Modeling and simulation of a downdraft biomass gasifier 1. Model development and validation

Energy Conversion and Management 52 (2011) 1386–1396 Contents lists available at ScienceDirect Energy Conversion and Management journal homepage: www.elsevier.com/locate/enconman Modeling and simulation of a downdraft biomass gasifier Model development and validation Avdhesh Kr Sharma ⇑ Mech Engg Dept., D.C.R University of Science & Technology, Murthal, Sonepat 131 039, Haryana, India a r t i c l e i n f o Article history: Received 29 December 2009 Received in revised form 27 September 2010 Accepted October 2010 Available online 29 October 2010 Keywords: Modeling Simulation Biomass gasification Equilibrium Kinetics Suction gasifier a b s t r a c t An ‘EQB’ computer program for a downdraft gasifier has been developed to predict steady state performance Moving porous bed of suction gasifier is modeled as one-dimensional (1-D) with finite control volumes (CVs), where conservation of mass, momentum and energy is represented by fluid flow, heat transfer analysis, drying, pyrolysis, oxidation and reduction reaction modules; which have solved in integral form using tri-diagonal matrix algorithm (TDMA) for reaction temperatures, pressure drop, energetics and product composition Fluid flow module relates the flow rate with pressure drop, while biomass drying is described by mass transfer 1-D diffusion equation coupled with vapour–liquid-equilibrium relation When chemical equilibrium is used in oxidation zone, the empirically predicted pyrolysis products (volatiles and char) and kinetic modeling approach for reduction zone constitutes an efficient algorithm allowing rapid convergence with adequate fidelity Predictions for pressure drop and power output (gasifier) are found to be very sensitive, while gas composition or calorific value, temperature profile and gasification efficiency are less sensitive within the encountered range of gas flow rate Ó 2010 Elsevier Ltd All rights reserved Introduction Thermochemical conversion of woody biomass under restricted supply of oxidant is among the most promising non-nuclear forms of future energy Besides utilizing a renewable energy sources, the technology also offers an eco-efficient and self sustainable way of obtaining gaseous fuel usually called producer gas It can be used in either premixed burners (dryers, kilns, furnaces or boilers) for thermal applications or in direct feeding of high efficiency internal combustion engines/gas turbines for mechanical applications After adequate cleaning up and reforming, the generated gas can also be used for feed high temperature fuel cells or for production of hydrogen fuel [1] For electric power generation applications, the motive power from prime mover such as IC engine or gas turbine can be connected to an electric generator to produce electric energy Applications of IC engines have proved to be the most efficient and least expensive decentralized-power-generation systems at lower power range Research efforts have been expanded worldwide to develop this technology cost-effective and efficient in lower power range Recent progression in numerical simulation techniques and computer efficacy become the effective means to develop more advanced and sophisticated models in order to provide more accurate ⇑ Tel.: +91 09416722212; fax: +91 01302484004 E-mail address: avdhesh_sharma35@yahoo.co.in 0196-8904/$ - see front matter Ó 2010 Elsevier Ltd All rights reserved doi:10.1016/j.enconman.2010.10.001 qualitative and quantitative information on biomass gasification In the present work, the objective is not merely to develop a theoretical model of a downdraft gasifier system, but also to develop an efficient algorithm that allow rapid convergence and adequate accuracy of predictions Presently, the gasification modeling techniques include the application of thermodynamic equilibrium, chemical kinetics, diffusion controlled, diffusion–kinetic approach and CFD tools None of approaches have clear advantage over the others Pure equilibrium approach has thermodynamic limitations, instead of its inherent advantage of being generic, relatively easy to implement and rapid convergence, even though, researchers have successfully demonstrated the application of equilibrium chemistry in downdraft gasifiers Zainal et al [2] reported an interesting model for biomass gasifier describing the equilibrium calculations considering water–gas shift and methane–char reactions Melgar et al [3] combine chemical and thermal equilibrium in order to predict gas composition and Baratieri et al [1] presented an equilibrium model based on minimization of Gibbs energy using Villars–Cruise–Smith (VCS) algorithm They validated the predictions successfully Later, Sharma [4] has compared the theoretical predictions of reduction zone using equilibrium, kinetic modeling and experimental data For optimum performance, Sharma has identified a critical length for the reduction zone (where all char gets converted) At a more sophisticated level, Ratnadhariya and Channiwala [5], suggested that separate thermodynamic modeling can be approached to different zones of a downdraft gasifier On the other hand, non-equilibrium formulations such as kinetic rate A.Kr Sharma / Energy Conversion and Management 52 (2011) 1386–1396 1387 Nomenclature A d f k M _ Dm R T V [C] D h L/l _ n_ m= DP Ru D t _ res r CV Dh hhv dL ME Q_ Re Y Sg area (m2) particle diameter (m) friction factor thermal conductivity/rate constant mass solid mass conversion thermal resistance temperature (K) volume/velocity species concentration diffusion coefficient/diameter enthalpy (kJ/kg) length (m) mass/molar flow rate pressure drop universal gas constant residence time in CV diameter ratio of annulus control volume hydraulic diameter (m) high heating value (kJ/kg) length of CV methane-equivalent heat flow or release/absorbed Reynolds number mass fraction or ratio specific gravity Greek letters l dynamic viscosity (kg mÀ1 sÀ1) erad radiative emissivity q density r Stefan–Boltzmann constant and/or diffusion controlled model including CFD tools are more accurate, no doubt, but are detailed and computationally more intensive It takes time for convergence by a few orders of magnitudes [6] Non-equilibrium approaches use char conversion as a surface phenomena describing by char reactivity and global reactions of char–gas and gas–gas reactions An effective global rate constant may be defined to account for both diffusion and kinetics of these reactions Wang and Kinoshita [7] modeled the kinetics of the heterogeneous and homogeneous reactions of char conversion in reduction zone for a given residence time and bed temperature, while Giltrap et al [8] used the reaction kinetics parameters reported by Wang and Kinoshita in order to develop a model of the gas composition and temperature for char reduction zone of a downdraft gasifier Babu and Sheth [9] further modified these reaction rates using a variable char reactivity factor to predict the results agreeing with experimental data Later, Gao and Li [10] presented the downdraft gasifier model by combining a pyrolysis model (based on Koufopanos scheme) and reduction model following [7–9] to simulate the temperature field and gas concentration field in time and space The overall pressure drop across the gasifier system is an important parameter It monitors not only the health of a suction gasifier but also the volumetric efficiency of engine and hence the engine power output The pressure drop across the conventional packed bed depends on system geometry, medium porosity, permeability and physical properties of working medium Unlike, in gasifiers the bed maintains widely varying temperature specifications, particle size distribution and bed porosity Such study on pressure drop through a downdraft biomass gasifier bed is limited in open x eb n humidity bed porosity correction factor for annulus Subscripts/superscripts i number of CVs A/a ambient cel cellulose DB dry biomass dev developing flow mfd modified fully developed preheat preheating zone tuyer sat saturated vapour j reaction number an annular region hc hemicellulose dry drying zone f fluid (gas/air) p particle pg producer gas vol/v volatile w moisture k species ash ash lg lignin eff effective fd fully developed flow pyr pyrolysis s solid (biomass, char, ash) red reduction zone DBp mass percentage in dry biomass literature Sharma [11], measured the pressure drops across the gasifier bed at various particle size arrangements in cold and hot flow, and at various locations of a 20 kWe open top downdraft gasifier in addition to temperature profile, gas composition, calorific value These data has been used in this work In fact, the selection of level and modeling approach (viz chemical equilibrium, chemical kinetics or diffusion controlled) depends on statement of the problem and therefore may vary considerably from one case to another Since, the objective of the present work is not to invoke the highest level or most sophisticated gasifier model, yet it is an attempt to develop an efficient algorithm that enable rapid convergence without affecting the validity Such comprehensive work, in fact, is missing in the archival literature This comprehensive work, therefore, presents the modular treatment (allowing scope of further improvement at module level) to fluid flow, heat transfer, biomass drying, pyrolysis, and oxidation and reduction reactions processes to form a powerful tool for simulation of suction (downdraft) gasifier Here biomass drying has been described via thermal equilibrium, where mass transfer determines the rate of moisture removal from wet biomass particles Devolatilization rate and pyrolysis products is described by single pseudo-first order reaction and empirical model, chemical equilibrium for rapid convergence in oxidation zone (>800 °C), and kinetic scheme for reduction zone ( 4000 (3c) Porous Bed: Ergun equation [14] DP i ¼ 150ð1Àeb;i Þ2 lðT i Þli qðT i Þe3b;i d2p;i AT _ f Þi þ ðm Concentric annulus [15]   DP an ¼ K dev þ fmfd DdLeff1 Reeff = ReDh/n; _ n¼ (5) _ 2pg m Deff = Dh/n _ _ ð1À r Þþð1À r Þ2 = lnð r Þ (4) 2qpg A2an _ ð1À r Þ2 ð1À r Þ _ 1:75ð1Àeb;i Þli _ f Þ2i ðm qðT i Þe3b;i dp;i A2T _ where r ¼ r o =ri (5a) (5b) are straight pipes of circular cross-section, the pressure drop can be computed from the Darcy–Weisbach equation The entrance and developing flow effect through the tuyers has been modeled in terms of average entry length pressure drop parameter Kdev, fitted to the data of Schmidt and Zeldin in Ref [13] as given in Table The pressure drop through the gasifier bed (maintaining widely varying temperature specifications, particle size distribution and bed porosity) has been obtained using Ergun correlation [14] for complete flow regime Pressure drop through concentric annulus is modeled from modified Darcy–Weisbach friction-factor in terms of effective Reynolds number and effective (annulus) diameter as reported in [15] The details of equations describing the flow resistance across the tuyers, porous bed and annulus are given in Table 2.2 Heat transfer For heat transfer analysis, the approach of Sharma et al [16] has been followed in the present work Here, fuel bed is assumed to be isotropic; solid and gases are considered to be in local thermal equilibrium These assumptions are justified for fixed bed gasifiers operating under steady state conditions, since residence time of solids in the CV is two to three order magnitude higher than that of gases This module describes the formulation of energy interaction for the heat inflows and outflows due to advection of fluid and solids, heat loss through insulated wall, internal thermal interaction between adjacent CVs and the quantity of heat generated or consumed in each CV in order to compute the reaction temperature of each CV In developing heat transfer module, the heat generated/absorbed during drying, pyrolysis, oxidation or reduction is prescribed as input These would subsequently be determined by the modules of the respective sub-processes (cf Eq (6) in Table 2) Table Heat transfer equations Energy equation hP i P _ _ _ _ _ solid ðmi Cpi Þin þ gases ðmi Cpi Þin T in þ Q v ap þ Q pyr þ Q oxid hP i P _ P _ _ _ þQ red þ jk Q dif ;jk ¼ solid ðmi Cpi Þout þ gases ðmi Cpi Þout T out (6) Effective thermal conductivity [16] Reduction zone Gas Ash Fig Zonal description of the gasifier 2k k C ðln C þC Þ s f þ keff ¼ kg ðln C þC ÞÀks C ks d2ct dp ð1Àerad Þ þ 4rXdp T and X ¼ eb =1 þ 2eebrad ð1Àeb Þ Thermal resistance for ith zone DT ¼ jk ; where Rsi = Rt(i,bed) + Rt(i,ins) + Rt(i,o) Q_ dif ; jk Rsi where jk = up, down, side (7) (8) 1389 A.Kr Sharma / Energy Conversion and Management 52 (2011) 1386–1396 Fig Single CV used in heat transfer module with all thermal interactions using enthalpy of formation of reactants and products The transfer of energy between adjacent CVs due to fluid and solid particles motion is accounted for by the mass flow rate, temperature of the fluid and solid flows and all heat transfer interactions including the radial outward (heat loss) from the bed to surroundings have been modeled using thermal resistance as shown in Fig The details of equations representing the heat transfer module for each CV are given in Table The total resistance to radial heat loss to the surroundings in the ith zone of the gasifier bed is given as the sum of resistances due to granular bed, insulation and the outer surface of the reactor (cf Eq (8)) In the preheating zone, there is an additional resistance due to the annular jacket The axial heat transfer of the porous gasifier bed has been modeled by considering advection of solid (biomass/char) and fluid (air/gas) streams, while conductive and radiative heat fluxes at boundaries of each CV have been modeled in terms of effective thermal conductivity, Keff, following Sharma et al [16] The keff model needs inputs in terms of bed temperature, particle size and bed porosity at current location Here, bed porosity varies with current particle size and modeled using Eq (2), while emissivity of char particles is fixed at 0.75 2.3 Thermochemical processes Modeling of the biomass thermo-chemical conversion phenomena: preheating, drying and pyrolysis, and chemical reactions: oxidation and reduction in a downdraft gasifier has been presented to predict the rate of heat generation/absorption in each CV and outflow products 2.3.1 Biomass drying The mechanism of moisture transfer to woody biomass includes diffusion through the fluid film around the solid particles and diffusion through the pores to internal adsorption sites The actual process of physical adsorption is practically instantaneous, and equilibrium can be assumed to exist between the surface and the fluid envelope As moist biomass particles came into contact with air having low humidity level, the particles tend to lose moisture to the surrounding air until equilibrium is attained For modeling, following assumptions are made: No shrinkage in particle due to moisture evaporation Temperature gradient in moist biomass particles is neglected Equilibrium can be assumed to exist between the surface and the fluid envelope Drying is allowed to continue through pyrolysis zone as well as oxidation and reduction zones as well The local thermal equilibrium between the gaseous and solid media is assumed in each control volume, which makes it implicit that heat transfer between the solid and gases is much faster than the mass transfer Thus, mass transfer determines the rate of moisture removal from the biomass particles to the gases/air flowing around them The analytical solution for one-dimensional mass diffusion in a spherical particle of wood [17] is used in this work Equations representing the drying process with coefficients are listed in Tables and 2.3.2 Pyrolysis of biomass In downdraft gasifier, the pyrolysis process is modeled at slow heating rate to predict pyrolytic yields (viz., volatile composition and char) and devolatilization rate as a function of temperature and residence time The biomass particles shrink on pyrolysis giving char and ash Following assumptions are invoked:  Char and biomass particles are non porous  Char yields from cellulose, hemicellulose and lignin considered to be pure carbon  Char yield in the gasifier is insensitive to pyrolysis temperatures encountered in the pyrolysis zone  The complex constituents of volatiles are assumed to be decomposed into CO, H2, CO2, H2O, tar (heavy hydrocarbons) and light hydrocarbons (mixture of methane and ethylene) The whole process of thermal decomposition of dry biomass can be represented by a single equation as: kdry Dry biomass ðDBÞ ! Char þ Volatiles ðCO; H2 ; CO2 ; H2 O; Methane-Equivalent & TarÞ ð14Þ Table Equations representing to moisture evaporation Diffusion equation [17] X in ÀX eqb X out ÀX eqb ¼ p82 ðeÀðp=2Þ where b ¼ 4Ddif t res d2p b þ 19 eÀ9ðp=2Þ ; tres ¼ b (9) þ Þ M b;CV _b m Simpson [18] relationship   2 Kh þ K Khþ2K K K h2 X eqb ¼ 1800 W 1ÀKh (10) 1þK Khþ2K K K h where W = 349 + 1.29 (T À 273) + 0.0135 (T À 273)2 K = 0.805 + 0.000736 (T À 273) À 0.00000273 (T À 273)2 K1 = 6.27 À 0.00938 (T À 273) À 0.000303 (T À 273)2 K2 = 1.91 + 0.0407 (T À 273) À 0.000293 (T À 273)2 (11) Relative humidity ratio air h ¼ xxair;sat ¼ p mwxwair=p mwair (12) Antoine equation [19]   B log10 ðpv ;sat Þ ¼ A À TþC (13) v ;sat a Table Coefficients for Antoine equation for saturation vapour pressure [19] Temperature range (K) A B C 255.8–373 379–573 4.6543 3.55959 1435.264 643.748 À64.848 À198.043 1390 A.Kr Sharma / Energy Conversion and Management 52 (2011) 1386–1396 On heating, these constituents become unstable and decompose into char and volatiles Furthermore, the volatiles break-up into various lighter hydrocarbons For describing the volatile composition and char yield during slow pyrolysis of the biomass, the present work follows the approach of Sharma et al [20], where the thermal degradation of biomass constituents has been described by individual decomposition scheme of cellulose, hemicellulose and lignin Model uses mass fractions of cellulose (Ycel), hemicellulose (Yhc) and lignin (Ylg) in biomass as input information given in Table The chemical composition can be obtained from the elemental balance knowing the mass fractions, chemical formulas and molecular masses of cellulose, hemicellulose and lignin The rate of devolatlization of biomass during slow pyrolysis process can be described by a single pseudo-first order reaction as given by Eq (15) in Table Each of the three constituents of dry and ash-free biomass, viz., cellulose, hemicellulose and lignin are considered to break up into a fixed fraction of char and volatiles as described by Eqs (16) and (17) in Table These fractions of char from these three constituents along with their chemical formula are presented in Table Six species are considered to be part of the volatiles, viz., CO, CO2, H2, H2O, C1.16H4 (ME) and C6H6.2O0.2 (tar) following [24] Thus, the process of pyrolytic decomposition of dry and ash free biomass C6HHBOOB can be represented as: C6 HHB OOB ¼ C1 Hchar Ochar þ n_ v CO þ n_ v CO2 þ n_ v H2 þ n_ v H2 O þ n_ v C1:16 H4 þ n_ v C6 H6:2 O0:2 ð23Þ 2.3.3 Oxidation chemistry in gasifier bed The pyrolysis products get oxidized in short supply of oxygen in the oxidation zone (near air tuyers) of a gasifier Owing to the widely varying reaction equilibrium constants and the reaction time scales, some of the reactions might not be attaining equilibrium in the oxidation zone, and hence the solution of full equilibrium equations to compute oxidation process in the gasifier would both be erroneous and numerically difficult In the present work, therefore, a heuristic approach is adopted Oxidation of the pyrolysis products is allowed to consume the available oxygen in a sequence of descending order of reaction rates as described below: Table Proportion of cellulose, hemicellulose and lignin in hardwood [21] Type of wood Cellulose (Ycl) Hemicellulose (Yhc) Lignin (Ylg) Hardwood 0.43 0.35 0.22 Table Equations representing to pyrolysis model Rate of devolatilization [22] dMv ol dt ¼ Àkpyr M v ol ¼ À7:0  107 ðsÀ1 Þ expðÀ1560=TÞM DB Y v ol     dM v ol ¼ ðDt res Þi dmdtv ol dt _ v ol;i ¼ Dm i (15) (16) (17) Empirical mass ratios [20] (18) À1:8447896þ7730:317þ5019898 T Y CO=CO2 ¼ e Y H2 O=CO2 = Y ME=CO2 =  10À16T5.06 T (19) (20) Heat of pyrolysis [20]     o o o Dhpyr ¼ hf À Y char hf DB char À Y v ol Pk¼6 k¼1 Y k   o hf (21) k Biomass constituents Cellulose Hemicellulose Lignin Reference Fractional char yield Chemical formula 0.05 0.10 0.55 Tillman et al [21] C6H10O5 C6H10O5 C9H7.95O2.4(OCH3)0.92 Grobski et al [23] Oxidation of hydrogen (Reaction (R1) in Table 8) completes itself first If oxygen remains, light hydrocarbons are oxidized to H2O and CO (R3) Oxidation is fast, and is assumed to happen instantaneously whenever oxygen is available Products of oxygen are assumed to attain equilibrium in each CV If more oxygen remains, tar (R4) and char (R5) share the oxygen in the proportion of their reaction rate constants at the temperature of the CV under consideration to get oxidized to CO The principal chemical reactions taking place in the oxidation zone along with their rate expressions are listed in Table Although these expressions are not used in the present computations, they have been used only to guide the sequence of oxidation reactions described above If n_ V k stands for the molar flow rate (mol/s) of species k, then after completely consuming all the H2 in the gaseous phase (Reaction (R1) in Table 8), the O2 that would remain n_ V O2;1 ¼ n_ V O2 –n_ V H2 =2 If oxygen remains ðn_ V O2;1 > 0Þ, light hydrocarbon or methane-equivalent gets oxidized to CO and H2O, therefore, n_ V O2;2 ¼ n_ V O2;1 À 1:58n_ V CO If more oxygen remains ðn_ V O2;2 > 0Þ, simultaneous consumption of tar (Reaction (R4)) and char (Reaction (R5) in Table 8) start taking place The relative proportions of O2 consumed by these reactions has been accounted for by considering the ratio of the two reaction rates r* = kchar/ktar, where the reaction rates are obtained from Table Two cases can be discussed: one, when there is enough oxygen to oxidize all the tar and a proportionate quantity of char; and second, there is less oxygen than what is required to oxidize tar completely Oxygen remains after tar oxidation if n_ V O2;2 > ð1 þ r à Þð4:45n_ V tar Þ Here, 4:45n_ V tar mol/s of O2 is used up to oxidize tar and the remainder for char: thus, for every mole of char oxidized, r* moles of char are also oxidized (cf Reaction (R5)) In case n_ V O2;2 < ð1 þ r à Þð4:45n_ V tar Þ, all oxygen is consumed In this case, the molar rate of tar oxidation is n_ V O2;2 =½4:45ð1 þ rà ފ, and the tar that exits the zone is thus n_ V tar À n_ V O2;2 =½4:45ð1 þ rà ފ Correspondingly, rate of char oxidation is ½n_ V O2;2 rà =4:45ð1 þ rà ފ mol=s This gives the moles of char oxidized in the current CV If oxygen remains all of it is then used to oxidize CO in a likewise fashion Turns [27] quoted that for fuel-rich combustion, the water shift equilibrium equation can be safely applied, therefore we can write n_ V CO2 n_ V H2 =n_ V CO :n_ V H2O ¼ KpðT i Þ ¼ expðÀDG0 ðT i Þ=Ru T i Þ i Char yield [20] Ychar,ash-free = YclYchar + Yhcfchar + Ylg cchar Yvol = À Ychar,ash-free Table Fractional char yields from biomass constituents ð24Þ where DG0 ðT i Þ ¼ g 0CO ðT i Þ þ g 0H2 O ðT i Þ À g 0CO2 ðT i Þ À g 0H2 ðT i Þ Here, DG0(Ti) is the standard-state Gibbs function changes at atmospheric pressure The Gibbs function g0 for each species can be calculated using Eq (42) 2.3.4 Modeling reduction chemistry in gasifier bed Reduction of the oxidation zone products are primarily dominated by heterogeneous reactions of solid–char (R6)–(R8) and homogeneous reactions of gas–gas (R9) in complete absence of 1391 A.Kr Sharma / Energy Conversion and Management 52 (2011) 1386–1396 Table Chemical reactions in oxidation zone Reac no R1 R2 R3 R4 R5 R6 a b Oxidation reactions H2 + 0.5O2 ? H2O CO + 0.5O2 ? CO2 C1.16H4+1.58O2 ? 1.16CO +2H2O b C6H6:2o0:2 +4 45O2 ? 6CO + 3.1H2O a C + 1/2O2 ? CO CO + H2O M CO2+H2 Rate expressions 1.5 Aj 1.5 kH2 = ACOT exp(ÀECO/RuT)[C CO2 ][C H2 ] kCO = ACOexp(ÀECO/RuT)[CCO][C O2 ]0.25[C H2 O ]0.5 kME = ACH4 exp(ÀECH4 /RuT)[C O2 ]0.8[C CH4 ]0.7 0.5 ktar ffi kHC = AtarTP 0:3 A exp(ÀEtar/RuT)[C O2 ] [CHC] kchar = Achar exp(ÀEchar/RuT) [CO2] – Ej/Ru Ref 1.63  10 1.3  108 1.585  109 2.07  104 3420 15,106 24,157 41,646 [25] [25] [25] [26] 0.554 – 10,824 – [25] – C1.16H4 (light hydrocarbon or methane-equivalent) C6H6:2o0:2 (heavy hydrocarbon) represents the methane and tar respectively Table Reduction reactions, their reaction rates and constants Reac no Reaction R6 C + CO2 M CO R7 C + H2O M CO + H2 R8 C + 2H2 M CH4 R9 CH4+H2O M CO+3H2 Rate expression    CO r ¼ A1 exp ÀE P CO2 À KPeq;1 Ru T    PCO P H r ¼ A2 exp ÀE P H2 O À K eq;2 Ru T    PCH r ¼ A3 exp ÀE P 2H2 À K eq;34 Ru T    P CO P3H r ¼ A4 exp ÀE P CH4 P H2 O À K eq;4 Ru T oxidants These reduction reactions are inherently slower than the oxidation reactions by several orders of magnitude, thus, equilibrium may not be established in the reduction region At moderately high temperatures (