Effect of Particle Size on Low-Temperature Pyrolysis of Woody

Article pubs.acs.org/EF

Effect of Particle Size on Low-Temperature Pyrolysis of Woody Biomass Hayat Bennadji,†,§ Krystle Smith,†,∥ Michelle J. Serapiglia,‡,⊥ and Elizabeth M. Fisher*,† †

Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, New York 14853, United States New York State Agricultural Experiment Station, Department of Horticulture, Cornell University, Geneva, New York 14456, United States

‡

S Supporting Information *

ABSTRACT: When biomass is thermochemically processed, the size of the biomass particles affects processing time requirements and yields. This study investigates the effects of particle size at the centimeter scale on pyrolysis through both experimental and modeling approaches, with three types of woody biomass; poplar, pine sapwood, and pine heartwood. Large (D = 3.81 cm) and small (D = 2.54 cm) wood spheres were pyrolyzed under thermally thick conditions at three final pyrolysis temperatures in a reactor with turbulent gas flow; the same wood materials were also pyrolyzed in a thermogravimetric analyzer (TGA), under kinetic control. The experiments were simulated using a previously published 1-dimensional pyrolysis model, which includes transport and kinetics of solid to vapor reactions for biomass components. Particle size had a strong effect on devolatilization timing and also affected the yields of some species. The model was successful at predicting the qualitative features and approximate magnitudes of quantities such as temperature overshoot, product yields for thermally thick particles, and devolatilization timing in both TGA and thermally thick particles. However, the dependence of yields and timing on wood type and particle size were not reproduced by the model.

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INTRODUCTION The chemical energy stored in biomass has the potential to make a major contribution to human energy needs. Pyrolysis is a key but imperfectly understood process in thermochemical routes for biomass utilizationwhether it occurs alone or as a step toward gasification or combustion. Many industrial biomass thermal conversion processes, such as fixed-bed gasification or grate firing, use feedstocks with relatively large particle size, i.e. wood chips, avoiding the expense of grinding the biomass to kinetically controlled sizes.1 Most previous scientific investigations of biomass size effects reported the comparison between submillimeter-scale and macroscopic wood particles.2−4 The effects of particle size at the centimeter scale have mainly been confined to studies in packed beds,5 where both particle size and bed dimension are important, and studies in fluidized beds,6−9 where temperatures are high enough for gas-phase reactions to be important, and where internal temperatures of the wood particles could not be determined. Recently, macro-TGA experiments2,10,11 have addressed particle size effects with a focus on heterogeneous char formation.2,11 Particle size strongly influences the timing of pyrolysis.6 Effects on product yields are smaller6 and can be explained through two possible mechanisms. First, temperatures rise more slowly in the interior of larger particles. This lower effective heating rate allows more time for reactions to occur at low temperatures. Thus, for a given external heating environment, the effective decomposition temperature is lower for large particles than for small ones, leading to different product yields in the case of parallel reactions that have different temperature dependences.7 Second, the heterogeneous reactions of volatile products on solid surfaces should play a larger © XXXX American Chemical Society

role with larger particles. Under low and moderate heating conditions, volatile products experience pressure-driven flow through minimally modified wood vessel structures,11 traveling primarily along the grain of the wood. On average, the distance that volatile products must travel from the interior of the wood to the surface, through the small wood-vessel passageways, is larger for large particles. Thus, heterogeneous secondary tar reactions on solid surfaces have more opportunity to occur in large particles. During the pyrolysis of large (or thermally thick) biomass particles, heterogeneous tar decomposition into secondary char and gas occurs and affects product yields.2,3,12,13 While the fundamental understanding of key pyrolysis reactions of biomass constituents is improving,14,15 the complexity of biomass pyrolysis processes16,17 is not fully reflected in existing numerical models18,19 suitable for modeling pyrolysis of thick particles or reactors. Kinetic modeling of whole biomass pyrolysis typically treats the biomass as a single chemical species20 or as a handful of components1,21 that decompose independently upon heating, through a series of first-order homogeneous reactions. Although subsequent homogeneous gas-phase reactions of the pyrolysis products can be modeled,21 models do not exist for the catalytic processes that are primarily responsible for secondary reactions within the pyrolyzing biomass particle. For computational tractability, simplifications are also made in describing the transport processes in the biomass.18 In most cases, models are tested through comparisons to experimental mass loss data obtained under controlled-temperature conditions.18 If species Received: August 19, 2014 Revised: November 15, 2014

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dx.doi.org/10.1021/ef501869e | Energy Fuels XXXX, XXX, XXX−XXX

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data is used in mechanism development, it is generally in the form of time-integrated species yields. Additional high-quality time-resolved data is needed to gauge the capabilities and limitations of current whole-biomass pyrolysis models, especially in the thermally thick regime. The current study investigated the effect of particle size under conditions amenable to modeling. We focused on the low end of the pyrolysis temperature range, both to avoid gasphase reactions and because of the value of that temperature range in biochar production.22 In the thermally thick regime, this temperature range is important for decomposition even when the external temperature is considerably higher.9 In order to investigate the variability due to the wood type, three woody biomass materials (white pine sapwood, white pine heartwood, and poplar), were included in the study. Pyrolysis was performed for two centimeter-scale particle sizes, in reactor designed to produce reliable time-resolved species data. The same biomass materials were studied and in a kinetically controlled thermogravimetric apparatus using submillimeterscale particles. The experimental results were then compared to a numerical model, which includes the transport model of Park et al.20 and a recently revised version23 of the lumped kinetic model developed by Ranzi et al.21

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Table 1. Analyses of Wood Samples: Proximate Analysis (Dry Basis); Ultimate and Component Analyses (Dry, AshFree Basis)a sapwood

heartwood

poplar

proximate analysis (wt %)b volatile matter fixed carbon ash C H O cellulose hemicellulose lignin

85.00 ± 0.15 86.86 ± 0.04 14.79 ± 0.15 13.02 ± 0.06 0.20 ± 0.01 0.12 ± 0.02 ultimate analysis (wt %)c,d 51.57 ± 0.29 51.27 ± 0.76 6.14 ± 0.03 6.52 ± 0.37 42.28 ± 0.26 42.21 ± 0.46 component analysis (wt %)e 42.9 ± 0.2 39.5 ± 0.6 15.2 ± 1.6 10.7 ± 0.8 29.6 ± 0.4 27.3 ± 1.0

87.06 ± 0.013 12.70 ± 0.026 0.24 ± 0.011 47.72 ± 2.09 6.35 ± 0.03 45.93 ± 0.13 n/a n/a n/a

a

Standard deviations of three measurements are presented. bASTM E870-82 (2006). cMeasured with a Thermo Delta V isotope ratio mass spectrometer interfaced to a temperature conversion elemental analyzer, COIL: Cornell Isotope laboratory, normalized. dN content < 0.1% in all cases. eEstimated from measurements of neutral detergent fiber, acid detergent fiber, and lignin, obtained with an ANKOM A200 digestion unit with ANKOM Technology methods 5, 6, and 9, respectively. Analysis performed by Dairy One, Ithaca, NY.

EXPERIMENTAL PROCEDURE

Thermogravimetric Experiment. The TGA experiment used a low heating rate and small particle and sample size to achieve kinetically controlled pyrolysis, with a uniform sample temperature equal to the imposed furnace temperature.24−26 Critical particle size estimates for kinetic control are generally 100−1000 μm.25 Wood samples were oven-dried in air at 60 °C to a constant weight and then ground with a Mini-Mill (IKA Analytical Mill-MF 10) to pass through a 250 μm sieve. The particles were reduced to less than 250 μm to ensure that the thermal decomposition was within the kinetically controlled regime. Approximately 10 mg (9.74 ± 0.43 mg) of the wood powder was spread uniformly on the bottom of a platinum pan and analyzed by a TA Instruments Q500 thermogravimetric analyzer. Kinetic control was verified for one wood material by repeating the pyrolysis experiments with different sample masses (5 mg, 10 mg) and different particle sizes (250 μm, 500 μm); mass fraction profiles were independent of these choices.27 In addition to verifying kinetic control, these investigations indicated that secondary char formation, both inside and outside the particle, is negligible under the TGA conditions. The temperature of the furnace was programmed to rise from room temperature to 105 °C at a heating rate of 20 °C min−1 and held for 10 min to ensure all moisture was removed from the sample; high purity nitrogen flowed through the furnace at a rate of 60 mL min−1. The samples were then heated from 105 to 500 °C at a heating rate of 5 °C min−1. All samples were analyzed with an electro-balance which was purged with nitrogen at a flow rate of 40 L min−1. For each type of wood, three runs were performed with the same experimental conditions and excellent agreement was achieved in the mass loss curves (see Appendix A). Centimeter-Scale Study in the Pyrolysis Reactor. Preparation of Spheres. The wood samples used in this study were spheres of two different diameters: 2.54 and 3.81 cm. Poplar spheres were made from untreated poplar dowels and white pine spheres were made from sapwood and heartwood of a single sample of trunk wood harvested near Ithaca, NY. The properties of the wood samples are summarized in Table 1. Prior to pyrolysis, the wood spheres were dried in air at 105 °C until no further weight change was observed, then cooled to room temperature and stored in desiccators. Pyrolysis Experiments. Pyrolysis was performed at atmospheric pressure in a bench-scale tubular reactor designed to pyrolyse solids under well-defined conditions and to produce reliable time-resolved data on the production of light gas and volatile species. The reactor was previously described by Corbetta et al.23 and Bennadji et al.;28

minor modifications to the apparatus included the addition of turbulizing plates upstream of the wood sample and a change in the wood insertion procedure. The heating for wood pyrolysis was provided by 5.80 g/s of preheated nitrogen flowing turbulently through the reactor at an average velocity of 5−6 m/s. This high flow rate was selected for the nitrogen carrier gas in order to heat up the sample rapidly and to inhibit homogeneous secondary gas-phase reactions through rapid dilution of the products.28 Once the steadystate temperature conditions were reached, a wood sphere was quickly inserted into the center of the reaction section where it underwent pyrolysis for 30 min in the case of the smaller spheres and 40 min in the case of the larger spheres. The resulting char was then removed into a side arm of the reactor where it was cooled with a 0.39 g/s flow of room-temperature nitrogen which cooled the char to below 100 °C. Temperature Measurements within the Wood Spheres. The temperature histories within the wood spheres were measured at three radial locations: center (r = 0) and two r = R/2 locations, using sheathed 0.5 mm OD K-type thermocouples inserted through holes drilled using a 0.508 mm drill bit, positioned using a 3-D-printed guide. This size thermocouple was previously shown to be small enough to have a negligible effect on measured temperature profiles.28 Figure 1 shows the installation of thermocouples inside the wood spheres. The sphere is held in place via a small metal pin inserted the

Figure 1. Thermocouple locations and access holes within the wood sphere. The z axis is the direction of gas flow. B


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Table 2. Summary of Experimental Conditionsa

a

particle size

low temperature (369−380 °C)

medium temperature (413−424 °C)

high temperature (463−482 °C)

D = 2.54 cm D = 3.81 cm

white pine sapwood; poplarb white pine sapwood and heartwood; poplar

white pine sapwood and heartwood; poplar white pine sapwood; poplar

white pine sapwood; poplarb white pine sapwood; poplar

Detailed information is given in Appendix B. bThe high- and low-temperature poplar data is previously published.23

negative-z direction, at the furthest downstream point. The wood grain was aligned with the tracheids or vessels in the z direction. Gas Sampling and Analysis. The products released during the reactor pyrolysis process were sampled downstream of the wood particle through an open-ended quartz sampling tube. Sampled gases passed through ice−water traps for tar removal and then flowed continuously through the 5 cm-path length gas cell of a Fourier transform infrared (FTIR) analyzer (Nicolet 6700) for time-resolved profiles of gas and volatile species. The average residence time of gases in the sampling and analysis equipment was only 3 s, but the FTIR data was averaged over approximately 90 s for better signal-to-noise ratio. Additional measurements were taken in series for hydrogen with online gas chromatography (Thermo Trace GC)/thermal conductivity detector (GC/TCD) equipped with a 10 port sampling valve with 1 cm3 sample loop, a precolumn (TG-Bond S 0.53 mm × 20 μm × 15m), and an analytical column (TG-Bond Molsieve 5A 0.53 mm × 15m). The GC method was designed to obtain a time-resolved hydrogen history while backflushing the heavy components to waste: After the sample loop was loaded, the valve was switched to inject the sample into the precolumn. As soon as the hydrogen entered the analytical column, the valve was switched back to the loading position, while the precolumn was backflushed to vent during the analysis, reducing the total analysis time. This cycle was repeated every 84 s. In addition, a nondispersive infrared (NDIR) analyzer (Horiba PG250A) was installed in parallel to obtain carbon monoxide histories with 1 s time resolution. The operating conditions are summarized in Table 2 and given in detail in Appendix B. For each condition, the pyrolysis was repeated a minimum of two times. A complete set of pine heartwood data could not be obtained because samples split during pyrolysis under high-temperature conditions. For all conditions, the Reynolds number based on the reactor diameter was between 4000 and 4600. The measured mole fractions of species, which reflect a high degree of dilution are converted into mass production rates divided by the initial mass of dry sample as described previously by Bennadji et al.28

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fractions of all species scaled up to account for 100% of the initial mass. In the initial composition, we also used recommended proportions29 of the different lignin species designated the kinetic mechanism. The resulting compositions are shown in Table 3. Table 3. Initial Composition (wt %) Used in the Numerical Simulation compound

white pine sapwood

white pine heartwood

cellulose hemicellulose LIGHa LIGCa LIGOa ash

48.7 17.5 3.7 14.9 14.9 0.2

50.8 13.9 3.9 15.6 15.6 0.12

a LIGH, LIGC, and LIGO represent different varieties of lignin, as described elsewhere.21

The macroparticle pyrolysis modeling requires a model for transport processes as well as kinetics, which were adopted from the one-dimensional isotropic heat transfer and fluid flow model of Park et al.20 as implemented in COMSOL Multiphysics Version 4.3.27,30 Physical properties are listed by Corbetta et al.;23 identical values of all physical parameters are used for all woods, with the exception of density, which is determined experimentally for each individual wood sphere of both chemical and particle properties. The model of singleparticle pyrolysis was implemented in one-dimensional, spherical coordinates. The model simulates processes in the interior of the wood particle. The thermal boundary condition consists of an imposed external convective heat flux determined experimentally via measurements with aluminum spheres under conditions similar to those for the wood-sphere experiments. In addition, radiative heat transfer is included based on the emissivities of the Park et al. model.20 Initial heat fluxes range from 24 to 43 W/m2. A previous publication23 compares this model to TGA and centimeter-scale pyrolysis data from three independent laboratories, also presenting the sensitivity of results to several key model parameters. The choice of model parameters was a compromise that yielded reasonably good results for a range of conditions and biomass samples. In the current paper, we simply evaluate the performance of this model with a new experimental data set encompassing larger particles. While tuning of model parameters would yield better agreement with the current data set, it would undoubtedly reduce the quality of agreement with the previous data sets.

NUMERICAL MODEL

A number of studies of biomass pyrolysis have proposed more or less complex reaction schemes to account for slow and fast pyrolysis and primary and secondary gas phase pathways.18 Here we adopted the kinetic scheme of Ranzi et al.,21 as revised recently by Corbetta et al.23 This scheme treats biomass as five components: cellulose, hemicellulose, and three species representing lignin, each with different proportions of C, H, and O. These components decompose independently to produce gaseous species and representative, lumped, tar species. Reactions include transformation of initial biomass components and intermediate solids into intermediate solids, tars, gases, and adsorbed gases. No secondary tar reactions are included in the mechanism, but pseudoreactions describing the desorption of adsorbed gases are included. In order to use the kinetic model, initial masses of the five components and ash must be selected. In the case of poplar, we used the composition listed previously,23 which is derived from the measured elemental composition. In the case of the pine samples, estimated chemical composition was measured (Table 1), but the measured constituents sum to less than 100%. We selected an initial composition with the same proportions of cellulose, hemicellulose, and lignin as in Table 1, with mass

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RESULTS AND DISCUSSION This section presents thermogravimetric data, then temperature and species histories for the wood spheres. Next, quantities derived from the raw data are presented: (1) yields of char, gas, and individual gas and tar species; (2) devolatilization times. As relevant, we present trends with external temperature, wood C


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in each DTG curve is attributed to the decomposition of cellulose. The shoulder on the low-temperature side represents the decomposition of the hemicellulose, while lignin decomposes slowly over a wide temperature range.4,31−33 The char yields at 500 °C were 13.7%, 17.2%, and 16% for the poplar, the pine sapwood, and the pine heartwood, respectively. Two features of the curves differed among the different wood species. First, the prominence of the lower-temperature shoulder was greater for poplar than for the two pine samples, which were very similar to each other. This difference is consistent with the poplar’s higher hemicellulose content relative to pine.34 A consequence of this difference is discussed below. Second, at 200 °C, the pine heartwood showed a peak that was absent in the other two wood samples. This peak is attributed to the release or decomposition of extractives,26,35,36 which are known to be higher in heartwood than in sapwood;37,38 white pine extractives were found to be almost twice as high in heartwood as in sapwood by Conner et al.39 In the current study, the fraction of mass removed during the neutral density fiber digestion process was 12.0 ± 1.8% for pine sapwood and 22.4 ± 1.0% for pine heartwood samples. Because of harsh digestion conditions, these quantities are considerably higher than the mass losses that would be obtained with standard extraction techniques. However, the relative magnitudes indicate the presence of more extractives in the heartwood pine samples of the current study. The kinetic model was tested by comparing its predictions with the experimental thermogravimetric data. Because the temperature was specified, results were influenced only by the kinetic parameters, not thermochemistry or transport properties. The major peak size and shape was similar to that of the experiment. Due to the limited number of components in the model, the summation of distinct peaks was more evident in the simulation than in the experiment. The maximum decomposition rate occurred approximately 22 degrees lower in the simulation than in the experiment. We have confidence in the temperature of maximum biomass conversion, because that temperature is very close to a Curie point calibration performed on the TGA under heating and gas flow conditions identical to the current experiments. For that reason, we can only attribute the differences to variability among biomass components. The temperature discrepancy shown in part c was discussed in a previous publication.23 It was found that the model could be brought into better agreement with the experiment by increasing the activation energies of the reactions involving cellulose by 1 kcal/mol. However, adjusting the activation energies led to poor predictions of other data sets, and thus, the changes in activation energy were not adopted in the final version of the model.23 In the temperature range 400 to 450 °C, the char mass fractions were overestimated by between 7% and 40% for the three wood materials. Qualitative differences among wood types were only partially represented by the model: A slightly more prominent hemicellulose shoulder was present in the poplar simulation, as in the experiment. The simulation did not reproduce the extractives peak in the heartwood because extractives are not among the biomass components available in the model. Model predictions of the char mass fractions showed almost no variation with wood type, while experimental values were 10 to 20% higher for pine than for poplar. Some model predictions (both mass loss rate and char yields) could be improved by including an additional biomass component corresponding to the more volatile extractives kinetics proposed by Grønli et al.26

type, and particle size. For each type of data, we discuss the extent of agreement between the experiment and simulation. Thermogravimetric Results. Figure 2 shows the average of the three thermogravimetric replicates for white pine

Figure 2. Comparison between experimental and simulated TG analysis of (a) pine sapwood, (b) pine heartwood, and (c) poplar wood. Experiments are solid black lines, and model predictions are dashed blue lines. Part c is reprinted from ref 23. Copyright 2014 American Chemical Society.

sapwood and heartwood; also included are the poplar results reported previously.23 The plots display the time derivative of TG curves (DTG), with the same general features for all three materials. The pyrolysis process can be divided into three stages, namely the drying stage (400 °C). The majority of the biomass decomposed between 220 and 380 °C. The temperatures corresponding to the maximum pyrolysis rate of poplar, pine sapwood, and pine heartwood were similar, at 366, 369, and 363 °C, respectively. The main pyrolysis peak D


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Figure 3. Temperature histories at two positions in 2.54 and 3.81 cm diameter sapwood spheres pyrolyzed at three temperatures, comparison between experimental and simulation results. Experiments are solid black lines, and model predictions are dashed red lines. Error bars represent standard deviations based on repeated experimental measurements.

parameters of the model was analyzed previously by Bennadji et al.28 and by Corbetta et al.23 Pyrolysis Species Profiles. Figure 4 shows the instantaneous mass production rates of individual species, normalized by the initial mass of the wood sample. Each case shows data combined from at least two runs; see Table B1 in Appendix B. Where available, results obtained with two different analytical devices (NDIR and FTIR) showed excellent agreement. Different classes of species were identified, including hydrocarbons, aldehydes, alcohols, and carboxylic acids, in addition to CO, CO2, and H2. With the exception of H2, which was below detectability at lower temperature conditions, the same main species were observed for all conditions, though the relative quantities varied somewhat, as discussed below. The overall timing and integrated yields are discussed in subsequent sections, while the focus of the current discussion is on the timing and shape of individual species histories. The relative timing of the different species varied with particle size and temperature, but the peak of HCHO was consistently among the earliest, while the peak of CH4 was consistently last. The formation of all detected species, except for methane, occurred with peaks well before the temperature maximum, within the temperature plateau regions identified in Figure 3. The methane peak, on the other hand, appears at times comparable to the measured temperature maximum, suggesting the possibility of a high-temperature production mechanism. Also shown in Figure 4 are the computed release rates of gaseous and volatiles species. Experimentally, each species profile had a single peak. In the case of carbon monoxide and methanol, the model predicted small secondary peaks in addition to the main peak; these secondary peaks are caused by the pseudoreactions representing high-temperature desorption of adsorbed carbon monoxide and methanol. Although integrated yields are predicted with some success (next section), the model is not successful in predicting the

This change to the mechanism did not seem justified given the limited data set of the current study but is a promising avenue for improving the mechanism in the future. Pyrolysis Reactor Results. Temperature Profiles. Under the conditions of the sphere experiments, the Biot number ranged from 1.1 to 1.4. Thus, the particles can be considered thermally thick,40 implying a large thermal gradient inside. Figure 3 reports the temperature histories of the particle at r = 0 and r = R/2, for pine sapwood. Readings from the two thermocouple locations indicated temperature gradients inside the wood particles. In addition, all profiles showed two thermal events: a decrease in the slope of the temperature profile followed by a temperature maximum, overshooting the external (reactor) temperature. These phenomena have been previously observed for macroparticle pyrolysis at low temperature and interpreted in terms of specific heat effects41 or successive endothermic and exothermic reactions.5,20,28 As expected, all thermal events appeared earlier for the smaller sphere than for the larger sphere, and exothermic peaks occurred later and lasted longer at lower temperatures. The corresponding simulation results in Figure 3 agreed reasonably well with experiment for all conditions. The predicted times of the thermal events were somewhat shorter than observed experimentally. The maximum temperature values (ranging from 383 to 485 °C at R/2 and from 386 to 508 °C at the center) were well-predicted, with the discrepancies vis-à-vis experimental data being nearly within the experimental repeatability. In both the experiment and the simulation, the timing of temperature maxima was essentially independent of measurement location (center vs R/2) in a sphere of a given diameter. Similar results and trends were obtained for pine heartwood and poplar wood particles, as reported in Appendix C. Note that the effect of different transport and thermochemical E


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It is clear that this discrepancy is due to the kinetic model, not to the heat transfer model, as the excess production of these gases occurs at times when temperature predictions agree well with measured temperatures, or even lag slightly behind measured temperatures (Figure 3). Adjustments of the product stoichiometries of the lumped reactions, and perhaps also of rate parameters, are needed in order to bring the predictions into better agreement with experimental results. The need for mechanism refinement is not surprising, as there has been little testing of biomass pyrolysis mechanisms against time-resolved species data. Appendix D shows experimental and simulation data for the pine heartwood and poplar samples, which were similar to the data presented in Figure 4. Yields of Individual Species. Product yields were obtained by integrating the data sets presented in Figure 4, with the resulting data available in Table E1 of Appendix E. The relative magnitude of different compounds’ yields and the overall agreement between experiment and simulation can be seen in Figure 5. This figure shows yields for all measured

Figure 5. Comparison of the measured and predicted species yields. Linear fit: y = 0.9988x. Adjusted R2: 0.869. Root mean squared error: 0.782%.

species using data from each of the runs with both sphere sizes. The predicted yield (y axis) is plotted against the experimentally determined yield (x axis). The ranking of magnitude of all species was predicted well, and good agreement was observed for the major gas species, namely carbon monoxide and carbon dioxide. A linear fit to the entire set of species yield data was virtually identical (slope = 0.9988) to the diagonal line shown in Figure 5. Acetic acid, formic acid, and methane showed considerable variation in experimental yield over the range of conditions tested here. That variation was not reflected in the simulation results, which were nearly constant. Methanol yields varied for both simulation and experiment, but yields were overestimated in most cases. Trends in measured species yields can be seen in Figures 6, 7, and 8. Figure 6 examines the trends in species yields with external temperature. In this figure, we present four pairs of experimental runs. For a given sphere size and wood type, we plot the yield at the maximum external temperature (approximately 470 °C) versus the yield at the minimum external temperature (approximately 375 °C). Compounds with data appearing above the diagonal line are those whose yield increases with temperature. Conversions from wood to volatile products are generally higher at higher temperatures, and many species (CO, CO2, CH4, etc.) followed the expected

Figure 4. Measured and predicted mass flow rates of species produced from 2.54 and 3.58 cm diameters pine sapwood at (a) low, (b) medium, and (c) high gas temperature. Experiments are symbols, and model predictions are lines.

timing of generation of individual species. The release patterns of formaldehyde and formic acid were reasonably well predicted, but most species were predicted to appear substantially too early. Notably, major species CO2, and to a lesser extent, CH3COOH, are predicted to rise steeply within the first 100 s of pyrolysis, considerably earlier than measured. F


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Figure 6. Comparison of yields at the highest external temperature to yields at the lowest external temperature; particle size and wood type are matched for each data point. Error bars represent standard deviations of replicates. Figure 8. Comparison of yields for 3.18 cm spheres to yields for 2.54 cm spheres. Wood type and temperature are matched for each data point. Error bars represent standard deviations of replicates.

Figure 7 shows a similar presentation of the effect of wood type. Points along the diagonal line are those with the same yield from poplar as from pine sapwood. Most species showed small or inconsistent differences between the two types of wood studied. But there were consistent differences for some species: formaldehyde and formic acid yields were consistently lower (on average 26% and 34% lower, respectively) for poplar than for pine sapwood, and acetic acid yields were consistently higher (by a factor of 2, on average) for poplar than for pine sapwood. Hydrogen yields were also consistently higher for poplar, but the data set was very limited. Acetic acid is mainly formed from the hemicellulose decomposition,42 thus its higher production from the poplar compared to the pine sapwood can be attributed to the higher content of acetylated hemicellulose polymers in the poplar wood, as seen in the shoulder of its TGA curve (Figure 2c). In Figure 8, trends with sphere size are presented. Each species yield for a 3.18 cm particle is plotted versus the yield of the same species from the corresponding 2.54 cm particle, made of the same wood material, and pyrolyzed at approximately the same temperature. Only poplar and pine sapwood results were included, as pine heartwood data set did not contain any matching conditions for large and small spheres. Points along the diagonal line would indicate identical yields for large and small spheres. For the majority of species, there was no consistent difference between yields for large and small spheres. There were a few species with consistent differences in yield: yields of methane, a minor species, were on average 70% higher for large spheres than for the corresponding small spheres. On the other hand, formaldehyde and formic acid yields were, respectively, 17% and 22% lower for large spheres than for the corresponding small spheres. With the exception of acetic acid, measured yields were similar in

Figure 7. Comparison of yields for poplar to yields for pine sapwood; particle size and external temperature are matched for each data point. Error bars represent standard deviations of replicates.

trend of yield increasing with temperature. Interestingly, there were a few compounds with the opposite trend, i.e. with all yields appearing below the diagonal line. These species (formic and acetic acids) either are produced less abundantly at higher temperatures, or are readily destroyed by intraparticle secondary reactions that are active at higher temperatures. G


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Figure 9. Char (a) and gas (b) yield vs external temperature: (triangles) poplar; (diamonds) pine sapwood; (squares) pine heartwood. Char was not recovered in pine heartwood experiments: (solid symbols) experiments with small spheres; (striped symbols) experiments with large spheres. Corresponding red symbols represent simulations. Error bars represent standard deviation of repeated measurements and are wholly inside symbols in some cases. Characteristic times for pyrolysis.

transfer resistance is greater, increasing opportunities for heterogeneous reactions catalyzed by the primary char,43,44 and have been observed in comparisons of centimeter-scale biomass particles vs powders.3,45 Overall, the results show that the particle size parameter exerted a less important influence than temperature under the conditions studied. The model, which does not include secondary tar reactions, showed essentially no effect of particle size but predicted the qualitative trend with temperature. One key parameter for designing thermal treatment devices is the residence time required for complete pyrolysis. A devolatilization time can be defined as the time at which a given fraction of the gases CO, CO2, CH4, and H2 have been released. To find this quantity, a Weibull curve was fit to experimental gas release data due to the low time resolution of the experimental species data. In Figure 10, the 90% devolatiliza-

magnitude to those obtained in other studies of particle size that investigate the same temperature range.3,5 However, the trends of individual species’ yield with particle size agreed only partially with these studies, which differ from the current study in heating rate or in the use of a fixed bed configuration. Comparison of results between Figures 6 and 8 provides evidence that the size-dependent effective temperature of decomposition does not have a large impact. If the effective pyrolysis temperature7 (determined by the particle-size-related heating rate) had a dominant effect on product yields, then the trends with increasing particle size would mirror the trends with decreasing temperature. While formaldehyde showed this anticipated behavior, formic acid and methane showed the opposite trends with particle size than would be predicted on the basis of effective pyrolysis temperature. Other compounds showed mixed or ambiguous results. Evidence for the importance of heterogeneous tar decomposition reactions in the particle size data is ambiguous. If the main effect of the larger particle size is to promote heterogeneous tar decomposition reactions via additional contact with char surfaces, then the larger spheres would have lower yields of primary tars and higher yields of the products of tar decomposition reactions. Unless quantitative information is available for all tars, however, ambiguity arises because tar species can be destroyed by some char-catalyzed reactions and created from more complex tars by other charcatalyzed reactions. Under the conditions of the present study, formaldehyde and formic acid show behavior consistent with being destroyed in heterogeneous reactions. However, another tar species, acetic acid, shows mixed results. Methane trends with particle size are consistent with its being a product in heterogeneous tar reactions. Yields of Char and Gas. The yields of gas were obtained by summing the yields of the individual gaseous species (CO, CO2, CH4, and H2), and char yields were determined by weighing the solid residue. Figure 9 depicts the measured and the predicted yields of char and gas as functions of external temperature. As expected, gas yields increased, while char yields decreased with increasing pyrolysis temperature. This trend was also seen in the model results, which agreed well with gas yields while underestimating char yields, especially at the lowest temperature. The effects of changing the particle size were consistent but small: char yields were on average 7% higher for the large spheres than for the small spheres. Higher yields are expected for larger particles because the intraparticle mass

Figure 10. Experimental 90% devolatilization times (symbols) compared to predictions of the empirical correlation (lines) of Gaston et al. for complete devolatilization.6 Closed symbols and dashed line represent 2.54 cm spheres; open symbols and solid line represent 3.81 cm spheres: (triangles) pine sapwood; (squares) pine heartwood; (diamonds) poplar.

tion time is plotted against the external temperature. Also included in the figure is the empirical correlation presented by Gaston et al.,6 which is based on experiments with dry oak spheres in a fluidized bed with temperatures between 500 and 900 °C and diameters between 0.6 and 3.3 cm. This correlation, a power law expression with diameter raised to the power 1.414, successfully predicted the dependence on both size and H


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Figure 11. Simulation predictions of 50% (a) and 90% (b) devolatilization times versus the corresponding experimental values. Closed symbols and dashed line represent 2.54 cm spheres; open symbols and solid line represent 3.81 cm spheres: (triangles) pine sapwood; (squares) pine heartwood; (diamonds) poplar. Linear fits (a) y = 0.7033x, adjusted R2 0.900, root mean squared error 31.4 s; (b) y = 0.9388x, adjusted R2 0.821, root mean squared error 81.6 s.

temperature, when extrapolated to the current experimental conditions. Appropriately, experimental data points based on 90% devolatilization fall slightly below the correlation, which is essentially based on 100% devolatilization. In contrast, Daouk et al.10 found virtually no effect of particle size on the timing of mass loss in a macro-TGA study of pyrolysis of cylinders with identical length but different diameters. This difference can be explained because Daouk’s study did not change the particle dimension along the grain, through which the transport of heat and species is dominant. The numerical simulation predictions are plotted versus experimental devolatilization times in Figure 11. The two parts of the figure show the comparisons for 50% and 90% devolatilization, based on gases. The 50% devolatilization times (part a) were predicted to be approximately 70% of their experimental values, consistent with the timing mismatch between simulated and experimental peaks seen above. Agreement was much better for the later part of the devolatilization process, with predictions approximately equal to 94% of the experimental values for 90% devolatilization times (part b). In addition, a second, thermal, time scale can be obtained independently from experimental data and the simulation. We defined the thermal time scale for pyrolysis as the time between the start of pyrolysis and the time at which the temperature at the center of the sphere reaches its maximum value. (Note that the time of the temperature maximum was virtually the same at the R/2 location.) This time scale is presented in Figure 12. Once again, the simulation predictions for both particle sizes and all three wood materials showed satisfactory agreement with the experimental data. Predicted thermal time was approximately 96% of measured thermal time. Neither time scale showed evidence of a systematic effect of wood type. Notably, the two time scales were similar: the 90% devolatilization time was approximately 87% of the thermal time, indicating that the devolatilization was largely complete by the time the temperature maximum occurred at the center.5,22,46,47

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Figure 12. Simulation predictions of thermal times versus the corresponding experimental values. Closed symbols and dashed line represent 2.54 cm spheres; open symbols and solid line represent 3.81 cm spheres: (triangles) pine sapwood; (squares) pine heartwood; (diamonds) poplar. Linear fit y = 0.9621x; adjusted R2 0.845; root mean squared error 110 s.

ditions were designed to minimize reactions outside of the particle, yielding time-resolved species profiles that reflect reactions within the particle. Comparisons were made between experimental results and the output of a general, lumped computational model that predicts the evolution of individual gases and representative tar species and can be adapted to any wood type and experimental configuration.23 The model includes transport and kinetics of solid to vapor reactions but does not contain secondary tar reactions. Experiments showed behavior typical of thermally thick biomass pyrolysis, with significant intraparticle temperature gradients and an evident impact of reaction thermochemistry on the temperature fields. The model was successful in predicting the qualitative features and approximate magnitudes of quantities such as temperature overshoot, TGA mass loss rate, product yields, and timing of gas release for the range of conditions studied. The main impact of particle size was on the timing of the heating and devolatilization processes. The time required for devolatilization, as well as the time required for heating to the peak temperature, were successfully predicted by the numerical model and a simple empirical correlation (for devolatilization time only). Devolatilization time scales increased with increasing particle size, showed little dependence on wood type, and decreased moderately with increasing temperature. Although the time required for the overall process was

SUMMARY AND CONCLUSIONS

This study investigates the pyrolysis of hardwood and softwood under several experimental conditions: thermogravimetric analysis in the kinetically controlled regime, and pyrolysis of large and small spheres under thermally thick conditions at three temperatures. The thermally thick experimental conI


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in performing the proximate analysis of samples and of Dana Paul in preparing the biomass spheres.

predicted well, the model consistently underpredicted the time required for the early stages of decomposition. Particle size had a subtle effect on the yields of char and individual gaseous species. Experimental yield data can be interpreted as reflecting the intraparticle reactions of some product species (formaldehyde and formic acid) to form secondary char, but the limited number of species monitored makes it impossible to obtain a complete or definitive picture of intraparticle tar reactions. The model, which did not include any secondary tar reactions, did not reproduce the relatively small differences in species yield with particle size. The wood type showed consistent effects on the yields of certain species, notably acetic acid, formic acid, and formaldehyde, on the same order of magnitude as the effect of particle size. These differences were not well reproduced by the model, nor were distinctive features of the different woods in the TGA mass loss rate curve. Given the limited number of components and the lumped treatment of tar species, the computational model was reasonably successful at predicting the key features of the pyrolysis phenomena. Several areas for future improvement of the mechanism have been identified: (1) representation of extractives among feedstock components, (2) inclusion of intraparticle tar reactions, and (3) reevaluation of the stoichiometries and kinetic parameters that are responsible for the early evolution of major gas species.

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ASSOCIATED CONTENT

S Supporting Information *

Appendix A: Thermogravimetric analysis of weight loss curves of sapwood, heartwood white pine, and poplar. Appendix B: Detailed experimental pyrolysis conditions. Appendix C: Predicted and measured temperature profiles at the half radius and radius of the pine heartwood and poplar spheres. Appendix D: Predicted and measured mass flow rates species produced from 2.54 and 3.58 cm diameters pine heartwood and poplar spheres. Appendix E: Measured species and lumped products yields (gas, tar, and char). This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*Phone: 607-255-8309. E-mail: emfi[email protected]. Present Addresses §

H.B.: Louisiana State University, Department of Chemistry, 338-G Choppin Hall, Baton Rouge, LA 70803, USA. ∥ K.S.: Raytheon, 1151 E. Hermans Rd., Building 805/H0330, Tucson, AZ 85756, USA. ⊥ M.J.S.: USDA-ARS, Eastern Regional Research Center, Sustainable Biofuels and Coproducts Research Division, Wyndmoor, PA 19038, USA. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors gratefully acknowledge the financial support of Fondation des Fondateurs. The authors also gratefully acknowledge the contribution of Sebastien Lachance-Barrett J


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Effect of Particle Size on Low-Temperature Pyrolysis of Woody

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