Prediction of Sawdust Pyrolysis Yields from a Flat ... - ACS Publications

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Prediction of Sawdust Pyrolysis Yields from a Flat-Flame Burner Using the CPD Model Aaron D. Lewis and Thomas H. Fletcher* Department of Chemical Engineering, Brigham Young University, Provo, Utah 84602, United States ABSTRACT: High heating rate pyrolysis experiments were performed on a softwood sawdust in a flat-flame burner reactor at temperatures from 1163 to 1433 K with particle residence times ranging from 23 to 102 ms at atmospheric pressure. Volatile yields of the 45−75 μm sawdust were measured and are believed to be similar to those that would occur in an industrial entrained-flow combustor or gasifier. A refractory tar yield near 1.5 wt % (dry, ash-free) was measured. A high percentage of the fully pyrolyzed sawdust char was spherical, having lost the original sawdust structure. It is suggested that the morphology of sawdust char may continue to change after complete mass release from pyrolysis. Sawdust pyrolysis was modeled using the chemical percolation devolatilization (CPD) model assuming that biomass pyrolysis occurs as a weighted average of its individual components (cellulose, hemicellulose, and lignin). Thermal cracking of tar into light gas was included using a first-order kinetic model from the literature. The devolatilization yields of three kinds of sawdust from three different reactors (flat-flame burner, drop-tube, and thermogravimetric analyzer) were accurately predicted.

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application to any planned entrained flow combustors or gasifiers, the data here provide a good test for generalized pyrolysis models that attempt to treat reaction behavior over a wide range of heating rates and temperatures. The measured pyrolysis data obtained in this study were modeled using the chemical percolation devolatilization (CPD) model combined with a first-order kinetic model that treats thermal cracking of tar into light gas. The CPD model assumes that biomass devolatilization occurs as a sum of its main components, namely, cellulose, hemicellulose, and lignin. This assumption in the modeling of biomass pyrolysis is consistent with other studies.6−10 The CPD model provides a very generalized way to model biomass pyrolysis and is shown to accurately predict the pyrolysis of three kinds of sawdust from three different reactors (flat-flame burner, drop-tube, and thermogravimetric analyzer).

INTRODUCTION Biomass is a sustainable fuel that allows energy generation from biological material, such as sawdust, switchgrass, corn stalks, straw, etc. A common way that biomass is converted into useful products is through thermal conversion. Pyrolysis, sometimes used synonymously with devolatilizaton, precedes gasification or combustion and is the thermal decomposition of the solid fuel into permanent gases, tar (condensable vapors), and char (solid residue).1 Biomass pyrolysis can also be a stand-alone process.2 Successfully modeling the gasification or combustion of biomass begins with an accurate pyrolysis model, especially since volatile yields of biomass pyrolysis are generally quite high. Numerous kinetic models of biomass pyrolysis have been proposed. Prakash and Karunanithi3 wrote a review concerning the many biomass pyrolysis models available in the literature. Di Blasi4 also authored an excellent review regarding the modeling of wood pyrolysis. Although pyrolysis rate constants for biomass are available in the literature, there is still a lack of pyrolysis models that are general and not specific to a certain type of biomass from a particular reactor. This work focuses on measuring the pyrolysis yields of a single softwood sawdust in a flat-flame burner reactor. The measured pyrolysis yields of sawdust are believed to be similar to those that would occur in an industrial entrained-flow combustor or gasifier. Relative yields of gas, tar, and char depend heavily on heating rate and final temperature,5 and the flat-flame burner reactor environment is very similar to the environment of industrial entrained-flow reactors. Sawdust pyrolysis yields are summarized, and attention is also given to the changes in particle morphology. Many current industrial biomass combustion systems utilize fluidized-bed technology, meaning larger particles and slower heating rates than studied here. However, these data represent a limiting case where temperature and concentration gradients inside particles are negligible, allowing measurement of true kinetics rather than apparent kinetics. In addition to direct © 2013 American Chemical Society

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EXPERIMENTAL PROCEDURE

Sawdust Sample and Preparation. Pyrolysis experiments were conducted on a single softwood sawdust. The sawdust was ground using an electric wheat grinder (Blendtec Kitchen Mill) and sieved to collect the 45−75 μm size range. Small sawdust particles were used in order to assume no interparticle temperature gradients for modeling purposes and to ensure a high initial heating rate of the particles. Figure 1 shows an image of the ground virgin sawdust from a scanning electron microscope (SEM). The tracheids and bordered pits on the raw sawdust are characteristic of softwood trees.11 As can be seen in Figure 1, a few long, skinny particles were able to pass through the sieve trays since their minimum diameter was less than 75 μm, but the number of these particles was thought to be small. The ultimate and proximate analyses of the softwood sawdust were performed at Brigham Young University and are included in Table 1. Apparatus and Operation. The atmospheric flat-flame burner (FFB) used by Ma12,13 and Zhang14,15 was utilized in this study with Received: November 19, 2012 Revised: January 16, 2013 Published: January 17, 2013 942

dx.doi.org/10.1021/ef3018783 | Energy Fuels 2013, 27, 942−953

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Figure 1. SEM image of raw sawdust collected from the 45−75 μm sieve tray. modifications. A schematic of the FFB is shown in Figure 2. Flat-flame burners are useful since they well approximate the reaction conditions in industrial entrained-flow reactors. For example, the FFB reactor allows maximum initial particle heating rates around 105 K/s, which nears particle heating rates of about 106 K/s, which are common in commercial, entrained-flow combustors and gasifiers.16 The flat-flame burner uses numerous small-diameter tubes to create hundreds of diffusion flamelets by feeding gaseous fuel through the tubes while introducing oxidizer in the vacant spaces between the tubes. The many small flamelets create a flat flame a few millimeters above the burner. The gaseous fuel supplied to the FFB was mainly CO, with a trace amount of H2 for flame stability. The fuel-rich pyrolysis experiments took place in a postflame environment composed primarily of N2 (∼60 mol %), CO (∼23 mol %), and CO2 (∼15 mol %), as predicted by thermodynamic equilibrium. The equivalence ratio for the gas conditions used in the pyrolysis experiments ranged from 1.3 to 1.6. Dried sawdust particles were entrained in nitrogen and carried to the middle of the burner surface through a small metal tube (0.053ˈˈ ID) at a feed rate near 0.5 g/h to ensure single-particle behavior and to prevent clogging. The sawdust particles then pyrolyzed while traveling upward in a laminar flow in a quartz tower before the particles were collected in a nitrogen-quenched, water-cooled collection probe. The collection probe was lined with a sintered stainless steel tube (5 μm pore size), through which quench nitrogen passed. About 30% of the quench nitrogen was released at the entrance of the probe through 10 equally spaced jets. The balance of quench nitrogen passed through the walls of the sintered tube, which prevented tar from collecting on the surface of the sintered tube. A virtual impactor and cyclone in the collection system separated the char aerodynamically while the tar and soot were collected on water-cooled micropore filters. There was no evidence of tar or soot deposition in the collection system before the filters. The low flow rate of carrier nitrogen (0.0367 SLPM) in the feed tube was considered negligible when compared with the 26−30 SLPM flow rates of other gases to the burner. The standard volumetric flow rate of quench N2 was about 2.3 times that of the hot gas. Particle residence time was controlled in the FFB by adjusting the height of the collection probe above the burner and was calculated using particle velocity measurements from a high-speed Kodak EktaPro camera. Gas temperature was controlled in the FFB by adjusting the flow rates of CO, H2, O2, and N2 to the burner. Centerline gas temperature measurements were made in the FFB using a B-type thermocouple bead coated with silica to prevent catalytic heating of the bead. The gas

Figure 2. Schematic of the FFB system. temperature profiles are shown in Figure 3 and are identified by the peak gas temperature measured in each profile (1163, 1320, 1433,

Figure 3. Centerline gas temperature profiles for all conditions. 1751 K). The gas temperature measurements were corrected for radiation losses from the thermocouple bead.17 Nonisothermal behavior is due to heat loss through the quartz tower. The temperature profile of the 1751 K condition of the FFB reactor is included in Figure 3 for completeness even though it was only used for a limited number of experimental runs. The equivalence ratio of the 1751 K gas condition was 1.16 and had a postflame environment of N2 (67 mol %), CO2 (23 mol %), and CO (9 mol %).

Table 1. Ultimate and Proximate Analyses of the Softwood Sawdust

a

moisturea (wt %)

ash (wt %, dry)

volatiles (wt %, dafb)

C (wt %, daf)

H (wt %, daf)

N (wt %, daf)

Oc (wt %, daf)

S (wt %, daf)

5.92

0.6

87.02

50.63

6.06

0.07

43.22

0.02

As-received basis. bdaf = dry and ash-free basis. cCalculated by difference. 943


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Elemental composition of sawdust chars and raw sawdust were determined using a Leco TruSpec Micro instrument. The SEM pictures in this work were taken using a FEI XL30 ESEM instrument with a FEG emitter.

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weights of sawdust fed and collected char). Each mass balance data point in Figure 4 is the average of four replicate experiments performed on the FFB and has an average 95% confidence interval of ±2.4 wt % daf. The ash-tracer data points often had no replicates due to the very time-consuming task of collecting enough sawdust char to perform an accurate ash test due to the low ash content in the sawdust. The difference between mass release calculated by mass balance and ash tracer in Figure 4 was usually within 2−3 wt %, although the two calculated mass release values at the 32 ms, 1163 K condition differed by 8.2 wt %. More confidence is given to the mass release numbers calculated by mass balance since they represent the average of four times more data than the ash-tracer values. In addition, mass release calculated by ash tracer is typically only trusted more than the corresponding mass-balance values when the ash-tracer values are lower, since this would imply that the mass balance of the experiment had been disturbed by low char collection efficiency. All the experiments except for the 32 ms, 1163 K condition yielded fully pyrolyzed char yields, as evidenced by the asymptotic mass release values near 95 wt % daf in Figure 4, as calculated using a mass balance. The 32 ms, 1163 K condition on the FFB represented the lowest temperature, shortest residence time condition of the FFB. Adjusting gas flow rates to decrease the gas temperature any lower than 1163 K resulted in an unstable flame. The mass release of fully pyrolyzed sawdust from the FFB reactor exceeded the ASTM volatiles value (see Table 1) by 7.4 wt % daf. Borrego et al.18 also measured a mass release significantly greater than the ASTM value in the pyrolysis of wood chips in a drop-tube furnace. The higher mass release in the FFB experiments is attributed to the extreme difference in particle heating rates that exist between a flatflame burner and a muffle furnace that is used in ASTM testing. The initial particle heating rate influences the effect of cross-linking reactions in a devolatilizing particle.18 Sufficiently high particle heating rates result in a higher volatiles yield since cross-linking reactions kinetically compete with the release of material from a particle during pyrolysis. The elemental compositions of sawdust chars collected from the FFB experiments are shown in Table 2. Each char composition reported in the table typically represents the average of three replicates, and the average standard deviation of % C, % H, and % N for each char was 1.21, 0.09, and 0.01 wt % daf, respectively. The char composition allows the elemental composition of the volatile matter to be calculated using the ultimate analysis of the virgin sawdust and a mass balance. This is helpful since individual gas species were not measured in these FFB experiments. If the sawdust chars in Table 2 contained any sulfur, it was below the sulfur detection levels (0.001%) of the Leco TruSpec Micro instrument. Using data from Table 2, Figure 5 was generated to show how the chemical composition of the sawdust char changed with increasing residence time in the FFB at conditions where the peak gas temperature ranged from 1163 to 1433 K. The composition of the raw sawdust is included as a reference. As expected, the sawdust chars grew more carbon-rich

EXPERIMENTAL RESULTS AND DISCUSSION

Sawdust Mass Release. Mass release refers to how much of the initial mass leaves the particle and is an indicator of the extent of pyrolysis. For example, 85 wt % dry, ash-free (daf) mass release during pyrolysis means that 85% of the initial particle’s daf mass transformed to volatiles (i.e., tar and light gas). The equation for daf mass release appears below

⎛ m0 ⎞ particle − mchar ⎟·100% % mass release (daf)=⎜⎜ 0 ⎟ ⎝ m particle − mash ⎠

(1)

0

where mchar, mash, and m particle are defined as the dry mass of the char, ash, and initial particle, respectively. It is common to also calculate mass release using the ash in the sawdust as a tracer since this calculation does not depend on the char collection efficiency during experimentation

⎛ x 0ash ⎞ ⎜ 1 − xash char ⎟ % mass releaseAsh Tracer (daf) = ⎜ ⎟·100% 0 ⎜ 1 − x ash ⎟ ⎠ ⎝

(2)

where xash char and x0ash are defined as the ash fraction in the char and virgin sawdust, respectively. Figure 4 shows the daf mass release from the FFB sawdust pyrolysis experiments as calculated by ash tracer and mass balance (using

Figure 4. Mass release of sawdust during FFB pyrolysis experiments.

Table 2. Elemental Composition of Chars from Sawdust FFB Pyrolysis Experiments

a

peak gas temperature (K)

particle residence time (ms)

collection height above burner (in.)

yield (wt %, daf)

C (wt %, daf)

H (wt %, daf)

N (wt %, daf)

1163 1163 1163 1163 1320 1320 1320 1433 1433 1433

32 55 78 102 29 40 51 23 31 39

1 2 3 4 1 1.5 2 1 1.5 2

13.8 6.7 6.4 6.4 6.4 5.5 6.0 5.8 6.4 5.4

58.58 61.32 68.66 73.25 66.07 69.01 70.04 61.48 65.39 72.48

4.98 3.54 2.57 3.37 5.35 3.59 3.07 3.28 2.76 2.52

0.20 0.22 0.27 0.25 0.17 0.30 0.31 0.24 0.28 0.26

Oa (wt %, daf) % ash (dry basis) 36.24 34.92 28.50 23.13 28.42 27.10 26.58 35.01 31.57 24.74

10.74 18.30 25.22 22.21 18.95 24.17 27.43 20.93 33.40 25.50

Calculated by difference. 944


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Figure 7. Silver birch tar yields from a fluidized-bed reactor (adapted from the work of Stiles and Kandiyoti,25 copyright (1989), with permission from Elsevier).

Figure 5. Compositional progression as sawdust transforms into char at 1163−1433 K in the FFB.

is suggested in the literature that there exists a small fraction of biomass tar that is or becomes refractory.4,19,29,30 Other researchers have shown that hotter reactor temperatures result in an increased fraction of polyaromatic compounds and condensed ring structures in the biomass tar.25,31,32 To test for refractory tar, sawdust was fed in the FFB at a condition where the peak gas temperature was near 1750 K using a particle residence time of 85 ms (collection height of 6.5ˈˈ above the burner). The average daf tar yield from two replicate experiments was 1.49 wt %. This suggests that the asymptotic biomass tar yields in Figure 6 were that of a refractory tar. Zhang et al.33 measured complete tar destruction when pyrolyzing Hinoki cypress sawdust in a drop-tube furnace above 1200 °C. The measured sawdust tar yield in the FFB at a hotter condition differs from the findings of Zhang et al., but could be explained by a difference in residence time, sawdust type, or how experimental tar was defined. The elemental composition of the collected sawdust tar is shown in Table 3. Each tar composition reported in the table typically represents the average of three to six replicates, and the average standard deviation of % C, % H, and % N for each tar was 3.47, 0.20, and 0.04 wt % daf, respectively. Although individual light gas species were not measured, it is possible to calculate the elemental composition of the bulk gas using Tables 1−3 and a mass balance. Sulfur was only detected in the sawdust tar from the 1163 K, 32 ms experimental condition. If there was any sulfur in the other nine tar samples, it was outside the sulfur detection limits (0.001%) of the Leco TruSpec Micro instrument. Table 4 shows a summary of the elemental compositions of sawdust tars at each peak temperature from the experiments of Nunn et al.34 and this work. The experiments of Nunn et al. involved the pyrolysis of sweet gum sawdust using an electrical screen heater at peak temperatures of 770, 895, and 1355 K. A single average tar composition was calculated at each peak temperature of the FFB in Table 4 by taking the average composition of sawdust tars that were collected from all residence times of the same peak temperature (see Table 3). For example, the average 1320 K tar composition is the average composition of sawdust tars obtained from 1320 K FFB experiments at 29, 40, and 51 ms. Temperature was found to have a greater impact on the elemental tar composition of C, H, and O than in the sweet gum sawdust pyrolysis of Nunn et al.34 This conclusion was drawn by the greater standard deviations of the elemental compositions of C, H, and O in the sawdust tars of this work when compared to those of Nunn et al. (see Table 4), even though the temperature range studied in this work was more narrow. The difference in observed impact of temperature on sawdust tar composition could be explained by the difference in experimental facilities. The sweet gum sawdust experiments were performed using an electrical screen heater, which allowed tar to escape the heated region of the electrical screen before much tar cracking occurred. Char Morphology. Figure 8a,b shows SEM images of sawdust char collected from the FFB reactor at peak gas temperatures of 1163 and 1320 K. The asymptotic mass release values for the conditions at which the chars were collected (see Figure 4) indicate that the chars

with increased residence time in the FFB reactor while the hydrogen and oxygen contents decreased. Tar Yields. The measured tar yields from sawdust pyrolysis are shown in Figure 6 and were calculated based on the daf mass that

Figure 6. Tar yields from sawdust pyrolysis experiments in the FFB. collected on the water-cooled polycarbonate filters in the FFB collection system. Each data point in Figure 6 is the average of four replicate experiments and has an average 95% confidence interval of ±0.72 wt % daf. The decreasing yields of sawdust tar at the 1163 K gas condition and the low tar yields at the 1320 and 1433 K conditions were the result of tar-cracking reactions, which results in a gas yield that increases proportionately to the destruction of tar. The high temperatures in the FFB resulted in very low tar yields, especially considering that sawdust tar yields in other experiments can be as high as 75 wt %.19 There is much literature that indicates wood tar begins to thermally crack into light gas near 500 °C.20 Scott et al.21 report that it is unlikely that a wood particle can still be in the primary pyrolysis phase at any temperature above 500 °C and that secondary reactions must occur above this temperature. Other researchers have studied the conditions at which maximum biomass tar yields occur and have concluded that these conditions involve short residence times with high heating rates at a maximum temperature near 500 °C.22−24 Plots in the literature, such as the one shown in Figure 7, indicate that tar yields from wood pyrolysis pass through a maximum near 500 °C, and then decline at higher temperatures due to secondary tar-cracking reactions. Both high temperature and residence time contribute to the cracking of the wood tar into light gas. It is interesting to note that the tar yields from the sawdust pyrolysis experiments level off in Figure 6 near 1.5 wt % (daf) at each of the three temperature conditions in the FFB. This is important because even low tar yields in industrial processes can cause problems by corroding equipment, causing damage to motors and turbines, lowering catalyst efficiency, and condensing in transfer lines.26−28 It 945


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Table 3. Elemental Composition of Tars from Pyrolysis FFB Experiments of a Softwood Sawdust peak gas temperature (K)

particle residence time (ms)

yield (wt %, dafa)

C (wt %, daf)

H (wt %, daf)

N (wt %, daf)

Ob (wt %, daf)

S (wt %, daf)

1163 1163 1163 1163 1320 1320 1320 1433 1433 1433

32 55 78 102 29 40 51 23 31 39

6.5 3.1 2.0 1.5 2.1 1.2 1.4 0.8 1.8 1.6

65.29 73.99 77.22 80.79 77.45 80.15 81.34 80.92 82.43 89.97

5.05 4.68 4.53 4.57 4.95 4.46 4.53 3.95 2.88 3.64

0.39 0.27 0.29 0.26 0.53 0.55 0.60 0.70 0.38 0.26

29.23 21.06 17.96 14.38 17.07 14.84 13.53 14.43 14.31 6.13

0.04

a

c

daf = dry and ash-free basis. bCalculated by difference. cBelow detection limits of instrument.

Table 4. Effect of Peak Temperature on the Elemental Composition of Sawdust Tar peak temp (data from Nunn et al.34) C H O a

peak temp (this work)

770 K

895 K

1355 K

standard deviation of tar element

52.6% 6.1% 32.3%

53.9% 5.9% 37.1%

55.0% 6.2% 32.3%

1.20 0.15 2.77

avga C avg H avg O

1163 K

1320 K

1433 K

standard deviation of tar element

74.3% 4.7% 20.7%

79.6% 4.6% 15.1%

84.4% 3.5% 11.6%

5.06 0.69 4.55

avg = average elemental composition of all sawdust tars collected at a single temperature, but different residence times, in the FFB (see Table 3).

Figure 9. SEM images of the sawdust char at two magnifications collected at 1433 K, 39 ms in the FFB. Figure 8. SEM images of the sawdust char collected at (a) 1163 K, 102 ms and (b) 1320 K, 40 ms in the FFB. reactor could be explained by particle fracturing due to the eruption of expanding gas-filled pockets within the char particles; this explanation was first used to describe the small size of white oak char particles collected from a laminar entrained flow reactor above 900 °C at a heating rate of ∼1000 °C/s and a particle residence time of 1 s.32 Jarvis et al.32 also point out that the sawdust char particles must be somewhat plastic above 900 °C in order to explain how the char particles maintain sphericity despite particle fracturing. To test for the effect of temperature and residence time on sawdust char morphology, sawdust was fed in the FFB at 1433 K, 110 ms and 1751 K, 85 ms, both using a reacting distance of 6.5ˈˈ between the burner and the collection probe. The sawdust char collected at the 1433 K, 110 ms condition is shown in Figure 10. A mixture of sawdust char shapes was observed even when given additional residence time in the FFB. It appeared that the additional residence time (compare with Figure 9a) did result in a lower fraction of particles with a fibrous structure having high aspect ratios, although a definite statement on the matter would require a much more exhaustive analysis than a few SEM images can provide. However, the vast majority of sawdust char particles collected at 1751 K, 85 ms (see Figure 11a,b) was spherical in shape. This suggests that a hotter temperature results in a larger percentage of spherelike sawdust char particles when comparing to experiments with similar particle residence times. It is important to note that all the SEM images shown (Figures 8−11) in this work were from conditions in which the sawdust was fully pyrolyzed (see Figure 4), which may suggest that the morphology of sawdust char continues to change after complete pyrolysis.

were fully pyrolyzed. Similar to the findings of Dupont et al.,35 two different kinds of sawdust char particles were observed. The first type of particles was spherical, having lost the original sawdust structure due to melting of the cell structure and plastic transformations.36 The second type of particles had a fibrous structure with high aspect ratios that resemble the original sawdust (see Figure 1). The presence of two kinds of sawdust char particles is in contrast to the rapid pyrolysis sawdust experiments of Zhang et al.33 using a drop-tube reactor, where only spherical sawdust char was observed. Perhaps the relatively long particle residence times (>3 s) used in the experiments of Zhang et al. can explain these morphological differences in the sawdust chars. The presence of any spherical sawdust char is only characteristic of chars pyrolyzed at high heating rates since Cetin et al.36 did not observe any major structural changes of sawdust pyrolyzed at a low heating rate of 20 K/s. Figure 9a shows sawdust char collected from the FFB reactor at a peak gas temperature of 1433 K. The two different types of sawdust char are still observed at the higher temperature. Note that some of the spherical char particles contained a large central void, which can also be observed in the sawdust chars in Figure 8a,b. In addition, many of the spherical sawdust char particles collected from the FFB reactor had a significantly smaller diameter than the virgin sawdust size fraction of 45−75 μm. For example, the average diameter of the char particles shown in Figure 9b is 15 μm. These small spherical char particles were present in the FFB sawdust chars collected at other temperatures as well (see Figure 8a,b). The small char particles noticed from the FFB 946


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m pc p

dTp dt

= hθA p(Tgas − Tp) − σεpA p(Tp 4 − Twall 4) +

∑ riΔ ̇ Hrxn,i i

(3)

where the term on the left side of eq 3 describes the thermal inertia of the particle. The terms mp, cp, Tp, and dt are defined as the particle mass, particle heat capacity, particle temperature, and time step, respectively. The first term on the right-hand side of eq 3 describes the particle heating up from convective heat transfer, where h is the heat transfer coefficient (Nu·kgas/ dp), Ap is the external surface area of the particle, and Tgas is the gas temperature. The θ term is sometimes called the blowing factor and accounts for decreased heat transfer to the particle during high mass transfer from the particle (i.e., during pyrolysis).40 The blowing factor is calculated as

Figure 10. SEM images of the sawdust char collected at 1433 K, 110 ms in the FFB.

θ=

B e −1 B

(4)

where B is the transfer number and is defined by B=

⎛ dm p ⎞ ⎜ ⎟ 2π ·d p·kgas ⎝ dt ⎠ c pGAS

(5)

where cpGAS, kgas, and dp are the gas heat capacity, gas thermal conductivity, and particle diameter, respectively. The thermal conductivity of the gas is calculated at the film temperature, which is the average of the Tp and Tgas. The second term on the right-hand side in eq 3 is the radiative heat transfer to or from the particle, where σ, εp, and Twall are defined as the Stefan−Boltzmann constant (5.67 × 10−12 W/cm2/K), particle emissivity, and wall temperature, respectively. The last term of eq 3 takes into account the heat of pyrolysis and moisture evaporation (although dried particles were fed in this work), where ṙi is defined as dmp/dt. The input particle velocity profile was used by replacing 1/dt with vp/dz in eq 3. The particle temperature was calculated using eq 3 using a predictor-corrector technique that calculated ΔTp at every time step in order to update Tp. A variable time step was used that chose an appropriate time step to minimize computational error. The CPD model uses structural and kinetic parameters to describe a particular fuel. The structural parameters in the CPD model are molecular weight of the cluster (MWc1), molecular weight of side chains (Mδ), initial fraction of intact bridges (p0), coordination number (σ + 1), and initial fraction of char bridges (c0). The structural parameters typically come from 13C NMR measurements, except c0, which must be determined empirically. The definitions of the kinetic parameters in the CPD model are summarized in Table 5. Bio-CPD Model Background. The biomass devolatilization modeling efforts in this work stem from the efforts of Fletcher et al.41 and other researchers.42,43 Fletcher et al. proposed structural and kinetic parameters for the biomass components of cellulose, hemicellulose, and lignin for use in the CPD model. The parameters in Table 6 were determined mainly from 13C NMR measurements and known structures, whereas the parameters in Table 7 came from fine-tuning kinetic parameters using pyrolysis data and the optimization program OptdesX44 by Design Synthesis Inc. Fletcher et al.41 used the structural and kinetic parameters to compare predicted primary pyrolysis yields of cellulose, hemicellulose (i.e., xylan

Figure 11. SEM images of the sawdust char at two magnifications collected at 1751 K, 85 ms in the FFB.

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BIOMASS PYROLYSIS MODELING CPD Model Background. The chemical percolation devolatilization (CPD) model37 was originally developed to predict coal devolatilization yields as a function of time, temperature, pressure, and heating rate using a description of the coal’s chemical structure. Coal is modeled as aromatic clusters connected by labile bridges. Upon heating during pyrolysis, the bridges become activated and can proceed through two competing pathways. The intermediate bridges can either break to form side chains and subsequent light gas or else release light gas and simultaneously form a char bridge that will remain stable at typical pyrolysis temperatures. The competing reaction rates are a function of kinetic parameters, including activation energy, pre-exponential factor, and a standard deviation for the activation energy. The rate at which bridges rupture is modeled, and percolation statistics for Bethe lattices predict the relationship between the number of cleaved bridges and detached clusters. The tar yields are calculated using Raoult’s law, a vapor pressure correlation, and a flash calculation at every time step. Secondary reactions of tar (i.e., tar cracking into light gas) are not part of the CPD model. It is important to only compare the CPD model with pyrolysis experiments that have been conducted using sufficiently small particles since the model assumes that the temperature gradients within the particle are insignificant, which is only true when the Biot number is less than 0.1.38 The version of the CPD model that was used in this work required profiles of particle velocity and gas temperature. Particle temperature was calculated using the following particle energy equation of an assumed-spherical particle in an inert laminar flow39 947


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the CPD model uses to predict the number of detached clusters as a function of cleaved bridges. Perhaps different statistical models besides that for Bethe lattices would better model the linear polymers of cellulose and hemicellulose, but the initial results of the CPD model are promising for predicting measured pyrolysis yields of cellulose45 from the literature. Further Development of the Bio-CPD Model. As discussed above, Fletcher et al.41 determined the structural and kinetic parameters for cellulose, hemicellulose, and lignin for use in the CPD model. This work extends to modeling biomass pyrolysis by combining the predicted devolatilization yields of biomass components (i.e., cellulose, hemicellulose, lignin) in addition to adding a tar-cracking model to accurately predict tar and light gas yields. The char-to-gas kinetic ratio, ρ, was changed for hemicellulose from 1.08 to 1.35 in this work to more accurately predict fully pyrolyzed tar, char, and light gas yields of xylanbased hemicellulose from the literature.46 Fletcher et al.47 previously used the kinetic parameters in Table 7 (using ρ = 1.08) to model xylan pyrolysis at a heating rate of 20 K/min and demonstrated good agreement between predicted and measured xylan char yields from a thermogravimetric analyzer (TGA).48 The accurate prediction of xylan char yields from the CPD model would also indicate that the xylan volatiles (i.e., tar + gas) yield would be accurate from the TGA experiments. However, because tar and gas yields from xylan pyrolysis were not reported in the TGA experiments,48 the ability of the CPD model to predict accurate tar and gas yields individually from xylan pyrolysis was not previously evaluated.41 When using ρ = 1.35 in the CPD model to predict the final pyrolysis yields of xylan at a heating rate of 20 K/min, the code predicted tar, char, and light gas fractions of 0.45, 0.23, and 0.32, respectively. Using the previous value of ρ = 1.08, the CPD model predicted tar, char, and light gas fractions of 0.35, 0.27, and 0.38, respectively. Changing the kinetic parameter ρ for xylan only changed the fully pyrolyzed char yield by 4 wt %, but mainly changed the tar and light gas fractions to more accurately match measured xylan pyrolysis yields46 at temperatures of 475−500 °C, where the effects of tar cracking were minimal. To predict biomass pyrolysis yields, the CPD model was run separately for cellulose, hemicellulose, and lignin using the parameters in Tables 6 and 7, with the exception of using a ρ value of 1.35 for hemicellulose. Running the CPD model separately for each of the three biomass components resulted in the predicted devolatilization yields from primary pyrolysis of pure cellulose, hemicellulose, and lignin. The char, tar, and light gas yields of a particular biomass were then calculated as the weighted average of the pyrolysis yields of cellulose, hemicellulose, and lignin. When the cellulose, hemicellulose, and lignin percentages of a particular biomass could not be found in the literature, empirical equations were used in order to predict the cellulose and lignin percentages based on the ultimate and proximate analyses.42 The amounts of cellulose, hemicellulose, and lignin were normalized so that they add to 100%. The effect of extractives on the mechanism of biomass pyrolysis is not specifically addressed in the Bio-CPD model, even though it is known that biomass extractives can catalyze or alter the reactions that occur during biomass pyrolysis.49 As explained above, tar thermally cracking into light gas significantly affects tar and gas yields during biomass pyrolysis at temperatures exceeding 500 °C. Tar-cracking reactions result in a gas yield that increases proportionately to the destruction of tar. The CPD model treats the solid-phase reactions at the

Table 5. Definition of the Kinetic Parameters for the CPD Model Eb, kcal/mol Ab, s−1 σb, kcal/mol Eg, kcal/mol Ag, s−1 σg, kcal/mol ρ Ec, kcal/mol Ecross, kcal/mol Across, s−1

bridge breaking activation energy bridge pre-exponential factor standard deviation of Eb gas formation activation energy gas pre-exponential factor standard deviation of Eg char-to-gas kinetic ratio difference in activation energy between bridge breaking and char formation cluster cross-linking activation energy cluster pre-exponential factor

Table 6. Structural Parameters to Model Biomass Devolatilization Using the CPD Model41 structural parameter

MWc1

Mδ

p0

σ+1

c0

cellulose hardwood hemicellulose softwood hemicellulose hardwood lignin softwood lignin

81 77.5 81 208 186

22.7 21.5 22.7 39 34

1.0 1.0 1.0 0.71 0.71

3.0 3.0 3.0 3.5 3.5

0.0 0.0 0.0 0.0 0.0

Table 7. Kinetic Parameters to Model Biomass Devolatilization Using the CPD Model41

a

kinetic parameter

cellulose

hemicellulosea

lignin

Eb, kcal/mol Ab, s−1 σb, kcal/mol Eg, kcal/mol Ag, s−1 σg, kcal/mol ρ Ec, kcal/mol Ecross, kcal/mol Across, s−1

55.4 2.0 × 1016 4.1 61.2 3.0 × 1015 8.1 100 0.0 65.0 3.0 × 1015

51.5 1.2 × 1020 0.1 38.2 3.0 × 1015 5.0 1.08b 0.0 65.0 3.0 × 1015

55.4 7.0 × 1016 0.5 69.0 2.3 × 1019 2.6 1.7 0.0 65.0 3.0 × 1015

Xylan. bThis parameter was changed to 1.35 in this work.

and glucomannan), lignin, and black liquor with experimental pyrolysis yields. The CPD model requires a base structural unit, which, for coal, is an aromatic cluster. Fletcher et al.41 defined the base unit for biomass components of cellulose, hemicellulose, and lignin. The base unit for lignin was coniferyl, coumaryl, and sinapyl alcohols. The fixed anomeric carbon and attached hydrogen were considered the base cluster for cellulose and hemicellulose (see Figure 12). This definition of the base cluster for cellulose and hemicelluose translated into three intact bridges, an ether bridge, and two bridges that make up the sugar ring with their attached side chains. The chemical similarity between lignin and a low rank coal makes lignin a good candidate for Bethe lattice statistics, which

Figure 12. Anomeric carbon in cellulose. 948


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The use of Vizzini’s tar-cracking model maintained a very generalized biomass devolatilization model since both the primary and the secondary pyrolysis yields were predicted based on a weighted average of the individual biomass components of cellulose, hemicellulose, and lignin.

current particle temperature, but tar-cracking reactions occur at the gas temperature. Therefore, a separate tar-cracking model is required. The first-order tar-cracking model of Vizzini et al.50 was combined with the results of the CPD model to obtain accurate biomass pyrolysis yields above 500 °C. Vizzini et al.50 showed that their first-order tar-cracking model (see eqs 6 and 7) using the kinetic parameters in Table 8 accurately predicted

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RESULTS AND DISCUSSION Modeling of Sawdust Pyrolysis Experiments from FFB. Figure 13a−c shows a comparison between measured and modeled pyrolysis yields of sawdust from FFB experiments at peak gas temperatures of 1163, 1320, and 1433 K. The predicted pyrolysis yields in these figures were performed using the CPD model with the kinetic and structural parameters of Fletcher et al.41 combined with Vizzini’s50 tar-cracking model. The empirical correlation of Sheng and Azevedo42 was used to estimate the fraction of biomass components in the softwood sawdust. The resulting fractions for the cellulose, hemicellulose, and lignin components were 0.418, 0.293, and 0.289, respectively. The predicted sawdust pyrolysis yields matched the measured sawdust pyrolysis yields in the FFB within 8.0 wt % upon complete pyrolysis at 1163, 1320, and 1433 K. Although predictions with Vizzini’s tar-cracking model matched the tar and gas yields at the conditions of 1320 and 1433 K (see Figure 13b,c), this model underestimated the rate of tar cracking at the 1163 K condition with average discrepancies of 18.3 wt % between modeled and measured tar yields (see Figure 13a). The discrepancy between modeled and measured sawdust volatile yields at the early residence times of the 1163 K condition was lessened to 5.3 wt % using the tar-cracking kinetics of Fagbemi et al.20 that was developed for sawdust (instead of Vizzini’s generalized biomass tar-cracking model). This resulted in a better fit of the measured data, as shown in Figure 13d, but lessened the generality of the overall pyrolysis

Table 8. Kinetic Parameters for Predicting Biomass Tar Cracking50 biomass component

A (s−1)

E (kcal/mol)

cellulose hemicellulose lignin

3.0 × 106 1.49 × 106 1.49 × 106

26.2 26.2 26.2

tar and light gas yields to 850 °C from the separate pyrolysis of red maple sawdust and cellulose.

Tar → Light Gas

(6)

⎡ −dxtar ⎛ − E ⎞⎤ ⎟ ·x = −k·xtar = ⎢ −A ·exp⎜ ⎝ R ·T ⎠⎥⎦ tar ⎣ dt

(7)

In the tar-cracking model, xtar is the mass fraction of the biomass that is tar, and k is the rate constant with kinetic parameters A and E. In this work, the first-order tar-cracking model was applied to the primary pyrolysis tar yields of cellulose, hemicellulose, and lignin from the CPD model. The resulting tar yields were then combined by the weighted average of cellulose, hemicellulose, and lignin. The fraction of tar that was calculated to thermally crack to light gas was added to the gas yields. The weighted CPD char yield remained unchanged when considering secondary tar-cracking reactions.

Figure 13. Comparison of measured and modeled sawdust pyrolysis yields in the FFB at 1 atm and peak gas temperatures of 1163−1433 K. 949


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model. The values of A and E that were used in Fagbemi’s model were 4.28 × 106 s−1 and 107.5 kJ/mol, respectively. The tar-cracking predictions in Figure 13d were much improved using the sawdust-specific tar-cracking rate. Note that the prediction of the char yields did not change in Figure 13a−d since tar cracking only affects the tar and gas yields. Also note that both the biomass tar-cracking model of Vizzini et al.50 and the model of Fagbemi et al.20 predict complete tar cracking into light gas and do not consider the small amount of refractory tar discussed above. Figure 14 is included for reference to show the predictions of the CPD model using the kinetic and structural parameters of

allowed an assessment of the predicted biomass yields of the CPD model only considering primary pyrolysis without use of a tar-cracking model. Figure 15a shows the CPD model’s predictions of fully pyrolyzed yields of 100−212 μm pine sawdust in a drop-tube reactor.51 The biomass component fractions used to model the pine sawdust were taken as the average of several values from the literature52−57 since the component fractions were not reported for this experiment. The resulting cellulose, hemicellulose, and lignin fractions used in modeling were 0.427, 0.29, and 0.283, respectively. Even though some of the measured data in Figure 15a were at temperatures above 500 °C, tar cracking was not taken into account since secondary reactions were suppressed by quickly removing the pyrolysis vapors during experimentation at short residence times.51 Despite using short residence times and low temperatures to suppress tar-cracking reactions in their experimental setup, a small fraction of tar cracking would explain the slight decreasing tar yields and increasing gas yields from 500 to 600 °C in the data. To model the measured data in Figure 15a,b, an isothermal gas temperature profile was assumed in the reactor since measured temperature profiles were not reported.51 To collect the measured sawdust pyrolysis yields shown in Figure 15a, the researchers modified their drop tube reactor by inserting a steel-wire plug that completely covered the cross-sectional area of the tube. The steel plug captured all the pyrolyzing particles and was inserted 0.05 m downstream of where the sawdust particles were introduced into the reactor.51 Since the sawdust particles on the steel-wire plug were continually subjected to high temperature, the measured sawdust pyrolysis yields in Figure 15a are those from complete pyrolysis. Thus, the modeled pyrolysis yields in Figure 15a were those from complete pyrolysis, as predicted by the CPD model. The discrepancy between measured and modeled sawdust pyrolysis yields in Figure 15a can mostly be explained by material losses in the collection system, which was reported to be between 3.7 and 15.6 wt % (10 wt % on average).51 Figure 15b shows the modeled and measured mass conversion of pine sawdust in drop-tube experiments as a function of particle residence time at temperatures between 500 and 600 °C. The predicted mass conversions from the CPD model were within 10.0 wt % on average of measured data for the three temperatures tested. The small under-prediction of mass conversion at residence times above 200 ms at 600 °C

Figure 14. Comparison of measured and modeled FFB sawdust pyrolysis yields at 1163 K without using a tar-cracking model.

Fletcher et al.41 without including a secondary tar-cracking model for the FFB 1163 K case. The modeled yields in the figure are the predicted sawdust yields resulting solely from primary pyrolysis, as predicted by the CPD model. The predicted tar yield is close to the measured gas yield in Figure 14, rather than the measured tar yield. This figure clearly illustrates the need for a tar-cracking model above 500 °C for biomass since the measured tar and gas yields were so different from their predicted values. Modeling of Pine Sawdust Pyrolysis without Tar Cracking from the Literature. The CPD model using the parameters of Fletcher et al.41 (see Tables 6 and 7) was also evaluated with a data set from the literature that was obtained at conditions where minimal tar cracking occurred. This

Figure 15. Comparison of measured and modeled pine sawdust primary pyrolysis yields from a drop-tube reactor.51 950


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Importance of Inorganics. Modeling biomass pyrolysis as a sum of its main components (cellulose, hemicellulose, and lignin) with the CPD model was shown to accurately predict the pyrolysis of three kinds of sawdust in this work, although it is important to note that sawdust has a low ash content when compared with other kinds of biomass. Raveendran et al.59 stated that mineral matter plays a large factor in the distribution of biomass pyrolysis products. Experimental work is currently underway to evaluate the effectiveness of the CPD model to predict the pyrolysis yields of biomass with higher ash content using straw, corn stover, and switchgrass.60

may likely be explained by incomplete collection efficiencies in the reactor, as was previously mentioned. Modeling of Beech Sawdust Pyrolysis from the Literature. The CPD model using the parameters of Fletcher et al.41 (see Tables 6 and 7) was evaluated by comparing the measured and modeled solid mass fraction of beech sawdust that was heated at a relatively low heating rate in a TGA.58 Figure 16 shows the CPD model’s predictions of weight loss

■

CONCLUSIONS The pyrolysis yields of char and tar were measured for a single softwood sawdust in a flat-flame burner reactor from 1163 to 1433 K. The measured pyrolysis yields of sawdust are believed to be similar to those that would occur in an industrial entrained-flow combustor or gasifier due to the high heating rate of 105 K/s of the FFB reactor. A refractory tar yield was measured near 1.5 wt %. A high percentage of the fully pyrolyzed sawdust char lost its original structure and turned spherical, although some high-aspect-ratio sawdust char particles were also collected. It is suggested that the morphology of sawdust char may continue to change after complete mass release from pyrolysis. Sawdust pyrolysis was modeled using the chemical percolation devolatilization (CPD) model with the assumption that biomass devolatilization occurred as the weighted sum of its components (i.e, cellulose, hemicellulose, lignin). A tarcracking model from the literature was applied to the primary pyrolysis tar yields of the CPD model since tar cracking greatly impacts biomass pyrolysis yields above 500 °C. Predicted devolatilization yields of three different kinds of sawdust from three reactors (flat-flame burner, drop-tube, and TGA) were accurately predicted.

Figure 16. Comparison of measured and modeled char yields from beech sawdust pyrolysis in a TGA.58

curves from the pyrolysis of sawdust (diameter < 80 μm) at a heating rate of 1000 K/min to final temperatures ranging from 573 to 708 K with an isothermal stage upon heating to the final temperature. The reported biomass component fractions58 of the beech sawdust were normalized for CPD modeling. The resulting cellulose, hemicellulose, and lignin fractions were 0.459, 0.337, and 0.204, respectively. CPD predictions are shown in Figure 16. A tar-cracking model was not utilized with these predictions of the CPD model since tar cracking does not have an appreciable effect on char yields. The modeled solid mass fraction in Figure 16 was typically within 9 wt % of the measured beech data at 637 K, and the final solid mass fraction at 1650 s was only overpredicted by 8.9 wt %. Although the CPD model predicted a quicker initial mass loss of the solid beech sawdust than was measured at 708 K, the model captured the general character of the measured mass-loss curve at 708 K (see Figure 16). The predicted final solid mass fraction at 708 K was within 3.9 wt % of the measured value. Although not shown in Figure 16, the prediction of the CPD model was also compared with the aforementioned TGA data58 at the lower temperatures of 573 and 593 K. The CPD model overpredicted the final char yield at 2000 s by an average of 24 wt % at both temperatures. Although there was a wide discrepancy between the measured and modeled data at these lower temperatures, biomass pyrolysis of industrial significance typically occurs at higher temperatures (excluding torrefaction). The predictions of the Bio-CPD model at 573, 593, 637, and 708 K were all obtained without adjusting any model parameters. If being able to predict biomass pyrolysis at very low temperatures is of interest, the Bio-CPD model has the potential for improvement by modifying kinetic parameters. Nevertheless, the ability of the Bio-CPD model to predict measured biomass pyrolysis data over a wide variety of feedstock varieties, heating rates, and final temperatures is encouraging.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: tom_fl[email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was sponsored, in part, by Grant 2009-10006-06020 from the U.S. Department of Agriculture/NIFA. The authors wish to acknowledge Kolbein Kolste for helping with the experiments, and BYU’s Microscopy lab for their assistance in obtaining the SEM images.

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