Article pubs.acs.org/EF
Kinetic Modeling of Tar and Light Hydrocarbons during the Thermal Conversion of Biomass Diego Fuentes-Cano,* Alberto Gómez-Barea, Susanna Nilsson, and Pedro Ollero Bioenergy Group, Chemical and Environmental Engineering Department, University of Seville, Camino de los Descubrimientos s/n, 41092 Seville, Spain ABSTRACT: A model is presented to predict the tar and light hydrocarbons yields during the thermochemical conversion of woody biomass in the temperature range of 700−1000 °C. It takes into account the tar generation during fuel devolatilization and the thermally induced secondary conversion of the tars and light hydrocarbons, accounting for a limited number of species and reactions representing the main conversion mechanisms at temperatures above 700 °C. The stoichiometry and kinetics of the reactions were comprehensively selected from literature. Comparison with measurements from the literature is presented to discuss the applicability of the model to both pyrolysis reactors and gasifiers. The model quantitatively predicts the yields of benzene, tar class 3 (toluene) and 4 (naphthalene), as well as the main qualitative trends of tar class 2 (phenol) and 5 (pyrene) under pyrolysis conditions for different temperatures and residence times. It exhibits larger deviations under autothermal gasification conditions, where partial combustion of volatiles might influence tar and hydrocarbon reactions, but still gives reasonable qualitative predictions. Comparison with other detailed models from the literature shows that the present work improves the prediction capability of existing models.
1. INTRODUCTION The depletion of fossil fuel reserves and the availability of different lignocellullosic resources, such as agricultural wastes, forest residues, or waste wood, make the use of these biomasses an interesting alternative for chemicals, transportation fuels, and energy production. Besides the direct use of these alternative biobased fuels in conventional boilers, alternative technologies like fast pyrolysis and gasification are being considered. Fast pyrolysis aims at maximizing the yield of the liquids, but the pyrolytic oil generated is highly reactive and corrosive. Conversely, biomass gasification is intended for the production of a fuel gas containing most of the energy of the original fuel. The most promising gasification technology is conducted in a fluidized bed (FB), but the presence of heavy tars in the gas reduces the process efficiency, restricting the use of the gas to applications where the gas is not cooled. Different strategies, lumped as primary or secondary measures, have been applied to reduce the tar concentration in the gas product. Primary measures aim at converting the tar within the gasifier whereas secondary methods are those removing (or converting) the tar downstream of the gasifier. The condensing behavior of the gas is characterized by the tar dew point, which depends on the tar concentration and composition,1 being especially sensitive to the amount of heavy polyaromatic hydrocarbons. The use of models for predicting the evolution of tar composition and condensation behavior under different conditions is helpful for designing new gas cleanup strategies. The conversion of a fuel particle in thermochemical reactors can be conceptually divided in two different processes: dryingdevolatilization (hereafter abbreviated as devolatilization) and secondary conversion. During devolatilization, the particle is heated to the reactor temperature and decomposed into three main fractions: char, tar, and light gas. The yield and composition of these fractions depend on different variables, such as the particle heating rate and gas temperature,2 whereas © 2015 American Chemical Society
they are rather insensitive to gas composition surrounding the fuel particles.3 The secondary reactions modify the composition of the gas generated in the devolatilization step, and the extent of these reactions depends on the reactor conditions, such as temperature, residence time, composition of the surrounding atmosphere, presence of catalysts, and so forth. Under this conceptual division, the tar is converted following two steps in series: devolatilization and secondary conversion.2 During the devolatilization of wood at a high heating rate, like in fluidized beds, the primary tars generated are predominantly aliphatic and oxygenated,4 being highly reactive at temperatures above 600 °C. 5,6 Because of the complexity of the devolatilization process, this step is usually modeled applying experimental correlations. Primary model tars and the correlations to calculate their yields are selected by taking into account the parent fuel and the devolatilization temperature. Depending on the reactor configuration, during the secondary conversion the primary tar is decomposed by thermal/catalytic cracking and reacts with oxygen/steam. As a result, the total amount of tar decreases, increasing the yields of light gas and hydrocarbons, and the chemical structures of the remaining tar molecules become more aromatic and less oxygenated. An usual approach to model the secondary tar conversion is to lump the tar compounds into a limited number of classes (generally represented by single model compounds) and to presume a certain scheme of reaction and stoichiometry.2 The simplest approach considers one lump, usually called gravimetric tar, comprising all of the tar compounds whose decomposition proceeds by any of the Received: September 18, 2015 Revised: November 24, 2015 Published: November 24, 2015 377
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1000 °C. The stoichiometry and the kinetics of the secondary reactions were obtained by analyzing data from the literature. Its prediction capability is discussed by comparison with measurements taken from the literature under pyrolysis and gasification conditions as well as with the results from the two detailed kinetic models discussed above.9,10
mechanisms mentioned. This approach does not take into account the tar properties and composition and is not useful for the design and optimization of cleanup processes. Because of the complexity, only a few models have been published considering a scheme of reaction comprehensive enough to represent the main conversion mechanisms of different tars.7−10 These works use model compounds representing different tar families and establish reaction schemes, including the main tar conversion mechanisms. Maki et al.7 simulated the pyrolysis of a heavy oil assuming instantaneous devolatilization, obtaining the yields of the different products experimentally in dedicated tests. The tar mixture was simplified by lumping the different compounds into three classes: aliphatic tar, aromatic tar, and BTX (benzene, toluene, and xylenes). Kinetics and stoichiometry of the secondary reactions were based on literature data or adjusted to fit with experimental data. Umeki et al.8 modeled a wood steam-blown gasifier into two sequential steps: a first step of devolatilization (in steam) giving a total tar yield that was divided into acetol, toluene, and naphthalene, whose individual yields were adjusted by elementary analysis. The stoichiometry and kinetics of secondary reactions were taken from the literature and include thermal cracking and hydrogenation reactions. The yields of the light gas predicted by the model were in good agreement with measurements but the tar composition was not included in the comparison. Font-Palma9 developed a model for wood gasification in a fluidized bed. The model considered the lignin fraction to be the only tar precursor during devolatilization, producing a defined mixture of three primary compounds (vanillin, guaiacol, and catechol) while the remaining fraction (wood minus the lignin fraction) produced a mixture of light gases, whose stoichiometry was not specified. The secondary reactions included thermal cracking, hydrogenation, steam reforming, and oxidation with kinetics taken from the literature. The model did not consider the conversion of some oxygenated tars, limiting its application given that these compounds are highly reactive at temperatures above 600 °C. Abdelouahed et al.10 presented a model to simulate a dual fluidized bed gasifier conceptually divided into three modules: pyrolysis and secondary reaction modules for the simulation of a steam-blown gasifier and a char oxidation module representing the char combustor. The biomass devolatilization was modeled by empirical correlations obtained in dedicated experiments where the yield of gaseous compounds and four tars (benzene, phenol, toluene, and naphthalene) were measured as a function of temperature. The secondary conversion model took into account steam reforming and hydrogenation and thermal conversion reactions of the tar compounds in both gas phase and over char surfaces. In a recent publication,11 a fluidized bed gasifier (FBG) was simulated using a detailed chemical kinetic model.12 The system was modeled as a series of two ideal gas-phase reactors representing the two main zones: the bubbling region was modeled as a continuously stirred tank reactor and the freeboard zone as a plug flow reactor. The model for the bubbling region used devolatilization yields calculated by an existing particle model.12 The gasifier model predicts well the light gases whereas it fails for tar compounds. The deviation is typically 1 order of magnitude for monoaromatic tars and 2 orders of magnitude for polyaromatic hydrocarbons. This work presents a kinetic model to predict the tar and hydrocarbon conversion at temperatures in the range of 700−
2. MODEL DEVELOPMENT 2.1. Approach and Applicability. The present model describes the homogeneous thermal conversion of tar and light hydrocarbons. Reactions with molecular oxygen are not considered in the model, restricting its application to pyrolysis reactors. However, the model is expected to reasonably predict tar conversion in autothermal FBG, because in these systems, only a small fraction of the stoichiometric oxygen is fed so that the amount of oxygen available for interaction with volatiles is limited and most of the reactor has reducing conditions. Additionally, in FBG, the rates of steam reforming reactions of tars are not expected to be significant at temperatures below 900 °C in the absence of catalyst.13,14 The presence of hydrogen may also influence the rate and the product distribution of secondary conversion of tar,15,16 but this effect seems to be limited for the typical hydrogen concentrations and/or residence times in FBG.17−19 Therefore, the model aims at representing the conversion behavior of tar and light hydrocarbons in conventional FBG and pyrolysis reactors. However, to simulate the whole process, the present model has to be used as part of a reactor model, i.e., together with other submodels, such as fluid-dynamics, char gasification, and attrition. The reactor modeling and how to integrate the present submodel in it is beyond the scope of the present paper. The model comprises two submodels. The first submodel simulates the fuel devolatilization, which is assumed instantaneous, where the primary tars and light gases are released from the fuel particle. The second submodel simulates the secondary conversion of volatiles using the initial concentration of the gases from devolatilization, the temperature, and the residence time of the gas (assuming plug-flow conditions) in the reactor as input. Figure 1 shows a schematic representation of the model concept, indicating the two main zones as well as the input and output data. 2.2. Devolatilization Submodel. The fuel devolatilization is modeled using the experimental correlations obtained by Neves et al.20 where the reactor temperature is used as reference. Neves et al. reviewed works on biomass pyrolysis in
Figure 1. Model concept illustrating the two main submodels (devolatilization and secondary conversion) as well as the inputs and outputs. 378
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Table 1. Yields of C, H and O Given by20 for the Tar Mixture and Those Calculated for the Acetol-Catechol Mixture in the Present Work as a Function of the Yield of Acetol (x) model tar mixture total yield (kg tar/kg daf wood) yield of C (kg C/kg daf wood) yield of H (kg H/kg daf wood) yield of O (kg O/kg daf wood)
tar input20
C3H6O2 (acetol)
C6H6O2 (catechol)
error
a Cin = Ct·a Hin = Ht·a Oin = Ot·a
x Cace = x(3·12/MWace) Hace = x(6·1/MWace) Oace = x(2·16/MWace)
a−x Ccat = (a − x)(6·12/MWcat) Hcat = (a − x)(6·1/MWcat) Ocat = (a − x)(2·16/MWcat)
|Cin − (Cace + Ccat)| |Hin − (Hace + Hcat)| |Oin − (Oace + Ocat)|
the range of 200−1000 °C, summarizing the main results and developing a pseudoempirical model for the estimation of the main product yields from wood pyrolysis (hydrogen, carbon dioxide, carbon monoxide, methane, water vapor, char, and total tar) valid for a wide range of conditions. Two inputs are necessary to obtain the yields of devolatilization products from the model developed by Neves et al.:20 the elemental composition of the fuel and the devolatilization temperature (Figure 1). Devolatilization temperatures between 500 and 600 °C are typical for mmsized particles like those found in fluidized beds. The devolatilization temperature is set at 500 °C because the yield and composition of tar predicted by the model are hardly sensitive to temperatures within the range of 500−600 °C. The global tar yield is calculated using the model developed by Neves et al., and it is assumed to be a mixture of two model tars: acetol (C 3H 6O 2) and catechol (C 6 H6 O2 ). These compounds are formed during the early stages of wood devolatilization when the original structure of the fuel is decomposed into lighter hydrocarbons. Catechol is a predominant structural entity of lignin,21 which is the main source of lignocellulosic pyrolysis tars, and acetol is a representative compound of the thermal degradation of cellulose. The mixture of primary tars, i.e., the yields of acetol and catechol, is calculated (see Table 1) by minimizing the sum of errors of the C, H, and O mass balance between the model tar mixture and the elemental composition of the (total) tar reported by Neves et al. (Cin, Hin, Oin) at the temperature of devolatilization. 2.3. Secondary Conversion. Figure 2 presents the scheme proposed for secondary tar conversion, and Table 2 contains
secondary conversion submodel are the gas residence time and reactor temperature. First order kinetics with respect to each reactant concentration were considered for the secondary tar conversion reactions. The kinetic parameters were taken from the literature or calculated from reported measurements. Besides the primary tars (acetol and catechol), the tar compounds considered in the model are phenol (representing heteroatomic tars or tar class 2, TC2), toluene (representing monoaromatic tars or tar class 3, TC3), naphthalene (representing aromatic tars with 2−3 rings or tar class 4, TC4), pyrene (representing aromatic tars with 4−7 rings or tar class 5, TC5), and benzene. These model tars were selected because they are the most abundant compounds of the tar classes considered and because their conversions have been widely studied and reported. The stoichiometries of reactions R-1/R-9 were assigned by analyzing the conversion products of the different model tars (or compounds with similar chemical structure) from literature works, studying their thermal degradation in relevant conditions to the processes to be simulated, i.e., temperatures between 700 and 1000 °C in absence of oxygen. Some of these stoichiometries include more than one decomposition mechanism, but there was not enough information to assign kinetics to the different mechanisms. The stoichiometry of the thermal decomposition of acetol (R-1) was assigned analyzing the results of noncatalytic steam reforming experiments of Ramos et al.22 with the kinetic parameters taken from Morf.23 The conversion of acetol is assumed to produce light gases only, so catechol is considered as the only source of secondary tar. The decomposition kinetics of catechol was obtained by fitting experimental results obtained by Ledesma et al.21 The stoichiometry of reaction R-2 was set assuming that the main products of the thermal decomposition of catechol (and other “primary” tars, such as guaiacol and anisole24,25) are carbon monoxide and lighter hydrocarbons, such as cyclopentadiene and phenol.21 Toluene, despite not being accounted for by Ledesma et al.,21 has been included by the occurrence of Diels−Alder reactions involving these light hydrocarbons followed by the dehydrogenation of the cyclic hydrocarbon formed. The conversion of small and unsaturated hydrocarbon chains, here represented by butadiene, proceeds mainly by two different routes: C−C bond scission to produce lighter hydrocarbons and Diels−Alder/dehydrogenation reactions producing aromatic compounds. The stoichiometry of reaction R-3 was obtained considering measurements from Fairburn et al.26 and Xu and Tomita,27 and the kinetics used were taken from the C3H8 decomposition kinetics given in Xu and Tomita.27 The conversion of phenol was assumed to proceed by decarboxylation, producing CO and cyclopentadiene.28,29 The cyclopentadiene was further decomposed to light hydrocarbons21 or PAH through Diels−Alder/dehydrogenation reactions.23,30,31 It was considered that the main products of
Figure 2. Scheme of reaction proposed for secondary tar conversion. Hydrogen was not included in the scheme for a better visualization.
the stoichiometry and the kinetics of the reactions considered in the model. The simulations were conducted assuming a plugflow reactor with isothermal conditions. The initial concentrations for the secondary reactions are obtained from the devolatilization submodel. Other inputs needed for the 379
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reaction
stoichiometry
k0 (s )
R-1 R-2 R-3 R-4 R-5 R-6 R-7 R-8 R-9
C3H6O2 → 2 CO + CH4 + H2 C6H6O2 → 0.08 C7H8 + 0.15 C6H6O + 0.63 C4H6 + 0.17 CH4 + 1.85 CO C4H6 → 0.07 C10H8 + 0.2 C6H6 + 1.05 C2H4 + 0.02 H2 C6H6O → 0.25 C10H8 + 0.42 C6H6 + CO + 0.75 H2 C7H8 → 0.17 C10H8 + 0.89 C6H6 + 0.67 H2 C2H4 → C2H2 + H2 C10H8 → 0.625 C16H10 + 0.785 H2 C6H6 → C2H2 + 0.25 C16H10 + 0.75 H2 C16H10 → 16 C + 5 H2
R-10 R-11 R-12
CH4 + H2O → CO + 3 H2 CO + H2O → CO2 + H2 CO2 + H2 → CO + H2O
5.00 × 10 2.25 × 1010 5.01 × 1012 4.34 × 1011 2.23 × 1013 5.01 × 105 1.94 × 1013 2.14 × 1010 7.94 × 1023 k0 (m3 kmol−1 s−1) 3.00 × 108 4.09 × 103 1.41 × 105 5
Ea (kJ/mol)
ref
99.9 198 260 263 315 155 326 264 536 Ea (kJ/mol) 125 20.2 54.2
23 24 28 30 30 28 34 35 36 37 3 3
Table 3. Summary of the Main Experimental Conditions Used in the Experimental Works Selected from the Literature for Comparison with the Model Results author
reactor
carrier gas
fuel
temperature (°C)
Thomas et al.37 Zhang et al.38
tubular plug-flow drop-tube
N2 N2
catechol (doped) cypress sawdust
600−1000 800−1200
Dufour et al.39
tubular
N2
spruce wood
700−1000
van Paasen et al.40
FBG
air (experimental), N2 (simulation)
willow wood
740−940
gas residence time (s) 0.3 3.7 (800 °C), 2.6 (1200 °C) 2.7 (700 °C), 1.1 (1000 °C) 4
feed rate 0.65 mol % of C in the carrier gas 65 g/h 10 g/h (assumed) 1 kg/h
a model tar (catechol) in an inert atmosphere,37 two studying the secondary conversion of the volatiles produced during wood pyrolysis in a drop-tube reactor38 and in a tubular reactor,39 and one analyzing the composition of the tar mixture during FBG at different temperatures.40 These works were selected because they represent multiple reactor configurations and report detailed analysis of the products, including the yields of tar and some hydrocarbons. For a comparison between the model developed here with existing models, two models from the literature, Font-Palma’s model9 and Abdelouahed et al.’s model10 were also compared, when possible, with the experimental results (from refs 38 and 39). Font-Palma’s model9 was implemented considering only the decomposition of the lignin fraction, so the tar composition predicted using this model has to be considered as an approximation. The kinetics of secondary reactions were taken directly from ref 9. Abdelouahed et al.’s model10 was implemented taking the empirical correlations of the yield of gaseous compounds and tars given by the authors to simulate the primary pyrolysis as well as the same kinetics of homogeneous secondary reactions used by the authors. Note that, in the present work, only the homogeneous reactions and the soot gasification were taken into account, assuming that the interaction between gas and char is negligible.
phenol conversion (R-4) are benzene, naphthalene (TC4), and hydrogen. The kinetic parameters for R-4 were taken from Bruinsma et al.29 The toluene evolution is influenced by the concentration of hydrogen in the bulk gas. In the absence of a catalyst with a hydrogen-rich atmosphere, only lighter compounds are produced15 with benzene being the main conversion product. However, in an inert (Ar) atmosphere, both lighter hydrocarbons29 and heavier PAHs are produced.32 In the present model, because the concentration of hydrogen in pyrolysis reactors (and in air-blown fluidized bed reactors) is relatively small, the kinetics and stoichiometry of toluene conversion was obtained considering the data obtained under inert atmosphere.29,32 The kinetics of dehydrogenation of ethylene to produce acetylene (R-6) was reported by Xu and Tomita.27 The naphthalene conversion is assumed to proceed by dehydrogenation into pyrene and hydrogen (R-7). The kinetic parameters for this reaction were determined by fitting measurements from Bruinsma et al.33 The stoichiometry of benzene conversion (R8) was adjusted analyzing the works of Bruinsma et al.29 and Laskin and Lifshitz34 with the kinetic parameters taken from ref 34. The only reaction considered to produce soot in the present model is the dehydrogenation of pyrene (R-9), whose rate is assumed to be the same as that of the “6 ring” formation reported in Ledesma et al.35 The kinetic parameters for reactions R-10 through R-12, representing the homogeneous steam reforming of methane (R-10) and the water−gas shift reaction (R-11 and R-12), were taken from Jones and Lindstedt36 and Nilsson,3 respectively. 2.4. Model Verification. The model developed was tested by comparison with four experimental works selected from the literature (see Table 3): one studying the thermal conversion of
3. RESULTS AND DISCUSSION 3.1. Literature Data Simulation. Table 3 summarizes the operating conditions tested in the different works, including the range of temperatures, the gas residence time, and the fuel feeding rate. For simulation of the different experimental conditions, the fuel composition, reactor temperature, and gas 380
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Figure 3. Experimental data37 (solid circles) and predictions from the model developed here (open circles).
Figure 4. Experimental data38 (solid circles) and predictions from the models (open circles: this model; open triangles: Font-Palma’s model; open squares: Abdelouahed’s model).
residence time are required as inputs. The nitrogen flow rate was calculated to obtain the gas residence time. Thomas et al.37 This work studies the thermal (and oxidative) conversion of catechol at temperatures in the range of 500−1000 °C, making a detailed analysis of the decomposition products. A summary of the operating conditions is given in Table 3. The gas stream was doped
with the model tar compound (catechol). This way of operation is difficult to simulate with the two literature models,9,10 so the comparison of the measurements from ref 37 will be made only with the model developed here. In ref 37, the input concentration of catechol and the gas residence time are maintained constant using a catechol saturator operating at a constant temperature and varying the flow of the carrier gas 381
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Figure 5. Experimental data39 (solid circles) and predictions from the models (open circles: this model; open triangles: Font-Palma’s model; open squares: Abdelouahed’s model).
(N2).41 The reactor was designed to ensure plug-flow of the gas and to maintain isothermal conditions. Figure 3 compares the experimental data37 with the model results. The trends of the different tar classes and hydrocarbons with temperature are well predicted as well as the yields of heavy tars (TC4 and TC5). The yields of tar class 2 (TC2), 3 (TC3), and light hydrocarbons (LHC) are overestimated at intermediate temperatures (700−900 °C), whereas benzene is overestimated at temperatures above 900 °C. The overestimation of TC2, TC3, and LHC is probably due to the presence of compounds that were not measured in ref 37 and are present in small amounts as discussed in ref 4. This supposition is consistent with the difficulty to close the mass balances in ref 37 (the mass balances were within 98−101% for most temperatures except for temperatures in the range of 800−900 °C at which the mass balances were between 92 and 95%). Another reason for this overestimation could be the higher catechol conversion predicted by the model. At temperatures over 900 °C, the model overestimates the benzene yield and underestimates the yield of LHC. This behavior could indicate that, under the experimental conditions tested in ref 37, the decomposition kinetics of benzene used in the model are slower than the actual kinetics. Zhang et al.38 This group studied the thermal conversion of the pyrolysis products of a woody biomass generated in a nitrogen-blown drop-tube reactor in the temperature range of 600−1400 °C with gas residence times of 2−4 s. A comparison was made using the three models. The simulation was conducted reproducing the conditions reported in ref 38 (second row in Table 3). When using the model developed by Font Palma, a lignin fraction in the original wood of 25 wt % was assumed (input necessary for that model). Figure 4 presents the comparison between the results obtained by the different models and the experimental data. It is shown that the present model predicts well the yield of TC2 in the whole range of temperatures, whereas the model by Font-Palma gives much higher yields of TC2 (right axis) compared to the measurements, probably because this model includes two heteroatomic compounds that once formed are
not converted by any reaction. In contrast, the model developed by Abdelouahed et al. gives almost complete conversion of TC2 even at the lowest temperature. The trends of TC3 and TC4 are well predicted by both the present model and Abdelouahed et al.’s model, although the latter presents larger deviations. Conversely, Font-Palma’s model largely overestimates (right axis) the yields of TC4, indicating that the behavior of PAH compounds is not properly reproduced. The trends of soot and TC5 are predicted qualitatively by the model proposed here, but the yields of TC5 at temperatures up to 1000 °C and that of soot at temperatures above 1100 °C are significantly lower than the measurements. This deviation can be due to the absence of a reaction taking into account the acetylene polymerization reaction, which produces heavy PAH and soot at high temperatures. The overestimation of the acetylene above 1000 °C predicted by the model supports this hypothesis. In any case, the present model improves the results of soot formation obtained with the model from Abdelouahed et al. The trends of benzene, methane, and ethylene are properly predicted by the three models, although the present model gives the most accurate estimations. Dufour et al.39 This group studied the formation and secondary thermal conversion of the volatiles produced during the pyrolysis of wood chips in a tubular reactor. The temperatures tested ranged from 700 to 1000 °C, and the residence time of the vapors varied between 2.7 s at 700 °C and 1.1 s at 1000 °C. The main characteristics of the experiments are summarized in Table 3 (third row). The experiments were simulated assuming a biomass feeding rate of 10 g/h (batchwise experiments in the paper). Figure 5 presents the comparison between the tree models and the measurements. It is seen that the yield of benzene is properly predicted by the three models, whereas that of TC2 is not accurately predicted by any model below 900 °C. Abdelouahed’s model predicts complete conversion for TC2, whereas the present model reports yields higher than the measurements. The model by Font-Palma greatly overestimates the yield of TC2 (right axis). The actual yield of TC3 is poorly fitted by the two literature models, whereas the present model 382
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Figure 6. Experimental data40 (solid circles) and predictions from the model developed here (open circles).
From the comparison made, the ability of the three models to predict the various tar classes is summarized as follows: • TC2 yields are overestimated by the present model. Since polyaromatic tars are not overestimated (TC2 is the main source of these), two different explanations are possible. TC2 could be underestimated by the measurements (which is reasonable because typical GC analyses of tars only report a small fraction of TC2 even at temperatures as high as 900 °C42), or the model might overestimate TC2, indicating that any of the mechanisms of polyaromatics formation, such as the Diels−Alder/dehydrogenation reaction of unsaturated hydrocarbons, have a higher influence than that considered in the model. The models of Font-Palma and Abdelouahed et al. overestimate and underestimate (actually gives complete conversion) of TC2, respectively. • TC3 is properly predicted by the present model for pyrolysis conditions, whereas it is overestimated when simulating autothermal gasification conditions, indicating that oxygen reacts with TC3 or TC3 precursors (not considered in the model). The model of Font-Palma fails to predict TC3 under pyrolysis conditions because, in that model, the TC3 is only considered to be generated by reactions with oxygen, whereas Abdelouahed et al.’s model gives much higher yields of TC3 compared to the experimental results for temperatures below 1000 °C. • TC4 is accurately predicted by the present model, exhibiting deviations within ±10 g/kgdaf fuel. The other two models studied9,10 both fail to predict TC4 under pyrolysis conditions. The former9 because the only mechanisms considered for naphthalene conversion are combustion and steam reforming, whereas anthracene, the other TC4 included in the model, is not converted once it is formed. In the second model,10 the rate of thermal cracking of TC4 is very low (the tar yield predicted by the devolatilization submodel is similar to that obtained using the complete model). • TC5 trends are reasonably tracked by the present model, but the yields are underestimated, indicating that reactions producing TC5, such as the HACA (hydrogen abstraction acetylene addition) mechanism, should be included in the
predicts it reasonably well. TC4 is equally well predicted by both the present model and Abdelaouhed et al.’s model, whereas Font-Palma’s model largely overestimates TC4 (right axis). The yields of TC5 or soot are not available from the measurements,39 so it is not possible to make the comparison. The yields of methane and ethylene are estimated well by the present model and by the Abdelouahed et al. model, although it is seen that our model significantly subestimates the yield of ethylene at the lowest temperature (700 °C). van Paasen et al.40 This group reports the product composition from wood gasification in an atmospheric airblown fluidized bed between 740 °C (ER = 0.23) and 940 °C (ER = 0.29). Details of the experiments are given in Table 3 (fourth row). For the simulations, a volatiles residence time of 4 s was estimated considering the fuel feed rate and ER reported in ref 40. Although the reactions of tars and light hydrocarbons with oxygen were not considered in the present model, this comparison is included here to preliminarily assess the influence of oxygen on the tar composition under FBG conditions. The comparison is made only with the model developed in the present work. Figure 6 provides the comparison, showing the largest difference for TC2 followed by TC3, which are largely overestimated by the model. In general, the trends of TC4 and 5 are estimated reasonably well by the model, although the error in predicting the actual yields is slightly higher than the comparison made above in pyrolysis conditions. Benzene yield is properly predicted by the model. Except for benzene and TC5, the experimental yields are overestimated by the simulations. This indicates that the partial combustion of the volatiles taking place under gasification conditions influences the yields of the different tar compounds. It is speculated that during fluidized bed gasification the radicals produced in the bottom zone due to the combustion reactions promote the decomposition of TC2 and TC3 into light hydrocarbons. These light hydrocarbons would enhance the benzene and PAH formation in the upper zone of the reactor, where the oxygen concentration is negligible, compensating partially the reduction of PAH formation via TC2 and TC3 decomposition. 383
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Energy & Fuels
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model once its kinetics are known (it was not found in the literature under the conditions simulated). • Benzene yield is well-predicted by the three models, yielding a maximum between 900 and 1000 °C, although the benzene conversion mechanism differs substantially between the three models (thermal cracking in the present model, steam reforming in the Abdelouahed et al. model, and partial combustion in the Font-Palma model)
4. SUMMARY AND CONCLUSIONS A model was developed to estimate the composition of the main tar classes and hydrocarbons at temperatures in the range of 700−1000 °C during the thermochemical conversion of lignocellulosic biomass. It includes two submodels in series: the first for devolatilization, where the primary volatiles are generated, and a second where the volatiles are further converted by secondary reactions. The devolatilization model is based on a previous work,20 predicting the total tar yield released from the particle. Here, the total tar yield is considered to be distributed into two main primary tars (acetol and catechol). The secondary conversion submodel considers the subsequent conversion of the two primary tars by 13 reactions involving 4 secondary tars representing the main tar classes (phenol/TC2, toluene/TC3, naphthalene/TC4, pyrene/TC5), soot, four light hydrocarbons (benzene, butadiene, ethylene, and acetylene), light gases (CO, CO2, and H2), and H2O. The mechanism and the kinetics of the reactions involved were comprehensively developed based on previous fundamental studies and measurements from the literature. The present model predicts well the yields of TC3, TC4, and benzene as well as the main trends of TC2 and TC5 under pyrolysis conditions for different temperatures and residence times. It exhibits larger deviations under autothermal gasification conditions because partial combustion of volatiles seems to influence the tar and hydrocarbon reactions. Comparison with other detailed models from literature shows that the present model gives more accurate predictions.
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AUTHOR INFORMATION
Corresponding Author
*Fax: +0034-954486082. E-mail:
[email protected]. Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS The authors acknowledge the Junta de Andalucı ́a for its financial support in the project P12-TEP-1633 MO (FLETGAS2).
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ABBREVIATIONS BTX = benzene, toluene, and xylenes ER = stoichiometric ratio FB = fluidized bed FBG = fluidized bed gasifier GC = gas chromatography HACA = hydrogen abstraction acetylene addition LHC = light hydrocarbon PAH = polyaromatic hydrocarbon TC = tar class
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DOI: 10.1021/acs.energyfuels.5b02131 Energy Fuels 2016, 30, 377−385
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