A downdraft high temperature steamonly solar gasifier of biomass char: A modelling study

Energy Conversion and Management 108 (2016) 120–131 Contents lists available at ScienceDirect Energy Conversion and Management journal homepage: www.elsevier.com/locate/enconman Model development for biomass gasification in an entrained flow gasifier using intrinsic reaction rate submodel Xiaoyan Gao a, Yaning Zhang a,b,⇑, Bingxi Li a,⇑, Xiangyu Yu a a b School of Energy Science and Engineering, Harbin Institute of Technology, Harbin, China School of Chemical Engineering and Technology, Harbin Institue of Technology, Harbin, China a r t i c l e i n f o Article history: Received 30 June 2015 Accepted 28 October 2015 Keywords: Model development Intrinsic reaction rate model Biomass gasification Entrained flow gasifier a b s t r a c t Intrinsic reaction rate submodel is established in this study to consider the effects of diffusion rate and kinetic rate for simulating the char reactions due to their slow reaction rates and important controlling steps The biomass gasification model for an entrained flow gasifier is developed with the Euler–Lagrange method using ANSYS FLUENT software Gas phase is treated as continuous phase in standard k–e model to close governing equations whereas biomass particles are treated as discrete phase in discrete phase model (DPM) to track the movement of particles For homogeneous phase reactions, finite rate/eddy dissipation model is applied to calculate the reaction rates For heterogeneous phase reactions, intrinsic reaction rate model is realized by coding the user-defined functions (UDFs) to calculate char reaction rates The results obtained from this study show that the relative errors of volumetric concentrations are mainly within the range of 1–18% and the relative errors of lower heating value, gas production, cold gas efficiency and carbon conversion efficiency are within the ranges of 1–13%, 1–8%, 1–12% and 1–11%, respectively The CFD model developed in this study can be used to simulate biomass gasification processes for entrained flow gasifiers Ó 2015 Elsevier Ltd All rights reserved Introduction Energy shortages and pollution emissions are still the problematic issues all over the world, and the threats of resource exhaustion and environment pollution stress the need for exploring new energy resources Biomass resource, an abundant resource on the earth, is therefore becoming an important alternative for the world [1–3] According to the Annual Energy Outlook 2014 (AEO2014) released by the U.S Energy Information Administration (EIA), biomass power generation would grow with the increased use of cofiring technology in the near term and it would grow with the increased capacities of power plants in the long run As a result, the electricity generation from biomass would increase significantly with an estimated average annual growth rate of 4.4% from 2012 to 2040 [4] Entrained flow gasification is an important thermochemical conversion method to convert solid fuels into high value gaseous fuels and chemical products [1,5,6], even on a small scale because it is capable of gasifying any fuels to produce a ⇑ Corresponding authors at: School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, China Tel./fax: +86 451 86412078 E-mail addresses: ynzhang@hit.edu.cn (Y Zhang), libx@hit.edu.cn (B Li) http://dx.doi.org/10.1016/j.enconman.2015.10.070 0196-8904/Ó 2015 Elsevier Ltd All rights reserved clean and almost tar-free syngas in a short residence time [7,8] Entrained flow gasification is therefore widely studied and used [5,9–11] Fig illustrates the working principle of a typical downdraft entrained flow gasifier Generally, biomass fuels and gases are introduced from the top of the reactor, the biomass particles then mix with gases thoroughly through the reactor From the outlet at the bottom of the gasifier, the produced gas and ash exit Computational fluid dynamics (CFD) is a useful tool for designing gasifiers, predicting performances and optimizing structures [1,12,13] A CFD model can offer both spatial and temporal field solutions of temperature, velocity, pressure, etc., and it is therefore able to provide the predictions of operating performances as well as gasification mechanisms inside a gasifier [14,15] Several researchers developed mathematical models for simulating biomass gasification in entrained flow gasifiers Kobayashi et al [16] built a simple thermodynamic equilibrium model for biomass gasification in an entrained gasifier Coda et al [17] took the slagging/ melting tendencies into account and then built a thermodynamic equilibrium model Valero and Usón [18] divided gasification process into two isothermal zones and developed a model for a pressurized entrained flow gasifier Fletcher et al [19] developed a CFD model to study the flow and reactions inside an entrained flow X Gao et al / Energy Conversion and Management 108 (2016) 120–131 121 Nomenclature A, B DT,i dp Magnussen constants for reactants and products; pre-exponential factor absorption coefficient absorption coefficient of particle linear-anisotropic phase function coefficient k–e model constants molar concentration of species j in r reaction (kmol mÀ3) vapor concentration at particle surface and in the bulk gas (kmol mÀ3) swelling coefficient heat capacity (J kgÀ1 KÀ1) effective diffusion coefficient (m2 sÀ1) molecular diffusion coefficient of gas component i (m2 sÀ1) mass diffusion coefficient of chemical species i in the gas mixture (m2 sÀ1) Knudsen diffusion coefficient of gas species i (m2 sÀ1) molecular diffusion coefficient at reference temperature (m2 sÀ1) thermal diffusion coefficient (m2 sÀ1) particle diameter (m) dp,0 initial particle diameter (m) E fv,0, fw,0 activation energy (kJ molÀ1) initial volatile fraction and initial moisture fraction of fuel incident radiation generation term for turbulence kinetic energy specific enthalpy of gas phase (J kgÀ1) and convective heat transfer coefficient (W mÀ2 KÀ1) latent heat of moisture/volatile matters (J kgÀ1) enthalpy of reaction (J kgÀ1) turbulence kinetic energy (m2 sÀ2) and reaction kinetic rate (sÀ1) thermal conductivity of bulk gas (W mÀ1 KÀ1) mass transfer coefficient between vapor and bulk gas (m sÀ1) mass transfer coefficient between gas phase and particle phase (m sÀ1) intrinsic reaction rate (1 sÀ1 atmÀm) forward rate constant and backward rate constant for r reaction molecular weight of carbon and water vapor (kg kmolÀ1) molecular weight of chemical species i and j (kg kmolÀ1) mass of the tracked particle (kg) initial mass of the tracked particle (kg) a ap C C1e, C2e Cj,r Cs, C1 Csw cp De Di Di,m Dk,i D0 G Gk h hfg Hrec k kc kg kgp kint kf,r, kb,r Mc, Mw Mi, Mj mp mp,0 gasifier using CFX package Ku et al [20] considered the interactions between gas phase and particle devolatilizationphase in an Euler–Lagrangian CFD model by using Open FOAM software It is known that char reaction rate is much slower than devolatilization (pyrolysis) rate, and it is therefore the limiting step in gasification [21–24] However, intrinsic char reactivity of biomass fuel has not been considered in model development for biomass gasification in entrained flow gasifiers The objective of this study is twofold: (a) to develop a comprehensive CFD model for simulating biomass gasification in an entrained flow gasifier by considering the intrinsic char reactivity of biomass fuels, (b) to determine the relative errors between simulated and experimental results based on the entrained flow gasifier built in our institute pg pg,j ps,j R Ri,r b i;r R Rint Rp,j Rp;j r pore Sm, SF, Sh SpY i , RfY i Tg, Tp Tm T0 ui, uj u0i ; u0j v vg X xi, xj Yi YP, YR Re Sc Sct a, b d e g g0j;r ; g00j;r h hR h0 k l lt t0i;r ; t00i;r qg, qp rk, re rs ep s U bulk gas pressure (Pa) partial pressure of species j (atm) partial pressure of species j at particle surface (atm) universal gas constant (J kmolÀ1 KÀ1) and reaction rate (kmol mÀ3 sÀ1) net production rate of chemical species i in r reaction Arrhenius molar rate of production/consumption of chemical species i in r reaction intrinsic char reactivity (sÀ1) particle reaction rate with gas species j (kg mÀ2 sÀ1) particle reaction rate with gas species j (kg sÀ1) average pore radius (m) source term for mass, momentum and energy mass fraction source terms for chemical species i temperature of gas phase and tracked particle (K) mean temperature (K) reference temperature (K) gas phase velocity components (m sÀ1) fluctuating velocity of gas phase (m sÀ1) velocity of particle phase (m sÀ1) stoichiometric ratio of gas moles to carbon moles carbon conversion degree global coordinates (m) and mole fraction mass fraction of chemical species i mass fraction of product species and reactant species Reynolds number Schmidt number turbulent Schmidt number rate exponent temperature exponent dissipation rate of turbulence kinetic energy (m2 sÀ3) effectiveness factor rate exponents for reactant j and product j in r reaction porosity radiation temperature (K) initial porosity thermal conductivity of gas phase (W mÀ1 KÀ1) gas phase viscosity (kg mÀ1 sÀ1) turbulent viscosity (kg mÀ1 sÀ1) stoichiometric coefficients for reactant i and product i in r reaction density of gas phase and particle phase (kg mÀ3) turbulent Prandtl numbers for k and e scattering coefficient particle emissivity tortuosity of pores Thiele modulus Model development 2.1 Main assumptions The following assumptions are made for the model: The gravitational force of gas phase is neglected [19,25] The gasifier is operated under steady state conditions The gas phase is regarded as uncompressible ideal gas, and air is composed of 21% oxygen and 79% nitrogen All biomass granules are spherical in shape and uniform size, and slags during gasification are neglected [26] The gas phase species include CO, CO2, CH4, C2H4, H2, H2O, O2, N2 and tar (described as CxHyOz) [12,27] 122 X Gao et al / Energy Conversion and Management 108 (2016) 120–131 phase, latent heats of drying and pyrolysis, as well as the homogeneous and heterogeneous reactions heat In this study, standard k–e turbulent model is utilized to solve the turbulent stress The transport equations for turbulence kinetic energy k and its dissipation rate e are as follows [29]: biomass particles & oxidant Particle Combustion zone @ @ ðq kui Þ ¼ @xi g @xj @ @ ðq eui Þ ¼ @xi g @xj Gas path  ! lþ lt @k þ Gk À qg e rk @xj lþ lt @ e e e2 þ C 1e Gk À C 2e qg re @xj k k   ð4Þ ! ð5Þ The governing equation for chemical species i is given by: @ @ ðq u j Y i Þ ¼ @xj g @xj   qg Di;m þ lt @Y i Sct @xj þ DT;i @T g T g @xj  þ SpY i þ RfY i ð6Þ where the source term SpY i is caused by the presence of particle phase, and the source term RfY i is due to the production/consumption in chemical reactions The turbulent Schmidt number Sct is set to be 0.7 [5] Particle path Gasification zone 2.3 Particle transport model The particle phase is modeled in Lagrangian coordinates using discrete phase model (DPM) The impact of turbulence in gas phase on the particle is predicted by the stochastic tracking model The governing equations for a tracked particle are: dmp ¼ dt mp mp cp During drying process, moisture evaporation is described as a diffusion limited process [28] The contents of sulfur and nitrogen and associated reactions are neglected 2.2 Continuous phase model The gas phase is modeled in Eulerian coordinates, and all governing equations are given in Reynolds-averaged manner The governing mass equation for gas phase is: @ðqg ui Þ ¼ Sm @xi ð1Þ       dmp dmp dmp þ þ dt drying dt pyrolysis dt reaction dv X ¼ F i þ qp g dt Fig Schematic sketch of gas–solid flow in a downdraft entrained flow gasifier [1]   Á dT p 2À ¼ hpdp T g À T p þ ep pdp r h4R À T 4p dt     dmp dmp þ hfg þ hfg dt drying dt pyrolysis   dmp þ Hrec dt reaction ð7Þ ð8Þ ð9Þ P where the term Fi is the sum of forces between particle phase and gas phase Biomass particles in an entrained flow gasifier undergo the processes of drying, devolatilization, oxidation and gasification The detailed expressions for source terms are introduced in the following chemical reaction models 2.4 Chemical reaction models where the mass source Sm is the mass added to the gas phase from the particle phase The governing momentum equation for gas phase is: @ðqg ui uj Þ @pg @ ¼À þ @xj @xi @xj   l  @ui À qg u0i u0j þ SF @xj ð2Þ where the term Àqg u0i u0j is the Reynolds stress (turbulent stress) which is expressed according to the hypothesis of Boussinesq [29], and the mass source SF is the external body force from the interaction with the dispersed phase The governing energy equation is:   @ðqg ui hÞ @ @T g þ Sh ¼ k @xi @xj @xj ð3Þ where the energy source Sh is the source term due to the heat transfer of convection and radiation between gas phase and particle The chemical reactions inside a gasifier include the moisture release, pyrolysis, homogeneous reactions (oxidation and gasification of volatile matters) and heterogeneous reactions (oxidation and gasification of biomass char) (1) Drying Moisture release is simulated through using wet combustion model When the particle temperature reaches the evaporization temperature, moisture is released The evaporation rate is given by:   dmp ¼ pdp M w kg ðC s À C Þ dt drying ð10Þ If the temperature is higher than water boiling temperature, the evaporation rate is: 123 X Gao et al / Energy Conversion and Management 108 (2016) 120–131      dmp pdp kc  cp ðT g À T p Þ ¼ þ 0:46Re0:5 ln þ hfg dt drying cp ð11Þ (2) Pyrolysis The pyrolysis process is modeled by single kinetic rate model where the biomass pyrolysis is represented by a one-stage global reaction: Biomass ! Char þ Volatile ðR1Þ Volatiles ¼ x1 CO þ x2 CO2 þ x3 H2 þ x4 CH4 þ x5 C2 H4 þ tar In Fluent, the biomass char contains only solid carbon and ash, and the composition of volatile can be obtained through Thunman’s method [30] based on the mass balance with proximate and ultimate analyses The pyrolysis rate depends on the amount of volatiles remaining in the biomass particle, so the decomposition rate is given by:    à dmp ¼ Àk mp À ð1 À f v;0 Þð1 À f w;0 Þmp;0 dt pyrolysis ð12Þ H2 þ 0:5O2 ! H2 O ðR3Þ CH4 þ 1:5O2 ! CO þ 2H2 O ðR4Þ CO þ H2 O $ CO2 þ H2 ðR5Þ C2 H4 þ 2O2 ! 2CO þ 2H2 O ðR6Þ   y y Cx Hy Oz þ x þ À z O2 ! xCO þ H2 O 2 ðR7Þ Reaction rates of homogeneous phase reactions are calculated through finite-rate/eddy-dissipation model Both the Arrhenius and eddy-dissipation reaction rates are calculated in finite-rate/ eddy-dissipation model, and then the minimum of these two rates is chosen as the homogeneous reaction rate [33] Arrhenius expression is: b i;r ¼ R  00 i;r t Àt i;r  kf;r N Y  C j;r Ãg0 j;r À kb;r N Y  j¼1 The parameter needed is only kinetic constant obtained in Arrhenius expression Single kinetic rate model has been widely used in pyrolysis simulations due to its simplicity making it computationally tractable [12] In this study, the pre-exponential factor is 4.88  1012 sÀ1 and the activation energy is 177 kJ molÀ1 [31] Since the fraction of volatile mater in biomass is significant, the effect of shrinkage/swelling during the pyrolysis process should be taken into account [32] In this study, swelling coefficient Csw of 1.8 [32] is used to describe the change of particle diameter during devolatilization, and the particle diameter can be expressed as: ð1 À f w;0 Þmp;0 À mp dp ¼ þ ðC sw À 1Þ dp;0 f v;0 ð1 À f w;0 Þmp ð13Þ (3) Homogeneous phase reactions After the pyrolysis, the combustible gases (CO, H2, CH4, etc.) among volatile will react with oxidant fed into the reactor With insufficient oxidant, gasification reactions will also happen among the volatile gases The homogeneous phase reactions taken into account in this study are as follows: CO þ 0:5O2 ! CO2 ðR2Þ C j;r Ãg00 j;r ! ð14Þ j¼1 The kinetic rates for gas phase reactions used in this study are listed in Table Eddy-dissipation rate is determined by the smaller of the expressions below: i;r M i A Ri;r ¼ t e YR t0R;r MR qg minR k Ri;r ¼ t0i;r Mi ABqg e k ! ð15Þ P PN PYP ð16Þ 00 j tj;r M j In this study, A is equal to 4.0, and B is equal to 0.5 [33,37] (4) Heterogeneous phase reactions In order to improve the char reaction model, the intrinsic reaction rates of char reactions (intrinsic reaction rate model) are applied to take into account both diffusion effect and chemical reaction effect when establishing the heterogeneous phase reaction model [38,39] The heterogeneous phase reactions considered in this study are as follows: Table Kinetic parameters for homogeneous phase reactions a E b CO þ 0:5O2 ! CO2 0.5 133 H2 þ 0:5O2 ! H2 O Ri ¼ 1 Ri ¼ À0.3 1.3 [12] 2.8  109 [12] 2.43  109 6.4  1012 [34] [35] A expðÀE=RT g ÞC CH4 C bO2 0.5 1 0.5 304.6 326.4 1 173 1.00  1012 [36] a b E A Ref 80.2 9.17  106 [28] Ri ¼ A expðÀE=RT g ÞC aC2 H4 C bO2 Ri ¼ ATdg expðÀE=RT g ÞC aCx Hy Oz C bO2 0.5 À1 1.08  1013 a Rif ¼ A expðÀE=RT g ÞC CO C bH2 O Rib ¼ A expðÀE=RT g ÞC aCO2 C bH2 À Á Cx Hy Oz þ x þ 2y À z O2 ! xCO þ 2y H2 O À3 [27] a C2 H4 þ 2O2 ! 2CO þ 2H2 O 1.78  1014 A expðÀE=RT g ÞC H2 C bO2 203 CO þ H2 O $ CO2 þ H2 d Ref a 125 CH4 þ 1:5O2 ! CO þ 2H2 O f b A Ri ¼ A expðÀE=RT g ÞC aCO C bO2 À1 À3 À3 m Ri in kmol m s , E in kJ mol , C in kmol m , A in (koml m f indicates forward reaction, b indicates backward reaction ) K Àd s À1 with m = À a À b 124 X Gao et al / Energy Conversion and Management 108 (2016) 120–131 Chsi þ 0:5O2 ! CO ðR8Þ Chsi þ CO2 ! 2CO ðR9Þ Chsi þ H2 O ! CO þ H2 ðR10Þ In the intrinsic reaction rate model, both effectiveness factor g and intrinsic reaction rate Rint are used to express the char reaction rates, and the reaction rate of heterogeneous reaction can be expressed as: Rp;j ¼ qp dp ð17Þ gRint The order of reaction can be represented with m, so the intrinsic rate of reaction (Rint) is expressed as [12,40]: Rint ¼ kint pm s;j FðXÞ ð18Þ where the term F(X) is the surface function which depicts the variation of active site concentration depending on the carbon conversion degree The effectiveness factor g, the ratio of the actual reaction rate to the intrinsic rate, is defined as [38]:   1 g¼ À / / / dp /¼ ð19Þ sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðm þ 1Þkint FðXÞqp v g RT g pmÀ1 s;j 2M c De ð20Þ In an entrained flow gasifier, the relative velocity between gas phase and particle phase is small [38], so Eq (28) can be simplified to: kgp dp ¼2 Di ð29Þ The pressure ps,j can be expressed as: ps;j ¼ pg;j À Kd ¼ Rp;j Kd ð30Þ 2M c Di ð31Þ v g dp RT g Finally, the reaction rate of biomass char is given by: Rp;j ¼ qp dp  gFðXÞkint pg;j À Rp;j Kd m ð32Þ In order to solve Rp,j, Brent’s iteration method is applied in this study In Fluent, the unit for particle reaction rate is kg sÀ1, so multiply Rp,j by the external surface area to give: Rp;j ¼ pdp Rp;j ð33Þ The sub-model for determining the reaction rates of heterogeneous reactions is developed as user-defined functions to be compiled in the Fluent 2.5 Radiation model The universal gas constant R in Eq (20) is in the unit of atm m kmolÀ1 KÀ1 The effective diffusivity De is the diffusion coefficient of gas reactant through the particle pores Here, both molecular diffusion and Knudsen diffusion are taken into account, and the definition of De is [41]: De ¼ h s  1 þ Di Dk;i The radiation flux during the entrained flow gasification is calculated through P-1 model The radiation heat flux is: qr ¼ À   @G 3ða þ rs Þ À C rs @xi À1 ð21Þ The molecular diffusion coefficient is a function of temperature at a certain pressure [42] The coefficients for molecular diffusion and Knudsen diffusion are given as follows:  n Tm Di ¼ D0 T0 Tm ¼ Dk;i ð34Þ ð22Þ Yes Input parameter: Tm, T0, D0, A, , E, etc F(X), , Di, kint Access rpore Tg þ Tp ð23Þ sffiffiffiffiffiffi Tp ¼ 97:0r pore Mi Char reaction? Dk,i No De ð24Þ The porosity h of char particles and the mean pore radius r pore are obtained by [38,41]: h ¼ h0 þ ð1 À h0 ÞX r pore ¼ ð25Þ 2h FðXÞqp ð26Þ Through the bulk diffusion, the partial pressure of species j at particle surface can be obtained by [38]: kgp Rp;j v g ðp À ps;j Þ ¼ RT g g;j Mc ð27Þ The mass transfer coefficient kgp is determined by the Frössling equation [38]: kgp dp ¼ þ 0:6Sc Re Di Exit f(Rp,j)=0 Brent method Root? Yes Rp,j Return ð28Þ Fig UDF flow chart for char reactions No 125 X Gao et al / Energy Conversion and Management 108 (2016) 120–131 For simplification, the parameter C is introduced: Biomass & oxidant inlet C¼ ð3ða þ rs Þ À C rs Þ ð35Þ Thus, for particles, the transport equation for the incident radiation G is: Carrier gas inlet !   rT 4p @ @G À ða þ ap ÞG þ 4p a C þ ep ¼ @xj @xj p ð36Þ 2.6 Calculation procedure for char reactions Fig introduces the UDF flow chart for char reaction rates When the solver starts to calculate the char reaction, the DEFINE_PR_RATE macro is called According to the previously defined constants, the intermediate parameters in char reaction submodel including surface function, average pore radius, etc are computed in order to fill the char reaction rate function (Eq (32)) Brent iteration is continued to solve the Eq (32) Model validation 3.1 Experimental apparatus and material Outlet Fig Schematic diagram of the entrained flow gasifier at HIT Table Pyrolysis coefficients for sawdust Coefficient x1 x2 x3 x4 x5 Value 1.59 0.338 0.455 0.383 0.125 Table Main characteristics of sawdust a Characteristics Value Unit Proximate analysisa [36] Moisture content Volatile mater Fixed carbon Ash 9.20 75.46 13.80 1.54 % % % % Ultimate analysisa [36] C H O N S Lower heating value Minimum diameter Maximum diameter Mean diameter Diameter distribution Spread parameter Number of diameter Tracked number Density 43.01 6.42 39.64 0.17 0.02 15.09 75 425 340 Rosin–Rammler type 2.5 800 700 % % % % % MJ kgÀ1 lm lm lm – – – – kg mÀ3 Weight percentage on air dried basis An entrained flow gasification system was built in the School of Energy Science and Engineering, Harbin Institute of Technology (HIT), China The gasification system consists of a downdraft entrained flow reactor, a biomass fuel feeder, a gas supplying system, a heating and temperature measuring system and a sampling system A detailed description of the experimental apparatus and experimental procedures can be found in Ref [36] The schematic diagram of the entrained flow reactor is shown in Fig For simplification, a 2D geometric model of this entrained flow gasifier was built and the geometric dimensions were detailed in our previous work [43] The grid independence of the geometric model was verified based on the previously developed model [43] where five grids (0.04, 0.08, 0.17, 0.23 and 0.30 million cells) were examined The relative differences of gas volumetric concentrations between 0.17 million and 0.30 million were less than 2%, and the grid of 0.17 million cells is therefore adopted in this study The standard wall function is adopted for near-wall treatment, and second order upwind scheme is used as the discretization scheme The convergence criteria for energy and P1 are set to be 10À6 and the convergence criteria for the other variables are set to be 10À3 In this study, air gasification of sawdust in the entrained flow gasifier is simulated at various equivalence ratios and gasification temperatures Equivalence ratio is defined as the ratio of the actual air supplied to the stoichiometric air required for complete combustion The variation of equivalence ratio is controlled by altering the air supplying rate while keeping the fuel feeding rate and other operating parameters fixed The variation of gasification temperature is controlled by the electrical heating element installed in the entrained flow reactor while other operating parameters are fixed The main characteristics and pyrolysis coefficients for the sawdust used in this study are listed in Tables and 3, respectively, and the Table Reaction rate constants for char reactions [40] Reaction E (kJ molÀ1) A (sÀ1 atmÀm) Chsi þ 0:5O2 ! CO Chsi þ CO2 ! 2CO Chsi þ H2 O ! CO þ H2 179 245 170 1.00  109 1.20  108 3.55  105 126 X Gao et al / Energy Conversion and Management 108 (2016) 120–131 Table Parameters for experiments on sawdust gasification [36] ER Air flow rate (L minÀ1) N2 flow rate (L minÀ1) Sawdust feeding (g minÀ1) 0.22 0.25 0.28 0.31 0.34 8.6 9.8 11 12.2 13.4 41 40 39 38 37 10 10 10 10 10 tar produced during the pyrolysis process is described by C0.3H4.148O0.463 The surface function F(X) is described by [40]: FðXÞ ¼ 94:95X À 190:37X À 47:08X þ 6:14X þ 0:29 ð37Þ In addition, the intrinsic reaction rate constants for sawdust char are listed in Table (a) The inlet conditions for oxidant gas (air) are: flow rates (given in Table 5), air inlet temperature Tin,air = 300 K, and turbulence specification adopts turbulent intensity Iin, air = 5% and hydraulic diameter Din,air = mm (b) The inlet conditions for carrier gas (N2) are: flow rates (given in Table 5), N2 inlet temperature Tin,nitro = 300 K, and turbulence specification adopts turbulent intensity Iin,nitro = 5% and hydraulic diameter Din,nitro = mm (c) The outlet conditions are: gauge pressure Po = Pa, and turbulence specification adopts turbulent intensity Io = 5% and hydraulic diameter Do = 100 mm (d) Wall condition: no slip shear condition together with constant wall temperature and the wall temperature is equal to gasification temperature The flow rates of sawdust particles are given in Table 5, and the initial conditions for particles are given in Table For the particle phase, the maximum number of tracking step is set as 105, and the tracking length scale is specified as 0.001 m 3.2 Boundary conditions 3.3 Results and discussion The boundary conditions for simulating the entrained flow gasifier are given as follows: Volumetric concentration (%) 50 40 Experiment CO CO2 H2 CH4 C H4 Simulation CO CO2 H2 CH4 C2 H4 30 20 10 0.22 0.25 0.28 0.31 0.34 Equivalence ratio The CFD model is validated with experimental data taken from the published work [36] The relative errors between the simulated and experimental data are detailed in this study The relative error is defined as the absolute difference between the simulated and experimental values divided by the experimental value 3.3.1 Gas composition The simulated and experimental volumetric concentrations of the produced gas compositions at different equivalence ratios and gasification temperatures are shown in Fig In Fig 4(a), when equivalence ratio varies in the range of 0.22–0.34, the simulated volumetric concentrations of CO, CO2, H2, CH4 and C2H4 are in the ranges of 20.72–30.20%, 9.64–11.89%, 6.50–9.45%, 2.24–3.52% and 0.73–1.13% whereas the corresponding experimental values are in the ranges of 21.87–30.64%, 9.10–10.90%, 6.23–8.05%, 1.76–3.33% and 0.94–1.40%, respectively It is observed that increasing the equivalence ratio from 0.22 to 0.34 increases the yield of CO2 and decreases the yields of CO and H2, however, it (a) at different equivalence ratios (gasification temperature = 800 oC) Volumetric concentration (%) 50 40 Experiment CO CO H2 CH4 C2 H Simulation CO CO H2 CH4 C2 H4 30 20 10 800 850 900 950 1000 Simulated volumetric concentration (%) 40 CO CO CH C2H4 H2 30 20% 20 20% 10 Temperature ( oC) (b) at different gasification temperatures (equivalence ratio = 0.28) 0 10 20 30 40 Experimental volumetric concentration (%) Fig Simulated and experimental volumetric concentrations of produced gas compositions Fig Distribution of relative errors for produced gas compositions 127 X Gao et al / Energy Conversion and Management 108 (2016) 120–131 Experiment Simulation Experiment 0.22 0.24 0.26 0.28 0.30 0.32 0.34 Equivalence ratio 0.22 (a) at different equivalence ratios (gasification temperature = 800 oC) 0.26 0.28 0.30 0.32 0.34 (a) at different equivalence ratios (gasification temperature = 800 oC) Simulation Experiment Simulation -1 Gas production (Nm kg biomass) -3 Experiment 0.24 Equivalence ratio 10 Lower heating value (MJ Nm ) Simulation -1 Gas production (Nm kg biomass) -3 Lower heating value (MJ Nm ) 10 800 850 900 950 1000 o Temperature ( C) (b) at different gasification temperatures (equivalence ratio = 0.28) Fig Simulated and experimental lower heating values of produced gas 800 850 900 950 1000 o Temperature ( C) (b) at different gasification temperatures (equivalence ratio = 0.28) shows slight reducing effects on the yields of CH4 and C2H4 The changes are caused by the facts that higher equivalence ratio can promote the oxidation exothermic reactions (CO, H2, CH4, etc.) and cause higher temperature inside the reactor [44], which would support the endothermic gasification reactions [8,45] In Fig 4(b), when gasification temperature increases from 800 °C to 1000 °C, the simulated volumetric concentrations of CO, CO2, H2, CH4 and C2H4 vary within the ranges of 20.57–24.09%, 11.58–13.28%, 8.33–13.61%, 1.31–2.81% and 0.13–0.90% whereas the corresponding experimental values are in the ranges of 23.38–25.99%, 10.05–12.11%, 7.62–13.49%, 1.55–3.33% and 0.19– 1.31%, respectively The simulated and experimental results show that when gasification temperature increases from 800 °C to 1000 °C, the yields of CO2 and H2 increase whereas the yields of CO, CH4 and C2H4 decrease Endothermic char reaction and methane oxidation reaction are favorable at higher temperature, which improves the production of H2 and weakens the production of CH4 [20,46] However, in the range of 700–900 °C, water gas shift reaction accelerates with the increasing temperature, resulting in a decrease in CO yield whereas an increase in CO2 yield [8] The relative errors between the simulated and experimental gas compositions are presented in Fig The relative errors are 1–13%, 5–15%, 1–18%, 4–30%, 16–35% for CO, CO2, H2, CH4, C2H4, respectively Several researchers reported similar results for relative Fig Simulated and experimental gas productions errors between the simulated and experimental gas compositions The relative errors reported by Ku et al [20] were 25%, 25%, 19% and 19% for CO, H2, CO2 and CH4, respectively The relative errors of H2, CO, CO2 and CH4 reported by He et al [47] were in the ranges of 10–40%, 20–35%, 15–20% and 32–65%, respectively The relative errors reported by Liu et al [48] were 12%, 1%, 50% and 50% for CO, H2, CH4 and C2H4, respectively The relative errors reported by Zhao [36] were 4–12%, 1–20%, 0–17%, 9–40%, and 1–55% for CO, CO2, CH4, C2H4, and H2, respectively The results obtained in this study show that the relative errors between the simulated and experimental gas compositions are mainly within 18% except for a few points related to CH4 and C2H4 Ku et al [20] stated that the relative error of CH4 may be somewhat large (due to its small amounts), however, the small amounts can usually be neglected In this study, although the maximum relative errors are up to 30% and 35% for CH4 and C2H4, the corresponding differences between simulated and experimental volumetric concentrations are 0.58% and 0.29%, respectively, being very small amounts and therefore can be neglected As the developed CFD model can predict well for the compositions of most of the small-amount gases (CH4 and C2H4) and the other gases, the developed CFD model 128 X Gao et al / Energy Conversion and Management 108 (2016) 120–131 therefore can be used to predict the produced gas compositions of biomass gasification in the entrained flow gasifier 3.3.2 Lower heating value The simulated and experimental lower heating values of produced gas at different equivalence ratios and gasification temperatures are given in Fig In Fig 6(a), when equivalence ratio increases from 0.22 to 0.34, the simulated lower heating values of produced gas are in the range of 4.57–6.80 MJ N mÀ3 whereas the relevant experimental values are in the range of 4.65– 6.67 MJ N mÀ3 It is observed that the lower heating value of produced gas monotonically decreases when the equivalence ratio increases, this is due to the decreases in the main combustible species (CO and H2) [49] In Fig 6(b), when gasification temperature increases from 800 °C to 1000 °C, the simulated lower heating values of produced gas vary in the range of 4.65–5.51 MJ N mÀ3 whereas the relevant experimental values are in the range of 5.08–6.00 MJ N mÀ3 Both the simulated and experimental data show that increasing the gasification temperature decreases the lower heating value of produced gas Although the H2 production increases with the gasification temperature, the other combustible species (CO, CH4 and C2H4) decrease, making the lower heating value of produced gas decrease with the rise of gasification temperature [50] The relative errors between the simulated and experimental lower heating values of produced gas are within 1–13%, being lower than the relative errors of about 20% reported by Miao et al [51] for the lower heating values of produced gas from a circulating fluidized bed reactor and the maximum relative error of 28% reported by Ngo et al [50] for a three-stage gasification model 3.3.3 Gas production Gas production is determined as the total volume of the produced gas per kilogram biomass (N m3 kgÀ1 biomass) The simulated and experimental gas productions at different equivalence ratios and gasification temperatures are shown in Fig In Fig (a), when equivalence ratio varies from 0.22 to 0.34, the simulated gas productions are in the range of 1.47–1.83 N m3 kgÀ1 biomass whereas the corresponding experimental values are in the range of 1.42–1.82 N m3 kgÀ1 biomass Both the predicted and experimental results show that the gas production increases monotonically with the rise of equivalence ratio, this is due to the fact that higher temperature (caused by higher equivalence ratio) favors the cracking of tar and more gas could be produced [44] 100 Carbon conversion efficiency (%) Cold gas efficiency (%) 100 80 60 40 Experiment 20 Simulation 80 60 40 Experiment Simulation 20 0.22 0.24 0.26 0.28 0.30 0.32 0.34 0.22 Equivalence ratio 0.28 0.30 0.32 0.34 (a) at different equivalence ratio (gasification temperature = 800 oC) 100 100 Carbon conversion efficiency (%) Cold gas efficiency (%) 0.26 Equivalence ratio (a) at different equivalence ratios (gasification temperature = 800 oC) 80 60 40 Experiment 20 0.24 800 850 Simulation 900 950 Temperature ( oC) (b) at different gasification temperatures (equivalence ratio = 0.28) Fig Simulated and experimental cold gas efficiencies 1000 80 60 40 Experiment Simulation 20 800 850 900 950 1000 Temperature (oC) (b) at different gasification temperatures (equivalence ratio = 0.28) Fig Simulated and experimental carbon conversion efficiencies X Gao et al / Energy Conversion and Management 108 (2016) 120–131 In Fig 7(b), when gasification temperature varies from 800 °C to 1000 °C, the predicted gas production varies in the range of 1.66– 1.82 N m3 kgÀ1 biomass whereas the corresponding experimental gas production varies in the range of 1.68–1.76 N m3 kgÀ1 biomass Lapuerta et al [52] also reported that the gas production varied slightly when the gasification temperature increased from 750 °C to 1000 °C The relative errors between simulated and experimental gas productions are in the range of 1–8% These values are lower than the maximum relative errors of 128% and 20% reported by Ngo et al [50] and Miao et al [51] for the predicted gas productions, respectively 3.3.4 Cold gas efficiency Cold gas efficiency is defined as the ratio of the lower heating value of the fuel gas to the lower heating value of the raw biomass feedstock The simulated and experimental gasification efficiencies at different equivalence ratios and gasification temperatures are given in Fig In Fig 8(a), when equivalence ratio rises from 0.22 to 0.34, the simulated cold gas efficiency varies within the range of 55.43–66.53% whereas the relevant experimental value varies within the range of 56.05–62.81% Since cold gas efficiency is the product of gas production and lower heating value, it is therefore determined by the gas production and lower heating value collectively In Fig 8(b), when gasification temperature rises from 800 °C to 1000 °C, the simulated cold gas efficiency varies in the range of 52.77–60.78% whereas the relevant experimental value varies in the range of 59.36–66.71% van der Meijden et al [53] stated that high temperature can decrease the cold gas efficiency, both the simulated and experimental results in this study also show that increasing gasification temperature generally decreases the cold gas efficiency The relative errors between simulated and experimental gasification efficiencies are in the range of 1–12% These values are lower (a) Temperature (K) (b) CO mass fraction 129 than the maximum relative error of around 20% reported by Miao et al [51] for the predicted gasification efficiencies 3.3.5 Carbon conversion efficiency Carbon conversion efficiency is defined as the ratio the amount of carbon in the final produced gas to the amount of carbon in the biomass feedstock The simulated and experimental carbon conversion efficiencies at different equivalence ratios and gasification temperatures are shown in Fig In Fig 9(a), when equivalence ratio increases from 0.22 to 0.34, the simulated carbon conversion efficiency varies between 88.26% and 89.54% whereas the experimental value varies between 85.93% and 92.81% In Fig 9(b), when gasification temperature increases from 800 °C to 1000 °C, the simulated carbon conversion efficiency varies between 81.39% and 89.11% whereas the experimental value varies between 87.38% and 92.81% The relative errors between simulated and experimental carbon conversion efficiencies are in the range of 1–11% These values are lower than the values of 1–25% reported by Nikoo and Mahinpey [54] for the simulated carbon conversion efficiencies 3.3.6 Gasification phenomena The temperature and species mass fraction contours of a basic case inside the entrained flow gasifier when the gasification temperature and equivalence ratio are respectively 800 °C and 0.28 are shown in Fig 10 There is a highest-temperature zone located in the upper section of the gasifier, which is due to the exothermic oxidation reactions The temperature decreases along the reactor because of the occurrence of the endothermic reduction reactions Biomass particles injected from the top go through drying and pyrolysis rapidly, meanwhile the gas species of CO, H2, CO2, etc are released And then the oxidation reactions take place in the combustion zone, the mass fraction of CO2 reaches the maximum around the highest-temperature zone As the mixture of gas and particles moves further up into the gasification zone (where (c) H2 mass fraction (d) CO2 mass fraction Fig 10 Temperature and species mass fraction contours of a basic case 130 X Gao et al / Energy Conversion and Management 108 (2016) 120–131 reduction reactions take place), the mass fractions of CO and H2 increase whereas the CO2 decreases The carrier gas introduced from horizontal chamber affects the flow field resulting in nonuniform distributions of gas species inside the reactor Conclusions The intrinsic reaction rate model is developed to include the effects of bulk diffusion and chemical reaction, and it is realized by compiling user-defined functions (UDFs) to the ANSYS FLUENT software for calculating the char reaction rates The simulated results were then compared with the experimental values The following conclusions are obtained The relative errors of volumetric concentrations are mainly in the range of 1–18% for the gases of CO, CO2, H2, CH4 and C2H4 Although some relative errors are up to 35% for CH4 and C2H4, the maximum differences (0.58% for CH4 and 0.29% for C2H4) are very small amounts and they have no significant effects on the other gasification performances The relative errors of lower heating value, gas production, cold gas efficiency and carbon conversion efficiency are in the ranges of 1–13%, 1–8%, 1–12% and 1–11%, respectively The model developed in this study can therefore be used to predict the gasification performances for entrained flow gasifiers Acknowledgements This study is supported by China Postdoctoral Science Foundation (Grant No 2014M551240), Fundamental Research Funds for the Central Universities (Grant No HIT.NSRIF.2015080), and the Science and Technology Research Funds for Harbin Innovative Talents (Grant No 2014RFQXJ078) The financial support from the Collaborative Innovation Center of Clean Coal Power Plant with Poly-generation is also acknowledged The authors also acknowledge the help from Professor Abdel Ghaly who works in Department of Process Engineering and Applied Science, Faculty of Engineering, Dalhousie University, Canada The authors also acknowledge the reviewers and editor for their comments which significantly improved the quality of this study References [1] Basu P Biomass gasification and pyrolysis: practical design and theory Oxford: Academic Press; 2010 [2] Guo F, Dong Y, Lv Z, Fan P, Yang S, Dong L Pyrolysis kinetics of biomass (herb residue) under isothermal condition in a micro fluidized bed Energy Convers Manage 2015;93:367–76 [3] Li J, Zhang X, Pawlak-Kruczek H, Yang W, Kruczek P, Blasiak W Process simulation of co-firing torrefied biomass in a 220 MWe coal-fired power plant Energy Convers Manage 2014;84:503–11 [4] U.S Energy Information Administration Annual energy outlook 2014 with projections to 2040 Washington, D.C.: Office of Integrated and International Energy Analysis, U.S Department of Energy; 2014 April Report No.: DOE/EIA0383(2014) [5] Silaen AK Comprehensive modeling and numerical investigation of entrainedflow coal gasifiers [dissertation] New Orleans (LA): University of New Orleans; 2010 [6] Demirbasß A Biomass resource facilities and biomass conversion processing for fuels and chemicals Energy Convers Manage 2001;42:1357–78 [7] Tremel A, Becherer D, Fendt S, Gaderer M, Spliethoff H Performance of entrained flow and fluidised bed biomass gasifiers on different scales Energy Convers Manage 2013;69:95–106 [8] Qin K Entrained flow gasification of biomass [dissertation] Kongens Lyngby (RH): Technical University of Denmark; 2012 [9] Zhao Y, Sun S, Tian H, Qian J, Su F, Ling F Characteristics of rice husk gasification in an entrained flow reactor Bioresour Technol 2009;100:6040–4 [10] Qin K, Lin W, Jensen PA, Jensen AD High-temperature entrained flow gasification of biomass Fuel 2012;93:589–600 [11] Zhou J, Chen Q, Zhao H, Cao X, Mei Q, Luo Z, et al Biomass–oxygen gasification in a high-temperature entrained-flow gasifier Biotechnol Adv 2009;27:606–11 [12] Gómez-Barea A, Leckner B Modeling of biomass gasification in fluidized bed Prog Energy Combust Sci 2010;36:444–509 [13] Zhao Y, Wen H, Xu Z Conceptual design and simulation study of a cogasification technology Energy Convers Manage 2006;47:1416–28 [14] Ghenai C, Janajreh I CFD analysis of the effects of co-firing biomass with coal Energy Convers Manage 2010;51:1694–701 [15] Janajreh I, Shrah MA Numerical and experimental investigation of downdraft gasification of wood chips Energy Convers Manage 2013;65:783–92 [16] Kobayashi N, Tanaka M, Piao G, Kobayashi J, Hatano S, Itaya Y, et al High temperature air-blown woody biomass gasification model for the estimation of an entrained down-flow gasifier Waste Manage 2009;29:245–51 [17] Coda B, Cieplik MK, Wild PJ, Kiel JHA Slagging behavior of wood ash under entrained-flow gasification conditions Energy Fuels 2007;21:3644–52 [18] Valero A, Usón S Oxy-co-gasification of coal and biomass in an integrated gasification combined cycle (IGCC) power plant Energy 2006;31:1643–55 [19] Fletcher DF, Haynes BS, Christo FC, Joseph SD A CFD based combustion model of an entrained flow biomass gasifier Appl Math Model 2000;24:165–82 [20] Ku X, Li T, Løvås T Eulerian–Lagrangian simulation of biomass gasification behavior in a high-temperature entrained-flow reactor Energy Fuels 2014;28:5184–96 [21] Bridgwater AV The technical and economic feasibility of biomass gasification for power generation Fuel 1995;74:631–53 [22] Haykiri-Acma H, Yaman S, Kucukbayrak S Gasification of biomass chars in steam–nitrogen mixture Energy Convers Manage 2006;47:1004–13 [23] Cetin E, Moghtaderi B, Gupta R, Wall TF Influence of pyrolysis conditions on the structure and gasification reactivity of biomass chars Fuel 2004;83:2139–50 [24] Huo W, Zhou Z, Chen X, Dai Z, Yu G Study on CO2 gasification reactivity and physical characteristics of biomass, petroleum coke and coal chars Bioresour Technol 2014;159:143–9 [25] Chen WH, Chen CJ, Hung CI, Shen CH, Hsu HW A comparison of gasification phenomena among raw biomass, torrefied biomass and coal in an entrainedflow reactor Appl Energy 2013;112:421–30 [26] Sun S, Tian H, Zhao Y, Sun R, Zhou H Experimental and numerical study of biomass flash pyrolysis in an entrained flow reactor Bioresour Technol 2010;101:3678–84 [27] Xie J, Zhong W, Jin B, Shao Y, Liu H Simulation on gasification of forestry residues in fluidized beds by Eulerian–Lagrangian approach Bioresour Technol 2012;121:36–46 [28] Zhou H, Jensen AD, Glarborg P, Jensen PA, Kavaliauskas A Numerical modeling of straw combustion in a fixed bed Fuel 2005;84:389–403 [29] Tao W, editor Numerical heat transfer Xi’an: Xi’an Jiaotong University Press; 2001 [30] Thunman H, Niklasson F, Johnsson F, Leckner B Composition of volatile gases and thermochemical properties of wood for modeling of fixed or fluidized beds Energy Fuels 2011;15:1488–97 [31] Chen Y, Charpenay S, Jensen A, Wójtowicz MA, Serio MA Modeling of biomass pyrolysis kinetics In: Burgess AR, Dryer FL, editors Symposium (international) on combustion IntC 1998: twenty-seventh symposium (international) on combustion volume one; 1998 August 2–7; Boulder, USA Oxford: Elsevier Inc.; 1998 p 1327–34 [32] Li J, Brzdekiewicz A, Yang W, Blasiak W Co-firing based on biomass torrefaction in a pulverized coal boiler with aim of 100% fuel switching Appl Energy 2012;99:344–54 [33] Luan YT, Chyou YP, Wang T Numerical analysis of gasification performance via finite-rate model in a cross-type two-stage gasifier Int J Heat Mass Trans 2013;57:558–66 [34] Bustamante F, Enick RM, Killmeyer RP, Cugini AV, Rothenberger KS, Howard BH, et al Uncatalyzed and wall-catalyzed forward water-shift reaction kinetics AIChE J 2005;51:1440–54 [35] Bustamante F, Enick RM, Cugini AV, Killmeyer RP, Howard BH, Rothenberger KS, et al High-temperature kinetics of the homogeneous reverse water-gas reaction AIChE J 2004;50:1028–41 [36] Zhao Y Study on biomass gasification in an entrained flow reactor and in the cyclone gasifier [dissertation] Harbin (HLJ): Harbin Institute of Technology; 2009 [37] Patra C CFD simulations of fluidized bed biomass gasification [dissertation] Rourkela (OR): National Institute of Technology; 2013 [38] Vejahati F Entrained flow gasification of oil sand coke [dissertation] Edmonton (AB): University of Alberta; 2012 [39] Monaghan RFD, Ghoniem AF A dynamic reduced order model for simulating entrained flow gasifiers Part I: Model development and description Fuel 2012;91:61–80 [40] van de Steene L, Tagutchou JP, Sanz FJE, Salvador S Gasification of woodchip particles: Experimental and numerical study of char–H2O, char–CO2, and char–O2 reactions Chem Eng Sci 2011;66:4499–509 [41] Jeong HY, Seo DK, Hwang J CFD modeling for coal size effect on coal gasification in a two-stage commercial entrained-bed gasifier with an improved char gasification model Appl Energy 2014;123:29–36 [42] Ollero P, Serrera A, Arjona R, Alcantarilla S Diffusional effects in TGA gasification experiments for kinetic determination Fuel 2002;81:1989–2000 [43] Gao X, Zhang Y, Li B, Dong L CFD modeling of an entrained flow gasifier Appl Mech Mater 2014;448–453:1624–7 [44] Ergüdenler A, Ghaly AE, Hamdullahpur F, Al-Taweel AM Mathematical modeling of a fluidized bed straw gasifier: Part III – Model verification Energy Sources 1997;19:1099–121 X Gao et al / Energy Conversion and Management 108 (2016) 120–131 [45] Yoon SJ, Son YI, Kim YK, Lee JG Gasification and power generation characteristics of rice husk and rice husk pellet using a downdraft fixed-bed gasifier Renew Energy 2012;42:163–7 [46] Ji P, Feng W, Chen B Production of ultrapure hydrogen from biomass gasification with air Chem Eng Sci 2009;88:582–92 [47] He L, Schotte E, Thomas S, Schlinkert A, Herrmann A, Mosch V, et al Wood gasification in a lab-scale bubbling fluidized bed: experiment and simulation In: Yue G, Zhang H, Zhao C, Luo Z, editors Fluidized bed combustion IntFBC 2009: proceedings of the 20th international conference on fluidized bed combustion; 2009 May 18–20; Xi’an, China Berlin: Springer; 2010 p 686–92 [48] Liu H, Elkamel A, Lohi A, Biglari M Computational fluid dynamics modeling of biomass gasification in circulating fluidized-bed reactor using the Eulerian– Eulerian approach Ind Eng Chem Res 2013;52:18162–74 [49] Chang ACC, Chang HF, Lin FJ, Lin KH, Chen CH Biomass gasification for hydrogen production Int J Hydrogen Energy 2011;36:14252–60 131 [50] Ngo SI, Nguyen TDB, Lim YI, Song BY, Lee UD, Choi YT, et al Performance evaluation for dual circulating fluidized-bed steam gasifier of biomass using quasi-equilibrium three-stage gasification model Appl Energy 2011;88: 5208–20 [51] Miao Q, Zhu J, Barghi S, Wu C, Yin X, Zhou Z Model validation of a CFB biomass gasification model Renew Energy 2014;63:317–23 [52] Lapuerta M, Hernández JJ, Pazo A, López Julio Gasification and co-gasification of biomass wastes: effect of the biomass origin and the gasifier operating conditions Fuel Process Technol 2008;89:828–37 [53] van der Meijden C, Veringa HJ, Rabou LPLM The production of synthetic natural gas (SNG): a comparison of three wood gasification systems for energy balance and overall efficiency Biomass Bioenergy 2010;34:302–11 [54] Nikoo MB, Mahinpey N Simulation of biomass gasification in fluidized bed reactor using ASPEN PLUS Biomass Bioenergy 2008;32:1245–54