Energy & Fuels 1994,8, 671-679
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FLASHCHAIN Theory for Rapid Coal Devolatilization Kinetics. 5. Interpreting Rates of Devolatilization for Various Coal Types and Operating Conditions Stephen Niksa Molecular Physics Laboratory, SRI International, 333 Ravenswood Avenue, Menlo Park, California 94025 Received September 16, 1993. Revised Manuscript Received February 8,1994"
In this paper, FLASHCHAIN predictions based on the extended submodel for coal constitution developed in part 4 are evaluated for transient devolatilization. The complete dynamic behavior of 15 coals is evaluated at temperatures from 500 to 1200 K and reaction times to 10 s. The dynamics are depicted for extended heating periods at temperatures too low to achieve ultimate yields, as well as for nonisothermal decompositionduring rapid thermal transients. In almost all cases the predictions are within experimental uncertainty throughout. They also depict several important aspects of the dependence on coal rank, including a shift in the onset of devolatilization to higher temperatures with coals of higher rank, and an initial abundance of gases for low rank coals and of tars for bituminous coals. Based on its quantitative performance, the theory is also used to rigorously define nominal devolatilization rates for diverse coal types and broad ranges of operating conditions. At all conditions, the apparent activation energies are far too low to associate with any pyrolytic scission of model bridge compounds, such as ethylene linkages in polynuclear aromatics. These results also prove that heat and mass transport resistances are not responsible for the low values, because this theory is completely free of these considerations. Rather, the combined impact of chemical kinetics, the statistics for depolymerization and cross-linking, and flash distillation obscures any conceivable relations among rates of weight loss or tar evolution and the rates of the chemical reactions that underlie devolatilization. Whereas nominal rates are rather insensitive to coal type variations, they vary significantly with reaction time during isothermal devolatilization and, especially, with changes in heating rate. In fact, the FLASHCHAIN predictions show that a single pair of rate parameters cannot possibly represent the devolatilization behavior of any coal for different heating rates, at odds with other recent assignments.
Introduction The need to manage complex coal processing chemistry over diverse operating conditions has long been a driving force for coal research, and new approaches to measure and interpret devolatilization rates for diverse operating conditions are continually being reported. The progress of recent times has been reviewed by van Heek,' Howard,2 and Solomon,Serio, and Suuberg.3 Despite such attention, no aspect of coal devolatilization continues to stimulate controversyand confusionlike the nominal devolatilization rates for broad ranges of coal rank and operating conditions. A t low heating rates, the evolution rates of individual products or total weight loss can be monitored directly in thermogravimetric analyzers (TGA), so their magnitudes are known within reasonable tolerances. But even accurate rate determinations are hard to interpret because coal devolatilization is not amenable to simple kinetic rate laws. It is even harder to extrapolate rates for slow heating conditions to the most relevant technological conditions. The prospects in the laboratory deteriorate at heating rates faster than about 100 K/s, because there are no means to published in Advance ACS Abstracts, March 15, 1994. (1)Juntgen, H.; van Heek, K. H. Fuel Process. Technol. 1979,2,261. (2) Howard, J. B. In Chemistry of Coal Utilization, 2nd suppl., Elliot, M. A., Ed.; Wiley-Interscience: New York, 1982; Chapter 12. (3) Solomon,P. R.; Serio,M. A.; Suuberg, E. M. B o g . Energy Combust. Sci. 1993, e Abstract
monitor coal devolatilization rates directly during rapid thermal transients. Rates must be inferred from integral conversion data by fitting the parameters in a reaction model. During the fitting procedure, uncertainties in the operating conditions translate directly into uncertainties in the modeling parameters. And more often than not in the past, erroneous time-temperature histories compounded the intrinsic deficiencies of most models to undermine the reliability of extrapolations. In this paper, numerous data evaluations for coals across the rank spectrum demonstrate the utility of FLASHCHAIN for predicting dynamic devolatilization behavior. The complete behavior of 15 coals is evaluated at temperatures from 500 to 1200 K and reaction times to 10 s, including several cases for pyrolysis during heat-up as well as during extended reaction periods at different temperatures. Once the quantitative accuracy of the transient predictions has been established, the theory is used to rigorously define nominal devolatilization rates for diverse coal types and broad ranges of heating rate, reaction time, temperature, and pressure. In all cases, the theory predicts nominal rates that have very low activation energies, even though it has no heat or mass transport limitations whatsoever. The rank dependence of nominal rates is modest, showing a range of roughly one order of magnitude for coals across the rank spectrum. But no one pair of rate parameters can describe the devolatilization of any coal type at different heating rates, at odds with other
0887-0624/94/2508-0671~0~~50/00 1994 American Chemical Society
672 Energy &Fuels, Vol. 8, No. 3, 1994
recent assignments. These findings have important implications about the ways that nominal devolatilization rates can be used and abused, first, as comparative yardsticks for results from different laboratory studies and, second, as a basis to relate the devolatilization behavior of coals and model compounds. General Guidelines for the Data Evaluations. Whereas the data required to evaluate predicted ultimate yields in part 4 is rather insensitive to variations in the heating conditions, the database of transient measurements needed to validate predicted rates in this paper is subject to serious concerns. Over the years heated wiregrid reactors became the workhorse laboratory systems in this field, but legitimate problems with different approaches to their thermometry and calibration were recognized only r e ~ e n t l y .It ~ is even more difficult to monitor transient sample temperatures in entrained flow reactors because heating rates are faster and the pyrometry of coal suspensions is subject to many potential interferences. Virtually all of the reported temperature measurements of entrained coal suspensions were taken at suspension loadings that are so high that radial temperature gradients must be large in the regions where devolatilization occurs (although they have not yet been characterized or factored into the data reduction^).^^^ The notable exception of Fletcher's temperature data on individual entrained coal particles must be excluded because the conversion data is scattered and the ultimate yields show an implausibly large variation for two different initial particle sizes of the same c ~ a l . ~ J In the midst of such concerns it is difficult or, perhaps, unreasonably optimistic to compile a database from many separate laboratory studies that shows the behavior of a wide range of coal types. Instead, two recent wire-grid studies that each examine several samples will be used to establish the major findings on coal rank, then a few isolated cases will be brought in to suggest more versatility. The thermal histories in Xu and Tomita's experiments are unassailable because they used a Curie point induction heating scheme that circumvents the need for transient therm~metry.~ Ultimate grid temperatures are known very well and regarded as the actual sample temperature. Although their reported heating rate of 3000 K/s is only an estimate, each run also included a 4-9 isothermal reaction period. Such extended heating mitigates the impact of ambiguities in the heating characteristics and also in the cooling characteristics of this heater. Freihaut and Proscia's study contains transient and ultimate weight loss and tar yields from several coals."J Their experimental techniques are supported by very careful examinations of the thermometry system: although the tar yields are badly scattered in some cases and some of the transient and ultimate yields are clearly incompatible. These defects will be pointed out below. The operation of this grid heater is also peculiar in so far as heating rates and ultimate temperatures are not independently specified in cases without any isothermal reaction periods; rather, for transient studies, these (4) Freihaut, J. D.; Proscia, W. M. Energy Fuels 1989, 3, 625.
(5) Solomon, P. R.; Serio, M. A.; Carangelo, R. M.; Markham, J. R. Fuel 1986, 65, 182. (6)Serio, M. A,; Hamblen, D. G.; Markham, J. R.; Solomon, P. R. Energy Fuels 1987, 1 , 138. (7) Fletcher, T. H. Combust.Flame 1989, 78,223. (8) Fletcher, T. H. Combust.Sci. Technol. 1989, 63, 89. (9) Xu, W.-C.; Tomita, A. Fuel 1987, 66 (5),632. (10)Freihaut, J.D.; Proscia, W. M. Final Report on U. S. DOEContract No. DE-AC22-89PC89759, Pittsburgh Energy Technology Center, 1991.
Niksa operating conditions are related by q = 1.169T-60, where q is the heating rate (K/s) and T i s the ultimate reaction temperature (K). This regression pertains to all coal types except PSOC-l451D, the Pittsburgh No. 8 sample, for which the heating rates of individual runs are not reported. For the observed range of ultimate temperatures, from 575 to 1150 K, heating rates double from 610 to 1200 K/s. Consequently, there is a significant reduction in reaction time among sequential cases of heating to progressively hotter temperatures. The plots of transient yields versus ultimate temperature below give no indication of the variations in heating rate and reaction times, but they are accounted for in the FLASHCHAIN simulations for cases of devolatilization during heatup. The heating rates in cases with extended reaction periods at different temperatures are nearly the same at roughly 735 K/s. Finally, a cooling rate of 2000 K/s is apparent in the few representative thermograms that are reported, which is accounted for in the simulations (although this rate is so fast that decomposition during cooling is negligible). Wire-grid heaters were also used to collect the transient yields from hv bituminous samples in the studies by Oh et al." and Gibbons and Kandiyoti.12 Both assigned heating rates and reaction temperatures based on transient thermometry with fine-wire thermocouples, which are regarded as the actual sample temperatures. Compared to the other available data on decomposition of hv bituminous coals at this heating rate, these results are believed to be among the most reliable, perhaps because they use relatively low coal loadings on the wire grid.4 A cooling rate of -100 K/s is applied to Oh et al.'s data, and -500 K/s is applied to Gibbons and Kandiyoti's, consistent with the investigators' estimates. Among the results reported by Oh et al., only those which include tar yields and close the mass balance to within 5 % are included here. Serio et al.'s data for a heating rate of 0.5 K/s was acquired in a TGA where the sample temperature was assignedas the value from the thermocouple on the ceramic oven.6 There are two other potentially confusing aspects of product analysis and recovery to resolve in the data evaluations. Xu and Tomita report yields and compositions of oils (whichare mixturesof benzene, toluene, xylene, cresol, and phenol). In the evaluations oil yields are combined with the reported tar yields because their aromatic character should be associated with the nuclei in FLASHCHAIN, not the labile bridges. But none of the other investigators collected them. Against tar yields alone, FLASHCHAIN'S predicted yields of condensible liquids may be too high by as much as 5-7 wt % for hvA bituminous coals, but not by nearly as much for all other ranks.'3 The second potential complication pertains only to the lowest rank coals. Freihaut and Proscia observed substantial amounts of char fragments in some of their tar samples, especially from a Wyoming subbituminousC coal. Data from this coal are not considered. Their lignite data that are included (sample F1443) also seem to be affected, as seen in Figure 2a,b. Since this complication arises because low-rank chars can be friable enough to form fragments than can pass through wire grid reactors, it might also have affected some of the data in the other studies, although none of the other investigators made this observation. (11)Oh, M.-S.; Peters, W. A.; Howard, J. B. AIChE J. 1989,35, 775. (12) Gibbons, J.; Kandiyoti, R. Energy Fuels 1989, 3, 670. (13) Chen, J. C.; Niksa, S. Energy Fuels 1992, 6, 154.
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Figure 1. An evaluation of FLASHCHAIN predictions against transient weight loss ( 0 )and tar yields (0) reported by Xu and Tomitas for atmospheric devolatilization after 4 s at the indicated temperatures following heatup at roughly 3000 K/s. The eight coal types are arranged in order of increasing rank from parts a through h; their properties are defined in Table 2 of part 4. In the forthcoming FLASHCHAIN predictions, the operating conditions of temperature, heating rate, time, and/or pressure are varied to match those in the experiments, and the ultimate analyses reported in Table 2 of part 4 for the different coal samples are used to specify values for the structural model parameters (given in Table 3 of part 4). The reactivity parameters reported in part 4 (in Table 4, Figure 6a, and eqs 6 and 7) are applied without any modifications. A simulation of each thermal history requires less than 3 min on a 20-MH2, 386-type personal microcomputer.
Results Dynamic Behavior. A series of eight data evaluations appear in Figure 1for isothermal reaction periods of 4 s following heatup at 3000 K/s to the indicated temperatures. Insofar as ultimate yields are not achieved unless temperatures exceed 1100-1200 K, depending on coal type, these data express the reaction dynamics in terms of a temperature dependence. The series is arranged in order
of increasing rank of the samples to illustrate several facets of the rank dependence as continuous variations. FLASHCHAIN correctly predicts that (1)the onset of devolatilization shifts to higher temperatures for coals of higher rank; (2) the initial weight loss from low-rank coals is dominated by gases, whereas tars are the predominant early product from bituminous coals; (3) tar evolution ceases before gas evolution; (4) ultimate weight loss is fairly constant for ranks through hv bituminous, then falls off markedly for coals of higher rank; (5) tar yields pass through a weak maximum for hvA bituminous coals; and (6) the fraction of the total weight loss that is tar is an increasing function of rank. Specifically, the FLASHCHAIN predictions for weight loss are within experimental uncertainty in seven of the eight cases, the only exception being the systematically overpredicted values for the Hunter Valley hv bituminous coal. Otherwise, the predictions are quantitatively accurate from the onset to the completion of devolatilization. Very good quantitative performance is apparent in the predicted tar yields as well. They are within experimental
674 Energy & Fuels, Vol. 8, No. 3, 1994
uncertainty in seven of eight cases, the exception being the systematic underpredictions for the Morwell lignite. Otherwise,tar evolution is accurately predicted throughout the entire temperature range in these experiments. It is possible that the predicted asymptotic weight loss slightly underestimates the ultimate yields for the three low-rank samples (MW, SB, and WD), but not definite. On the one hand, these coals expel substantial amounts of oxygenated gases after tar evolution has ceased, as seen clearly in recent data on transient product distributions,’3 and this could explain the surge in yields at the highest temperature that is not represented by the simulations. On the other hand, fragmentation of these chars could also be responsible for the discrepancies. The data evaluations based on the 6 additional coals in Freihaut and Proscia’s study appear in Figure 2. Each case consists of separate figures for weight loss and tar yields, and two sets of results are shown for either quantity, data for “ultimate” and for transient conditions. Presenting the ultimate and transient behavior in the same plot has the advantage of clearly revealing defects in the transient data, because we know that transient yields during the heating period are necessarily lower than those with an extended isothermal reaction period at the same ultimate reaction temperature. Moreover, the transient data must asymptotically approach the ultimate values at the highest temperatures, where devolatization is complete by the end of the heating period, because the heating rates are similar in all cases in Figure 2. Data for the so-called “ultimate conditions” in Figure 2 are for 10-s isothermal reaction periods following heatup at 735 K/s to the indicated reaction temperatures. Transient conditions are for heatup to the indicated temperature followed by immediate cooling at the nominal rate of -2000 K/s. The heating rates vary from 600 to 1200 K/s for different reaction temperatures in the transient conditions, according to the correlation given earlier. The results for both kinds of thermal histories are depicted in Figure 2 as yields versus temperature, although the reaction times among the ultimate and transient conditions and among separate runs in a transient series are substantially different. In general, these data evaluations reaffirm the theory’s correct representation of the rank dependence of transient devolatilization. Again we see that for coals of higher rank, the onset of devolatilization shifts to higher temperatures; the initial weight loss is dominated by gas for low-rank coals and by tar for bituminous coals; and ultimate weight loss falls of rapidly for ranks higher than hv bituminous whereas ultimate tar yields pass through a shallow maximum for hvA bituminous coals. The FLASHCHAIN predictions depict all these tendencies. However, quantitative validations of the predictions throughout all stages of devolatilization are possible in only three of the six cases. Some of the most serious problems are illustrated in the data for the 1443lignite in Figure 2a. Ultimate and transient weight loss do converge to similar values, but only within the range from 54 to 65 wt % So it is difficult to evaluate the predicted ultimate weight loss of 53 wt %. Perhaps the scatter is a sign of lost char fragments. However, the ultimate tar yield of 20 wt ?6 is predicted within experimental uncertainty. At temperatures cooler than 800 K, the measured weight loss and tar yields for ultimate and transient conditions are indistinguishable, which is impossible. In this regime,this evaluation can only establish that the predictions accu-
.
Niksa rately depict the onset temperature for devolatilization of this coal. The comparisons for the hvA bituminous coals in Figure 2c-f are far more conclusive because the only problems with the data are that the transient tar yields do not asymptotically approach the ultimate values at the highest reaction temperatures. The weight loss and tar yields from both coals for the ultimate conditions at all reaction temperatures are predicted within experimental uncertainty, as is the transient weight loss for both coals. The transient tar yields for both coals are predicted within experimental uncertainty up to the point where the measured values deviate from the ultimate values at temperatures above 900 K. And at higher temperatures, the predictions must be more reliable than these data points. The evaluations for another hvA bituminous coal (F1451) and a low-volatility coal (F1516) in Figure 2g-j validate the predicted ultimate weight loss, except that the ultimate value for the lv bituminous is 5 wt 7% too high. Tar yields for the ultimate conditions are predicted fairly reliably, although there are clear inconsistencies among the data for transient and ultimate conditions. Predicted transient weight loss for F1451 are within experimental uncertainty up to 840 K and above 1000 K. For intermediate temperatures, the transient data approach and even exceed the ultimate values, which is impossible. The transient tar yields show the same aberration at intermediate temperatures, although the data at low and very high temperatures are predicted within experimentaluncertainty. These same defects are evident to an even greater degree in the data for the low-volatility coal. Transient weight loss and tar yields exceed the respective values for ultimate conditions at all but the highest temperatures, which is impossible. The weight loss from the anthracite (F1468) for both transient and ultimate conditions in Figure 2k,l is predicted within experimental uncertainty at all reaction temperatures. The evaluations with tar yields are also satisfactory, although such small magnitudes are comparable to the experimental uncertainty of 2 w t 5% throughout. Another case of transient weight loss and tar yields from an hvA bituminous sample appears in Figure 3. These data were reported by Oh et for heatup at 1000 K/s to the indicated temperatures followed immediately by extended cooling at -100 K/s. Both the total and tar yields are predicted within experimental uncertainty for all reaction temperatures for both pairs of FLASHCHAIN predictions. These two predictions differ in the way that sulfur contents are factored into the simulations. Normally sulfur is excluded,except as a contribution to the molecular weight of bridges. But for high sulfur contents such as the 5.3% in this coal, the propensity of sulfur to act as a cross-linking agent could potentially be significant. The two pairs of predictions differ only in their assignments for V B , the scission selectivity coefficient. In one case, VB is assigned as usual from (O/H)B;in the other it is assigned a lower value based on ((0+ S)/H)B. Accounting for sulfur’s cross-linking behavior lowers predicted weight loss by inhibiting tar formation during the later stages of devolatilization, as expected. Unfortunately, this data set cannot distinguish the best approach because one scenario improves the weight loss predictions while the other improves the predicted tar yields. Nevertheless, this case does illustrate the magnitude of the potential suppression of tar yields in high-sulfur coals.
Flashchain Theory
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Figure 2. An evaluation of FLASHCHAIN predictions against transient and ultimate total and tar yields reported by Freihaut and Proscia.loEach facing pair of figures is for one coal type. Plots on the left show transient weight loss during heatup to the indicated temperatures at heating rates from 600 to 1200 K/s ((0) and dashed curves), as well as "ultimate" weight loss for a 10-8 isothermal reaction period followingheatup at 735 K/s to the indicated temperatures [ ( O )and solid curves]. The analogoustransient and ultimate tar yields appear in the plots on the left. The six coal types are arranged in order of increasing rank from parta a through 1; their properties are defined in Table 2 of part 4.
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Figure 3. Comparison between predicted and observed weight loss [(e)and solid curvel and tar yields [(o)and dashed curvel from a Pittsburgh No. 8 hvA bituminous coal during heatup at lo00 K/s at atmospheric pressure, reported by Oh et al.ll Decomposition during cooling at 100 K/s is accounted for in the predictions at each temperature. The second set of predictions shown as dotted curves do not account for sulfur as a crosslinking agent, whereas the other pair does.
Throughout the data evaluations in Figures 1-3, the FLASHCHAIN predictions are within experimental uncertainties in the vast majority of cases, so that the total and tar yields are reliably predicted throughout all stages of devolatilization for coal types across the rank spectrum. The cases with the most significant discrepancies are those in which the data for the transient and ultimate conditions are inconsistent. On the basis of this performance, the theory will next be used to characterize the nominal rates of product evolution during devolatilization for diverse coal types and operating conditions, as an attempt to resolve the confusionthat currently surrounds these issues.
Nominal Devolatilization Rates Nominal devolatilization rates are defined by fitting single first-order rate expressions to predicted values of instantaneous tar and gas yields. Without a doubt such a simple reaction model is inadequate for any process modeling over even modest ranges of experimental conditions, for reasons that have been well known for years.2 Nevertheless, the assignedrate constants in the single firstorder reaction are often used in the literature as comparative yardsticks for many aspects of devolatilization. Nominal devolatilization rates are defined as
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where is the nominal devolatilization rate constant, A exp (-EJRT), s-l; P = (YT(~) + Yc(t))/V,"where YT(t) ) instantaneous yields of tar and gas, and Y G ( ~are respectively, predicted by FLASHCHAIN; and V" is the hypothetical ultimate yield, based on the sum of YT and YG for conditions severe enough to achieve the largest yield from a specified coal type, heating rate, and pressure. In what follows, V" is assigned from simulations at a temperature and reaction time that are high enough to achieve the maximum yields for the coal type, heating rate, and pressure under consideration. The rank dependence of nominal devolatilization rates is apparent in Figure 4 for uniform heating at 3000 K/s to at least 1500 K at atmospheric pressure. Also, lengths of the curvesconvey the temperature intervals for complete devolatilization. The nominal rates of devolatilization of
Figure 4. Arrhenius diagram of nominal devolatilization rates during transient heating at 3000 K/s at 0.1 MPa for the eight coal types that are used in Figure 1.In descending order, curves are for coals of increasing rank. The lengths of these curves indicate the temperature interval for devolatilization at this heating rate.
any coal type are highly nonuniform, so that straight lines on Arrhenius diagrams can only be coarse approximations. Variations are particularly severe soon after the onset of devolatilization, at the beginning of tar evolution, and much later, when tar evolution ceases. Eventually the gas evolution rates for all coal types approach very similar values. Rate variations with rank segregate roughly into two categories. For ranks from lignite through hv bituminous, rank variation are modest, especially during the later stages of devolatilization at high temperatures. Nominal rates for these coal types vary by a factor of 4 at 670 K but only by 25% at 1000 K. The temperature at which devolatilization commences also varies, from 550 K for the lignite to 650 K for the hv bituminous coals. (Of course, these temperatures will change for different heating rates.) Low-volatility coals comprise the second category. They begin to devolatilize at much higher temperatures and sustain significantly slower rates than the other ranks. Apparent activation energies for the two groups differ by nearly a factor of 2,being only 36 kJ/mol for the low-rank plus bituminous group and 65 kJ/mol for the low-volatility group. Such low values are the first of many indications that the rate parameters assigned to weight loss (or tar yields) are far too low to associate with any pyrolytic scission of model bridge compounds. To illustrate the consequences of the rank dependence of devolatilization rates, Figure 5 shows transient weight loss for atmospheric pyrolysis during heatup at 1000 K/s from four hv bituminous coals. These four coals span the range of behavior among all the coals in the hv bituminous subset described in part4. One simulation (with the largest transient yields) accounts for decomposition during slow cooling (at a rate of -100 K/s); the others are instantaneous values throughout. The predicted reaction rates and ultimate yields diminish for samples of higher rank. The data points in Figure 5 were taken from the compilation by Freihaut et al.4 of several studies in the literature with hv bituminous coals. They depict transient weight loss during heatup at 1000 K/s from two of the same coals behind the simulations. Whereas the data cover a range of 350 K when half of the ultimate yields have been evolved, the predictions for the four coal types without decomposition during cooling span a range of less than 100 K. An additional 100 K is added to this range by accounting for decomposition during extended slow cooling, but the predicted temperature range is still much smaller than
Energy & Fuels, Vol. 8, No. 3, 1994 677
Flashchain Theory
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Temperature,K Figure 5. Predicted and observed weight loss from hvA bituminous coals during heatup at 1000 K/s at atmospheric pressure, reported by Oh et al." [(a)and solid curvel and by Bautista et al.14 [(O)and dot-dashed curve]. All predictions are instantaneous valuesexcept for the uppermost solid curve,which accounts for decomposition during cooling at 100 K/s at each temperature. the observed range. Notwithstanding, such significant consequences should direct more attention to quenching schemes or, at least, to accurate determinations of cooling rates although, here again, there are conflicting results to consider. The data evaluation for Oh et al.'s experiments indicates that decomposition during cooling is substantial. However, Gibbons-Matham and Kandiyoti12report that a cooling rate of -100 K/s achieves nearly instantaneous resolution of transient devolatilization, at odds with the FLASHCHAIN predictions. To illustrate how nominal rates change as heating rates are varied, predicted rates for devolatilization during uniform heating at different rates appear in Figure 6a. They show that rate variations over the entire rank spectrum are never as substantial as those for varying the heating rate by a single order of magnitude. Regardless of coal type, rates of devolatilization increase in almost direct proportion to increases in heating rate; more specifically, rates increase by a factor of 6 for every order of magnitude increase in the heating rate. The apparent activation energies are surprisingly uniform, becoming only slightly larger for faster heating rates. They have the very low nominal value of about 60 kJ/mole for all coal types for this broad range of heating rates. Most important, these FLASHCHAIN simulations demonstrate that a single pair of rate parameters cannot possibly represent the devolatilization behavior of any coal for different heating rates. This finding is at odds with the recent claim of Solomon et al. that weight loss for different heating rates define one pair of parameters in a single first-order rate law. Their results are reproduced in Figure 6b.3 As explained in the primary reference, the data points are taken from experiments that impose heating rates within the range of order l O - L l O 5 K/s. Although the analysis that converts the weight loss data into the format of Figure 6b is not described, a nominal rate of 4.28 X 1014exp(-228.2/RT), where the activation energy is in kJ/mol, is assigned from this Arrhenius diagram. The comparison in Figure 6c goes a long way toward resolving these contradictoryfindings. This plot compares observed fractional weight loss versus temperature at heating rates of 1 and 1000 K/s to predictions based on Figure 6, a and b. Both solid curves are based on the
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Temperature,K Figure 6. (a) Nominal devolatilization rates for transient devolatilization during heatup at 1,100, and 104 K/s for samples XTSB (dotted curves),XTLD (solid curves),andXTKS (dashed curves). (b) Arrhenius diagram reported by Solomon et al.8 for devolatilization during nominal heating rates from 10-2 to 106 K/s. (c) Evaluation of transient fractional weight loss based on nominal rates in (a), as solid curves, and (b), as dotted curves. Data are from Gibbons and Kandiyoti12for 1 K/s (0) and for 1000 K/s(@)and from Serio et al.6 for 0.5 K/s (v). FLASHCHAIN predictions in Figure 6a for the hvA bituminous sample (XTLD). The rates have the same activation energy (58.1 kJ/mol) and respective A factors of 1.5 X 102 and 2 X lo4 s-1 for the low and high heating rate. Both dotted curves are based on the weight loss rate defined by Figure 6b. The data correlations with a low activation energy from FLASHCHAIN are within experimental uncertainty at all temperatures for both heating rates. But the regression of Solomon et al. can only represent the temperature a t which about half of the
678 Energy & Fuels, Vol. 8, No. 3, 1994
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Figure 7. Nominal devolatilization rates for an hvA bituminous (XTLD) from for heatup at 3000K's to 725 (dashed curve), 925 (solid curve), and 1300 K (dotted curve).
ultimate yield has been released at both heating rates. It misses the onset of devolatilization by more than 100 K at both heating rates and significantly underpredicts both of the temperature intervals needed for complete devolatilization, because the reaction rates are overpredicted by orders of magnitude. The variability of nominal devolatilization rates for ranges of reaction time and ambient pressure is apparent in Figures 7 and 8. The rates in Figure 7 are for devolatilization of an hv bituminous coal at three temperatures following heatup at 3000 K/s. Whereas the ultimate yield is achieved during the heating period to 1300 K, the yield after heatup to 725 K at this heating rate is very small. The case for 925 K includes substantial decomposition during heatup and also during the isothermal reaction period. During isothermal devolatilization at both of the lower temperatures, the nominal rates decrease by almost two orders of magnitude. Although the rates are erratic throughout, they tend to diminish continuously as reaction times are extended. Such complex behavior contrasts markedly with the uniform rates that would be observedfor isothermal devolatilization if this process abided by a single first-order rate law. Finally, the impact of pressure variations is apparent in Figure 8. These cases are for devolatilization at 0.1, 1, and 5 MPa during uniform heating at 3000 K/s for three coal types that cover the rank spectrum. For any coal type, nominal rates of devolatilization are virtually independent of pressure variations.
Discussion Any meaningful discussion of devolatilization rates must carefully distinguish the rates of product evolution from the reaction rates of the various chemical processes that underlie devolatilization, because they have virtually no discernible connection. Nominal devolatilization rates are based on the evolution of weight loss. And instantaneous weight loss is governedby several distinct chemicalreaction rates as well as the statistical probabilities underlying fragmentation and cross-linking, and the conditions for flash distillation. The interdav among these mechanisms is so complex that it is futile-to by to iifer anything about the chemical reaction rates from tar evolution or weight loss. Indeed, the nominal devolatilization rates based on FLASHCHAIN in Figures 4-8 are grossly different than the rates of all the chemical reactions used to prepare
- l . o " i " " " " ' i " " " " " " " 0.8 1 .o i .2
1.4
1.6
1.6
1ooorr Figure 8. Nominal devolatilization rates from three coals for transient devolatil&ationduring heatup at looo K,s at o,l (solid curves), 0.5 (dashed curves), and 5.0 MPa (dotted curves).
these simulations, conclusively demonstrating just how unrelated all these rate parameters are. In the literature, disparities among activation energies assigned for chemical reaction rates and those for the evolution rates of devolatilization products have been regarded as indications that mass or heat transfer mediates devolatilization mechanisms. These arguments recognize that nominal devolatilization rates have activation energies that are far lower than the bond dissociation energies of familiar organicfunctionalities in thermal decompositions. But roles for transport phenomena, either heat or mass, are definitively contradicted by the absence of a particle size dependence in any aspect of devolatilization behavior of coal in the pulverized fuel (pf) size grade, because none of the transport mechanisms that have been proposed proceed without radial gradients of either temperature or species concentrations.16 (Conversely, transport resistances do become evident as a size dependences for much larger parti~1es.l~) Moreover, the nominal devolatilization rates based on FLASHCHAIN predictions in this paper conclusively demonstrate that heat and mass transfer limitations have nothing to do with the low apparent activation energies in nominal devolatilization rates. Indeed, the mechanism behind the simulations is completely free of all heat and mass transfer considerations. Another source of confusion has been the way that the pseudofrequency factors in nominal rate constants are functions of heating rate and reaction time at constant temperature, especially when rate constants are used to assess consistency among different data sets. It is wellknown that rate constants assigned to data sets from different laboratories cover 3-4 orders of magnitude in similar regions of an Arrhenius diagram. This situation has brought numerous experimental techniques under closer scrutiny, which has already uncovered legitimate problems with transient thermometry on wire gridsS4 Similarly, the results in this paper may motivate better quenching schemes or, a t the very least, better characterizations of cooling rates. But most of the conjecture surrounding the scatter on Arrhenius plots3**J7does not properly account for the genuine variability of nominal devolatilization rates for different heating rates or reaction (14) Bautista, J. R.; Russel, W. B.; Saville, D. A. Znd. Eng. Chem. Fundam. 1 9 8 6 , ~536. , (15) Wagner, R.; Wanzl, W., van Heek, K. H. Fuel 1985,64, 571. (16) Niksa, S. MChE J. 1988, 34, 790. (17) Solomon, P. R.; Hamblen, D. C. Prog. Energy Combust. Sci.1983, 9,323.
Flashchain Theory times. Figure 6b is the most extreme example, because coal devolatilization data for diverse heating rates cannot possibly define a single pair of Arrhenius parameters. Defining nominal rates from data correlations with FLASHCHAIN demonstrates further that pseudofrequency factors change in proportion to variations in the heating rate. And the results in this paper reaffirm Howard et ala's interpretation of the ways that different thermal histories affect nominal rate constants for devolatilization, and reinforce their precautions.18 Nominal rates should never be directly compared to assess consistency among reactors that impose different thermal histories, or even at different instants among cases that have the same thermal history. In this paper, the heating rate dependence of nominal rates and the inevitability of low apparent activation energies are associated with FLASHCHAIN predictions. A complementary analysis based on the distributed activation energy model (DAEM) introduces a graphical technique that also shows that a new pair of apparent rate parameters is required to represent devolatilization during every different thermal history.lg This analysis also offers an expedient rationale for faster devolatilization rates at any particular temperature as the heating rate is increased. According to the DAEM, volatiles with the lowest activation energies are liberated first. At faster heating rates, more volatile precursors are retained at higher temperatures, including a higher proportion of those associated with lower activation energies. Consequently,the reactant appears to decompose faster, and the overall devolatilization rate at that temperature seems faster. In other words, both the amount and the thermal response of the remaining reactant change as the heating rate is varied. In light of the diversity of chemical functionalities in the bridges that bind aromatic nuclei together in coal, the dissociation energies of bridges must span an enormous range, so the heating rate dependence of coal devolatilization rates is plausible. Diverse dissociation energies were suggested by Gavalas et al. from basic chemical considerations20y21and corroborated by satisfactory data correlations of transient yields for different heating rates based on the DAEM.lg Nevertheless, the modalities and extents of the distribution of decomposition energies of the actual bridges in different coals cannot be measured, so these aspects of the energy distributions in pyrolysis models will remain hypothetical. This subjective aspect is the basis for a fundamental distinction among the available devolatilization models. In both the FG-DVC and CPD models, very narrow (18) Howard, J.B.;Fong,W.S.;Peters,W.A.ProceedingsoftheNAT0 Workshop on "Fundamentals of the Physical-Chemistry of Pulverized Coal Combustion";Lahaye, J.,Prado,G.,Eds.;MartinusNijhoff:Boston, 198'7: -__.,n 77. ... (19) Niksa, S.; Lau, C.-W. Combust. Flame 1993, 94,293. (20) Gavalas,G.R.;Cheong,P.H.-K.;Jain,R.Znd.Eng. Chem. Fundam. 1981, 20,113. (21) Niksa, S.;Kerstein, A. R. Combust. Flame 1986, 66, 95.
Energy & Fuels, Vol. 8, No. 3, 1994 679 distributions of activation energies centered on the values for ethylene bridge scission are implemented, based on erroneous connections between nominal devolatilization rates and model compound decompositions. In contrast, FLASHCHAIN theory emphasizes the diversity in the chemical constitution of bridges by using broad energy distributions, and introduces this feature as the essence of the rank dependence of devolatilization rates. The abundance of oxygen in the bridges of low rank coals accelerates their conversion rates, which has two ramifications: First, gases are expelled rapidly at low temperatures and, second, extensive cross-linking inhibits the production of tar precursors. In contrast, bridges in low-volatility coals have very little oxygen, so they decompose at relatively high temperatures at significantly slower rates. The transition between these two limiting cases is a sharp one, occurring in the high-volatile bituminous rank. Consequently for hv bituminous coals, small differences in the oxygen content cause appreciable differences in the rates and yields, compoundingthe acute sensitivity of the labile bridge fraction to carbon content seen for this rank (in Figure 3 of part 4). The data evaluations in this paper corroborate this emphasis on very diverse bridge conversion chemistry in so far as the accuracy of the predicted dynamic behavior for any coal type complements FLASHCHAIN'S performance with ultimate yields in part 4. Taken together, these findings convey a coherent survey of the ways that coals' chemical constitution affects product evolution rates and yields at any stage of devolatilization.
Conclusions 1. With FLASHCHAIN, ultimate analyses are the only sample-specific information needed to predict transient weight loss and tar yields for coals across the rank spectrum, based largely on the relations among the rate parameters for bridge conversion and (O/C)Band (O/H)B. 2. Nominal devolatilization rates based on FLASHCHAIN have very low apparent activation energies, proving that the interplay among chemical kinetics, the statistics for depolymerization and cross-linking, and flash distillation is responsible, not mass or heat transport limitations. 3. Rate variations due to coal rank are modest, being well within an order of magnitude for most coal types. Low-volatility coals are more resistant to thermal decomposition than other lower ranks. 4. Rates vary with reaction time during isothermal devolatilization and, especially, for different heating rates. Single lines on Arrhenius diagrams for different heating rates are figments, because pseudofrequency factors change in proportion to changes in heating rate whereas apparent activation energies remain the same. 5. Nominal devolatilization rates are independent of pressure.
