exchange coefficient KDPM are determined fromthe same drag law in the Eulerian grid.

Application of the KTGF for the particle–particle interactionsrequires many closure models which are implemented in theANSYS FLUENT code. These closures are presented in detail in thesoftware documentation ([28]) and have been widely describedin the literature ([10,7,12]), therefore they are not presented here.Instead the submodels used within this study are presented inTable 1 and for each model references to the literature are given.The Standard k—� model with mixture option ([28]) was used asthe turbulence model and the Discrete Ordinates ([28]) model withweighted sum of gray gases model for the gas absorption coeffi-cient was used to take into account the radiative heat transfer.

2.3. Gasification modeling

A step by step procedure is applied in the coal utilization pro-cesses. The injected coal particles are first heated up and drieduntil all moisture is removed. Then devolatilization starts whichis described by a constant rate model. This is followed by multiplesurface reactions on the remaining char. The composition of vola-tiles is known from coal analysis and it is assumed that it does notchange during the devolatilization. A VOL pseudo-species is intro-duced which is released in the devolatilization process and decom-poses quickly afterwards in a fast volumetric reaction to form thefinal mixture of species forming the known from measurementsvolatiles, as shown in Eq. (6)

VOL! aCH4 þ bCO2 þ cCOþ dH2 þ eH2Oþ f TAR ð6Þ

The product species can take part in homogeneous and heteroge-neous reactions. Additionally to the VOL, a pseudospecies calledTAR is introduced to the gas phase mixture. TAR is composed of

Table 1Closure models and parameters used in the simulations.

Granular viscosity Gidaspow et al. [29]Granular bulk viscosity Lun et al. [2]Frictional viscosity Schaeffer [30]Angle of internal friction 30�Frictional pressure Based on KTGF, [28]Frictional modulus Derived, [28]Friction packing limit 0.61Granular temperature Algebraic equation, [28]Solids pressure Lun et al. [2]Radial distribution Lun et al. [2]Elasticity modulus Derived, [28]Packing limit 0.63Drag model Gidaspow et al. [29]Restitution coefficient 0.9Normal discrete phase reflection coefficient 0.8Normal discrete phase reflection coefficient 0.5


devolatilization products known from coal analysis, whose amountis relatively small and do not contribute to the main speciesconcentrations substantially. Such an approach has been utilizedto reduce the number of transported species in the complexunsteady multiphase reacting flow in the reactor. It should bestressed that the contribution of the minor species can be easilyincluded in further analyzes. The measured and included in the sim-ulations devolatilization product species are presented in Table 2.

Further species taken into account in the simulation are O2 andN2. The volumetric reactions are modeled by means of finite rate/eddy dissipation model ([28]) with a general rate expression forthe finite rate model of the form

RV ¼ A expð�Ta=TÞYn

i¼1

½ci�gi ð7Þ

where A is the preexponential factor, Ta is the activation tempera-ture, n is the number of species affecting the reaction, ½ci� is themolar concentration of species i expressed in mol=m3 and gi isthe reaction order with respect to i. The constants for the volumet-ric reactions are given in Table 3, where the volumetric reaction rateRV is given in kmol/m3s.

The reaction rates are taken from ANSYS FLUENT database [28]besides the water–gas shift reaction which is taken from [31]. Thechar surface reaction rates are calculated using the user definedfunction (UDF) mechanism from general rate expression given by

RS ¼ ATb expð�Ta=T � aÞYn

i¼1

½ci�gi mpXch ð8Þ

where mp is mass of coal contained in a grid cell volume, Xch is thechar mass fraction. The constants for the surface reactions are givenin Table 4, where the reaction rate RS is given in kg/s. For reaction 1in Table 4 the coal mass mp in Eq. (9) shall be replaced with the ratiomp=qp, where qp is the material density of the solids (coal). The rateexpressions for the heterogeneous reaction 1 is taken from [32] andreactions 2 through 7 are taken from [33]. Since the reactions andtheir rates in the selected reaction set come from various literaturesources, they do not comprise a consistent reaction mechanism. Anappropriate approach would be to use a reduced mechanism basedon global reactions with rate expressions fitted by means of mea-sured data. Such a reduced mechanism would be appropriate for agiven system and conditions, therefore its applicability would belimited. On the other hand the importance of reaction kinetics isincreasing in states far from equilibrium, usually fast changingand low residence times. It is expected that the outlet gas composi-tion is close to equilibrium and the inconsistent reaction mecha-nism will have a minor contribution. It should be stressed herethat much more detailed and complex coal devolatilization, charconversion and homogeneous reactions approaches exist and havealready been applied to coal gasification modeling. An example ofsuch analysis with application to entrained flow gasification model-ing is given in [34,35]. Such an approach at the current stage of



Table 3Rate constants of the considered homogeneous reactions.

Reaction A Ta , K g1 g2 g3

1 COþ H2O! CO2 þ H2 2.75e+6 10,055 0.5 1 –2 CO2 þH2 ! COþ H2O 1.04e+8 14,010 1 0.5 –3 COþ 1=2O2 þ H2O! CO2 þH2O 2.24e+12 20,086 1 0.25 0.54 H2 þ 1=2O2 ! H2O 9.87e+8 3728 1 1 –5 CH4 þ 2O2 ! CO2 þ 2H2O 2.12e+11 24,380 0.2 1.3 –

Table 4Rate constants of the considered heterogeneous reactions.

Reaction A, units vary Ta , K a b g1 g2

1 Cþ 1=2O2 ! CO 1.762e+6 13,587 0 1 1 –2 Cþ CO2 ! 2CO 76.31 22,645 0 1 1 –3 2CO! Cþ CO2 6.262e�3 2363 20.92 2 2 –4 Cþ H2O! COþ H2 76.31 22,645 0 1 1 –5 COþ H2 ! Cþ H2O 6.262e�3 6319 17.29 2 1 16 Cþ 2H2 ! CH4 8.206e�2 8078 7.087 1 1 –7 CH4 ! Cþ 2H2 9.058 13,578 0.372 0.5 0.5 –

Table 5Input data used as boundary conditions.

Case 1 Case 2

Gasifying agent Air Air/steamCoal flowrate, kg/h 171 181Coal inlet temperature, K 288 288Air flowrate, kg/h 193 233Steam flowrate, kg/h – 18.3Gasifying agent inlet temperature, K 520 510

Coal (ar)HHV, MJ/kg 28.1 28.1Char, % 48.0 55.1Volatiles, % 34.1 27.7Ash, % 12.6 10.6Moisture, % 5.3 6.6

Fig. 2. Measured and Rosin–Rammler fit of the PSD.

Fig. 3. Instantaneous (left) and mean (right) solids volume fraction.

m/s m/s

Fig. 4. Instantaneous (left) and mean (right) gas phase velocity magnitude.


model development could not be used here due to associated longcomputational times.

2.4. Solution procedure

The governing equations are solved as unsteady using phasecoupled simple algorithm. The solution procedure is started witha gas flow only and the coal particles are later subsequentlyinjected. As the outlet flow variables and mass of coal in the reactorachieve a pseudo-steady state in which the flow parameters vary


about a stable mean, data are collected for computing flow param-eter averages.

3. Analyzed cases

Two case studies have been analyzed in which coal was gasifiedin air (case 1) and air/steam mixture (case 2). As mentioned earliertwo meshes were used in the simulations. The mesh independencehas been done for both analyzed cases. In Table 5 the input data forcoal and gasifying agent used in the simulations are presented. Theinput data, as well as other experimental results compared later



K K

Fig. 5. Instantaneous (left) and mean (right) gas phase temperature.


with the simulations, have been obtained from the Institute forChemical Processing of Coal in Poland [23]. The details regardingthe experimental facility and measurement procedure can befound in [26].

Coal particles size distribution has been measured and wasfitted by means of the Rosin–Rammler distribution

Yd ¼ expð�ðd=�dÞnÞ ð9Þ

where d is the particle diameter, �d is the size constant, n is thedistribution parameter and Yd is the mass fraction of particles ofdiameter greater than d. The size constant and distribution param-eter were determined by fitting the measured PSD. The constantsare: �d ¼ 1:197 mm and n ¼ 0:895. With this information the PSDof particles can be specified at the boundary for injected coal parti-cles. In Fig. 2 the measured and fitted particle size distribution ispresented.

CO CO2

Fig. 6. Mass fraction distribution of g


4. Results and discussion

As mentioned earlier the simulations were run unsteady until apseudo-steady state was obtained. After this point collection ofdata for obtaining averages of flow parameters started. The averag-ing times in the analyzed cases were as long as 90 s of real timeflow, however it was noticed that averaging times as short as20–30 s were required to obtain meaningful means. In Figs. 3–7sample results of the obtained flow field variables are presented.As can be seen in Fig. 3 the highest concentration of solids is atthe bottom in the barrel like part of the reactor. Only a smallamount of solids can be found in the riser tube and some of the sol-ids are trapped in the expanding section at the very top. It wasobserved that most of coal particles remain at the bottom whereinternal recirculation of solids was visible. As the particles aresucked to the riser section they will leave the reactor due to highvelocity in this part (cf. Fig. 4 or a small portion of them will betrapped in the expansion. It was observed that some of the parti-cles remained inside the reactor for very long time and their resi-dence times were of the order of simulation time. In Fig. 5 thetemperature of the gas phase is presented. As can be seen the pre-dicted gas phase temperature is relatively low at the bottom part ofthe reactor and increases to approximately 1150 K at the top,nonetheless all oxygen is consumed immediately after enteringthe reactor, cf. Fig. 7. In Figs. 6 and 7 the gas phase species massfractions have been presented. As can be seen carbon monoxideis formed in the dense bottom part of the bed, as could beexpected, and then is partially converted to carbon dioxide throughthe combustion reaction, due to still available oxygen at the bot-tom, and water gas shift reaction, due to available steam andincreasing temperature. The highest concentrations of methane,hydrogen and steam occur in the regions of highest devolatiliza-tion intensity, specifically in the center of the reactor where theinjected coal particles stream passes through.

In Figs. 8 and 9 results of measurements and simulations for airand air/steam gasification have been compared, respectively. Themeasurements are given with error bars for O2;H2;CO;CO2 andCH4. The measurement error is not available for tar and since N2

was used to close the mass balance, errors for these two speciesare not presented. As can be seen in general good agreementbetween the data has been obtained. It should be however stressedthat our first simulations led to results presented in 8 and namedsimulation for which CO and CO2 mole fractions depart considerably

CH4

aseous species: CO, CO2 and CH4.



H2 H2O O2

Fig. 7. Mass fraction distribution of gaseous species: H2;H2O and O2.


from the measured data. This is attributed to the application of thefinite rate/eddy dissipation model for which the reaction rates arepredicted by both, kinetic rate and turbulent rate. The final rate isthe smaller of the two. This leads to departure from expected closeto equilibrium conditions at the outlet of the reactor. To obtainappropriate CO=CO2 ratio at the outlet the water gas shift reactionrate has been artificially increased by a factor of 102, which leadto the results presented in Figs. 8 and 9 named simulation – modifiedwgs. As the model will be further developed it is planned to use adifferent approach to properly predict the conditions at the gasifieroutlet. The results presented in Figs. 3–7 correspond to the airgasification with the modified water gas shift reaction rate. Ascan be seen the predicted TAR pseudo-species mass fraction ishigher than the measured values in both analyzed cases. The reasonfor that may be the fact that the released TAR could react with oxy-gen only. Since in the upper part of the reactor there is no oxygenavailable and TAR cracking reaction has not been implemented tothe model, almost no conversion of the TAR occurs. Furthermoreit can be also observed that the mole fractions of nitrogen predictedin case simulation and simulation – modified wgs of Fig. 8 are differ-ent although these are the same cases for which, with the same car-bon conversion, nitrogen mole fractions should be the same. The

N2 O2 H2 CO CO2 CH4 tar0

0.1

0.2

0.3

0.4

0.5

0.6experimentsimulationsimulation−modified−wgs

Fig. 8. Comparison of measured and calculated syngas composition for airgasification.


carbon conversions are however different. It was observed duringthe simulations that the relatively large time step used in the sim-ulations (Dt ¼ 0:02 s) is too large for the fastest heterogeneousreaction of partial oxidation of carbon (reaction 1 in Table 4). Inthe solution procedure the rate of heterogeneous reactions isevaluated and maintained throughout the time step. This can leadto situations in which all substrates are consumed within the timestep and more products are formed than possible. In case of partialoxidation of carbon it is possible that more CO is formed than avail-able O2 would allow. This leads to increased concentration of CO,decreased concentration of N2 and higher carbon conversion. Forthe case named simulation the carbon conversion, defined as theratio of carbon mass in the gas to carbon mass supplied in fuelwas c ¼ 0:53. To overcome this problem a smaller time step couldbe used. This would however lead to a substantial increase of theoverall simulation time. Another possibility is to control thereaction rate based on the amount of available oxygen in the cellvolume within a time step. If the calculated reaction rate, withina time step, would lead to consumption of more oxygen thanavailable, the reaction rate is reduced to a level corresponding toavailable oxygen in a single computational cell. Such a control pro-cedure has been implemented to the CFD code by means of the user

N2 O2 H2 CO CO2 CH4 tar0

0.1

0.2

0.3

0.4

0.5

0.6

experimentsimulation−modified−wgs

Fig. 9. Comparison of measured and calculated syngas composition for air/steamgasification.




defined function mechanism and applied in cases named simulation– modified wgs of Figs. 8 and 9. The carbon conversions for air gas-ification (case simulation – modified wgs) was c ¼ 0:35 and for steamgasification was c ¼ 0:41. It should be stressed that in some regionsof the simulation domain the reaction rate for carbon partial oxida-tion is smaller than that resulting from the chemical kinetics, whichcan lead to slight extension of the oxidation zone at the bottom ofthe reactor visible in Fig. 7. Application of this procedure led toprediction of nitrogen mole fraction close to the measured valuefor air gasification. For air/steam gasification the predicted N2 molefraction is slightly higher than measured which suggests smallercarbon conversion than in the experiment. This issue will be exam-ined in our future work.

5. Conclusions

CFD model of coal gasification in a fluidized bed reactor hasbeen presented in the study. The model development can bedivided into to two parts. First part is devoted to appropriate mod-eling of the fluidized bed hydrodynamics and the second to thereacting solid and gaseous phases. Coupling of the two parts ischallenging and essential to obtain meaningful results. The DenseDiscrete Phase Model of ANSYS FLUENT has been used to simulatethe flow of the particulate phase in the coal gasifier. Kinetic Theoryof Granular Flow has been utilized to model the particle–particleinteractions. The heterogeneous reactions on coal particles surfaceare modeled using the finite rate chemistry (multiple surface reac-tions). Homogenous gas phase reactions have been modeled usingthe finite rate/eddy dissipation approach which led to departure ofthe gas composition from the measured values. This problem hasbeen overcome by artificial increase of the water gas shift reactionrate, however a different approach to the gas phase chemistryshould be applied. It has been also observed that the applied timestep led to wrong prediction of carbon conversion and thus differ-ent than measured nitrogen and carbon monoxide mole fractions.This issue has been addressed by application of a control proce-dure, which allowed avoiding this behavior in case of air gasifica-tion. The difference for the case of air/steam gasification are stillvisible. Since the model is under development these problemsshould be addressed in our future research.

Acknowledgements

The investigations have been supported by the National Centerfor Research and Development (NCBiR) as a research projectDeveloping a technology of coal gasification for highly efficientproduction of fuels and electric power, CzTB 5.2. The support isgratefully acknowledged.

The research have been supported by Research and Develop-ment Strategic Program ‘‘Advanced Technologies for Energy Gener-ation’’ project No.2 ‘‘Oxy-combustion technology for PC and FBCboilers with CO2 capture’’, supported by the National Centre forResearch and Development, agreement No. SP/E/2/66420/10

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