Energy 6 Fuels 1991,5, 673-683 In addition to the correlations of yields and product characteristics, this evaluation spawns two distinct interpretations of phenomena in the condensed phase. The NRC case utilizes a streamlined version of the theory in which charring occurs only by spontaneous condensation, not bimolecular recombination. It entails an incomplete degradation of the coal macromolecule, in that the fraction of the original aromatic nuclei which become susceptible to tar formation at any stage of the process is only about one-half, independent of thermal history. In this latter respect, this scheme is reminiscent of the two-component hypothesis,29which proposed that the organic material in coal can be regarded as a binary system of inert, graphitic material and a separate lump of precursors to volatile matter. The basic problem with this approach is that the amount of potential tar precursors is fixed for all conditions, so metaplast must remain in fully devolatilized cham if observed heating rate and pressure effects on the tar yields are reproduced. Char is the only ultimate repository for the small fragments in the model simulations; in contrast, no residual extractable material has been reported for fully devolatilized chars. Moreover, tar formation is not directly correlated with the metaplast reaction rate; rather, tars are expelled only as long as gases are expelled during spontaneous charring. Thus, the distinct stages of tar and gas evolution which characterize high-volatile H.; Howard, J. B. Combustion P h e n o m e ~in Coal o-Component Hypothesis of Coal Comtitution; The Pennsylvanin State University Studiea, Monograph 31: The Pennsylvania State University Press: University Park, PA. 1971.
(23)b - R . Dusts and the
673
bituminous coals cannot be reproduced with the NRC case. The WRC case is based on the complete model including bimolecular recombination. It predicts that the coal macromolecule completely disintegrates into much smaller fragments. Most, if not all, of the original aromatic nuclei enter fragments in the metaplast lump and thereby become susceptible to tar formation at some stage of devolatilization. Flash distillation of metaplast into tar competes with the reversible exchange of fragments between the metaplast and intermediate lumps via bimolecular recombination of metaplast and bridge scission of intermediate fragments. Consequently, tar evolution is inversely related to metaplast conversion, so that tars evolve only until all metaplast is converted into intermediate fragments in the ultimate solid residue. Of course, the intermediate fragments in the residue are surrogates for the nominally infinite lattice of actual char, and the predicted sizes of such fragments are meaningless. Hence, only the WRC case is consistent with the absence of extractable material from fully devolatilized chars that has been consistently reported by various investigators. As seen in part 111,the extent of disintegration of the coal macromolecule varies widely for coals of different rank and is most extensive for hv bituminous coals.
Acknowledgment. This work was supported by the Electric Power Research Institute, under their Exploratory Research Program, and also by the US. Department of Energy, under the Advanced Research and Technology Development Program administered by the Pittsburgh Energy Technology Center.
FLASHCHAIN Theory for Rapid Coal Devolatilization Kinetics. 3. Modeling the Behavior of Various Coals Stephen Niksa High Temperature Gasdynamics Laboratory, Mechanical Engineering Department, Stanford University, Stanford, California 94305 Received February 7,1991. Revised Manuscript Received June 24, 1991 In this paper, an evaluation of the theory formulated in part 1 for coals having 70-90'70 carbon quantitatively interprets these established trends: (1)weight loss is constant for coals of increasing rank through hvA bituminous, then falls sharply and becomes negligible for anthracites; (2) tar yields are nominally the same for lignites, then increase for coals of higher rank, reaching a maximum value for hvA bituminous coals; (3) tar yields diminish for medium- and low-volatile bituminous coals, although gas yields decrease even more abruptly, so the tar fraction increases monotonically for these ranks; (4) noncondensible gas yields decrease monotonically with increasing coal rank; and (5) the average molecular weights of tar decrease in proportion to the weights of the initial monomeric units in subbituminous through high-volatile bituminous coals, and continue to decrease for coals of higher rank. FLASHCHAIN also depicts the pressure dependence of yield enhancement by faster heating, for rates from 1to 3000 K/s, and of tar yields, from vacuum to 6.9 MPa, for both low- and high-rank samples. In addition to continuous trends, the model predictions also display scatter, so that the loosely banded relation between yields and coal rank is evident. In particular, for different samples of the same nominal rank, the model represents the sensitivity of tar and total yields to measured values of H/C and O/C ratios. These aspects of parametric sensitivity also have an important practical implication. Predictions based on only the ultimate analysis and linear regressions of all other required input data are as reliable as those based on the complete set of coal-specific input data.
Introduction Modeling the devolatilization behavior of various coal types progressed through its infancy stage during the past decade and advanced toward a coherent mechanistic explanation during the past three years. Statistical corre0887-0624/91/2505-0673$02.50/0
lations of data from a wide variety of coal types appeared first. For example, Neavel et al.' correlated the tar yields (1) Neavel,.R. C.; Smith, S. E.: Hippo, E. J.; Miller, R. N. h o c . Int.
conf. coal S C L 1 9 8 1 , ~
0 1991 American Chemical Society
674 Energy & Fuels, Vol. 5, No. 5, 1991
from packed beds of coal undergoing atmospheric pyrolysis and secondary cracking using only the hydrogen and sulfur contents of the parent coals. KO,Peters, and Howard2 proposed a correlating index for tar yields in terms of the numbers of labile and cross-linked bridges and the amount of abstractable hydrogen. Various approximations and autocorrelations were incorporated to obtain a final form written in terms of only the ultimate analysis. They also devised an empirical correlation for pressures from vacuum to 9 MPa. Generally, the correlated values agreed with measurements to within 5 wt % ,although heating rate and temperature effects and transients were not considered. Within the past three years, mechanistic models which represent coal as a cross-linked macromolecular solid have been put forth to rationalize coal rank effects in terms of systematic variations in initial coal structure. Data correlations from the FG-DVC model were reported for a lignite and Pittsburgh hvA bituminous sample? but the parameter adjustments are not systematic? Data correlations from the CPD model emphasize thermal history effects for a lignite and Illinois (Ill.) No. 6 and Pittsburgh (Pit.) No. 8 high-volatile (hv) bituminous samples.6*6 Among the four coal-specificparameters in this model, one was based on '3c NMR analysis and the rest were assigned from multivariable regressions of devolatilization data. As encouraging as these results are, the available modeling studies do not interpret the trends in the data base for coals across the entire rank spectrum. Indeed, several trends are established, especially since the addition of Xu and Tomita's detailed product distributions from 17 Total weight loss remains constant at roughly 50 wt % for ranks from lignite through the hv bituminous, then falls off for coals of higher rank, becoming insignificant for anthracites. Tar yields, however, are much more sensitive to coal rank. For rapid atmospheric devolatilization, they increase from 10 to 20 wt % for lignites to a maximum of 35 w t 90for hvA bituminous coals. Although tar yields diminish for coals of higher rank, the relative contribution of tar to the total yield increases monotonically for ranks from hvA bituminous to low-volatile (lv) bituminous. The yields of noncondensible gases decrease monotonically with the rank of the parent coal, even though tar yields pass through a maximum. The molecular weight distributions (MWDs) of tar appear to shift toward lower values as the rank of the parent coal is increased? although the opposite trend has also been r e p ~ r t e d . ~ This study uses FLASHCHAIN to demonstrate, for the first time, that continuous variations of only a handful of structural features in the coal underlie the dominant trends in the devolatilization behavior of coals across the rank spectrum. As explained in part 1of this series, the theory's submodel for coal's constitution and configuration uses the aroelemental analysis, carbon ):f( and hydrogen );fHC maticities, the number of aromatic carbons per cluster (i.e., monomeric unit) (AC/Cl), the solvent extract yield in pyridine (Ypy~), and the average molecular weight of (2) KO,G. H.; Peters, W. A.; Howard, J. B. Energy Fuels 1988,2,587. (3) Solomon, P. R.; Hamblen, D. G.; Carangelo, R. M.; Serio, M. A,; Deehpande, G. V. Energy Fuels 1988,2,405. (4) Solomon, P. R.; Hamblen, D. G.; Yu, 2.Z.; Serio, M. A. Fuel 1990, 69,754. (5) Grant, D. M.;Pugmire, R. J.; Fletcher, T. H.; Kerstein, A. R. Energy Fuels 1989,3, 175. (6) Fletcher, T. H.; Kerstein, A. R.; Pugmire, R. J.; Grant, D. M. Energy Fueb 1990,4,64. (7) Xu,W . 4 . ; Tomita, A. Fuel 1987, 66(6),627. (6) Freihnut, J. D.; Proecia, W. M.; Seery, D. J. Energy Fuels 1989,3, 692.
(9) Solomon, P. R.; Serio, M. A.; Deshpande, G. V.; Kroo, E. Energy Fuels 1990,4,42.
Niksa
1 0
01
0.2
0.3
oic Figure 1. A coalification diagram for all of the coal samples in this study. Ultimate analyses were reported by Xu and Tomita' (v);Freihaut et al.* (A);Vorres for the Argonne Premium Coal Samples15 ( 0 ) ;and Bautista et al." (0).These same symbols distinguish these sample subsets in all succeeding figures.
noncondensible gases (MWc). Most of this input data has been reported for the eight coals in the Argonne Premium Coal Sample Bank (APCSB), and the rest can be estimated within tolerable uncertainties. The predicted behavior of the APCSB coals is compared with reported yields and MWDs of tar and gas yields for coals having carbon contents from 65 to 90%. This examination of ultimate yields rationalizes all of the major trends cited above. The behavior for pressures from vacuum to 6.9 MPa, heating rates from 1 to 3000 K/s, temperatures to 1200 K, and reaction times from the onset of devolatilization through completion is also examined for selected samples. In addition to the continuous trends, the predicted behavior of samples having the same nominal rank illustrates the theory's response to modest variations in coal structure. The related topic of uncertainty in the input data is addressed with parametric sensitivity studies. Ultimately, correlations of various input data are found to be appropriate substitutes for direct measurements on every coal sample. The final evaluations of the theory entail a version in which only the ultimate analysis is required. Guidelines for the Data Correlations Taken together, the six laboratory studie~~A'*'~ selected for the model evaluation depict the behavior throughout the entire rank spectrum, for wide ranges of temperature, heating rate, and pressure. Wire-grid heaters in which the sample was dispersed in a layer only a few particles deep were used in all cases. In most cases, process temperatures were determined with fine-wire thermocouples and are regarded as the actual reaction temperature. Xu and Tomita circumvented thermometry by using a Curie point heater. The atmospheric pyrolysis behavior of 17 Asian and Australian coals7 and 10 American coals6 is used to elucidate trends in the yield structure, product distributions, and tar molecular weights. Data on pressure effects for five American bituminous coals were reported by Bautista et al," and Heyd.12 Heating rate effects are evident in recent studies with the APCSB ~amp1es.I~ Collectively, (10) Xu, W.-C.; Tomita, A. Fuel 1987, 66(5), 632.
(11) Bautista, J. R.;Russel, W. B.; Saville, D. A. Ind. Eng. Chem. Fundam. 1986,25, 536. (12) Heyd, L.E.Weight Loas Behavior of Coal During Rapid Pyrolysis and Hydropyrolyeis. M.S. Thesis, Department of Chemical Engineering, Princeton University, 1982. (13) Gibbins-Maltham, J.; Kandiyoti, R. Energy Fuels 1988,2, 606.
FLASHCHAIN Theory for Coal Devolatilization
sample N.D. lig Wyd. subb. Ill. No.6 Utah HVB Stock HVB Pit. No. 8 Fre. MVB POC.LVB
%C,daf
72.9 75.0 77.7 80.7 82.6 83.2 85.5 91.1
Energy & Fuels, Vol. 5, No. 5, 1991 675
Table I. Structural Data for the Argonne Premium Coal Samples O/C N/C S/C f,' "f.' AC/C1 MWYON 0.79 0.014 0.21 4X 0.54 0.24 9 277 0.86 0.013 0.18 2X 0.55 0.27 14 410 0.77 0.015 0.13 1 X 10" 0.72 0.30 15 316 0.86 0.017 0.11 2X 0.64 0.33 15 359 0.77 0.09 0.016 3X 0.75 0.35 14 275 0.76 0.08 0.017 4X 0.72 0.36 15 294 0.66 0.07 0.016 3X 0.81 0.39 18 299 0.58 0.02 0.013 2X 0.86 0.45 20 302
H/C
MWo Y p y ~ , w t96 daf
26.5 25.9 24.6 23.3 22.4 22.2 21.1 18.6
26 26 26 26 26 26 26 4.2
Table 11. Structural Model Parameters for the Argonne Premium Coal Samples sample N.D. lig. Wyd. subb. Ill. No. 6 Utah HVB Stock HVB Pit. No. 8 Fre. MVB POC.LVB
MWMON
275 407 321 349 272 299 306 313
MWA
CA
101 152 166 154 153 159 195 216
8.1 12.1 13.3 12.2 12.2 12.6 17.2 22.2
MWB/MWA MWC/MWA MWS/MWA
1.856 1.871 1.080 1.534 0.949 1.108 0.783 0.610
data from 38 different coal samples are represented. As seen in the coalification diagram for these samples in Figure 1, there are no systematic deviations among any of the major sample groupings. The scatter of the data on a coalification diagram is extremely important because the amounts of oxygen and hydrogen affect virtually all process chemistry. (On the diagram, the hydrogenation and oxidation chemistry underlying coalification itself are represented by upward and rightward displacements, respecti~e1y.l~)Likewise, devolatilization yields and product distributions from various coals display continuous trends, but with significant scatter as well (as seen below in Figure 3). At the very least, a model for coal rank effects must represent continuous trends. But one would also hope for a parametrization which distinguishes the behavior of samples having the same nominal rank, but different H/C and O/C ratios.
Input Data and Model Parameters As described in part 1, the submodels for coal constitution and macromolecular configuration use four types of structural elements-labile bridges, char links, peripheral groups, and aromatic nuclei. Coal is modeled as a mixture of linear chain fragments ranging in size from a monomer to the nominally infinite chain. Fragments are composed of identical aromatic nuclei interconnected by two different types of linkages, char links and labile bridges. Nuclei contain only aromatic carbon and all of the nitrogen. Char links are also completely aromatic. Labile bridges contain most of the aliphatic material, as well as all of the oxygen and organic sulfur. Due to the straight chain configuration imposed in this model, they are larger than the actual bridge structures in coal. The ends of the fragments necessarily contain one-half char link, and may also contain peripheral groups, which are aliphatic gas precursors. If the peripheral group is present, the chain end is identical to one-half of a labile bridge. Even though the structural elements of a given type are identical, monomeric units within fragments are not, because a monomeric unit consists of an aromatic nucleus and up to two half-linkages which can each be either of two types. Consequently, properties of the average monomeric unit are first inferred from analytical data and then evaluated from the chemical constitution of nuclei, bridges, and char links, as well as the proportions of bridges (14) van Krevelen, D. W.Coal; Coal Science and Technology 3;Elsevier: Amsterdam, 1981.
0.837 0.842 0.486 0.690 0.427 0.499 0.352 0.274
0.509 0.515 0.297 0.422 0.261 0.305 0.216 0.168
~(0)
0.911 0.911 0.911 0.911 0.911 0.911 0.911 0.970
P(0) 0.88 0.81 0.73 0.65 0.63 0.60 0.53 0.37
YB
VE
0.110 0.200 0.340 0.405 0.467 0.500 0.500 0.500
1.98 3.01 2.01 2.83 1.74 2.19 1.91 1.93
and char links in the whole coal initially. Relations expressing such definitions appear in part 1. The eight APCSB coals have undergone thorough analytical characterization, and almost all of the data needed to specify the input data to FLASHCHAIN is available. Ultimate and proximate analyses were provided by the APCSB,l6 and the 13C NMR results which specify f,' and AC/C1 were reported by Solum et al.16 The input data for the APCSB coals appear in Table I. The elemental compositions of this set display the loosely banded nature of coal rank, as well as the continuous graphitization process which underlies increasing coal rank. Nominal ranks from lignite to lv bituminous are represented. The carbon aromaticity and aromatic carbons per monomer both increase with increasing rank but, surprisingly, seem to be only loosely autocorrelated. Additional data presented below (in Figures 9 and 10) suggest a much stronger autocorrelation. The proton aromaticities are based on a linear regression of Gerstein et al.'s 'H NMR results,17 which is HfL = 1.1 X 10-2(%C,daf)- 0.554 (1) These values are roughly double those based on structural analysis,18but all model predictions are quite insensitive to this parameter. The average molecular weights of monomeric units, MWMON, are not required. They are shown only for comparison with the values from the coal submodel in Table 11, below. No rank dependence is evident in MWMow The average molecular weights of noncondensibles were evaluated from a two-part regression of values based on Xu and Tomita's product distributions8 (see Figure 5, part l), given by for %C, daf C 73.3 MWG = 26.5 (24 for %C, daf > 73.3 MWG = 59.7 - 0.451(%C,daf) (2b) Since the correlation coefficient, r, is -0.955 and the standard deviation, 6,is only 3.18, deviations from the correlated values will not be considered further. (16) Vorres, K. 5. Users Handbook for the Argonne Remium Coal Sample Program; ANAL/PCSP-89/1; October,1989; available from the National Technical Information Service. (16) Solum, M. S.; Pugmire, R. J.; Grant, D. M. Energy Fueb 1989, 3, 187. (17) Gerstein, B. C.; Murphy, P. D.; Ryan, L. M. In Coal Structure; Meyers, R. A., Ed.; Academic Prese: New York, 1982; Chapter 2. (18) Wender, I.; Heredy, L. A.; Neuworth, M. B.; Dryden, I. 0. C. In Chemistry of Coal Utilization, 2nd Suppl. Vol.; Elliot, M. A., Ed.; Wiley-Interscience: New York, 1981; Chapter 8.
676 Energy & Fuels, Vol. 5, No. 5, 1991
Niksa Table 111. Reaction Rate Parameters for all Coal Tvms reaction A factor, E,, kJ/mol bridge dissociation 2 x 10" 167 (a = 20.4) recombination 6 X 10le 217 peripheral group elimination 1 x 10'6 230 PAT(T,MW),MPa = 3 X lo' exp (-200MW'3.6/27 ~~
0
Q
1
.o
0 n P
1
1
301
$1
,
g
c 20
0
!-
"
70
\.\
75
SO
85
90
0
-I
95
Carbon Content, % daf
Figun, 2. Extract yields in pyridine versus carbon content on the dry &-free h i s . Sourcea of the various studies are reported by van Kre~elen.~'
The uncertainties surrounding the impact of rank on extract yields in pyridine are considerably larger, as seen in the data in Figure 2. For many years, the values in Figure 2 were interpreted with a maximum for coals having 86% C, as indicated by the dashed line. But this view is supported by only one of the available data sets (shown in Figure 2 with open circles); in fact, consideration of all of the data from coals with carbon contents to 88% C indicates no rank dependence whatsoever. Moreover, extract yields for all other common solvents14contradict the behavior of the pyridine extracts and indicate that yields decrease monotonically with increasing rank. To circumvent such ambiguities, the following two-part regression is implemented in FLASHCHAIN: for %C, daf < 88.1 YPYR= 26 (34 for %C, daf > 88.1 YpyR= 697.5 - 7.62(%C, daf) (3b) According to this treatment, yields are independent of rank for most coale and then decrease very sharply for mediumand low-volatile bituminous samples. The impact of such behavior and also of the considerable scatter in this data will be illustrated in parametric sensitivity studies below. The model parameters defined by the data in Table I appear in Table 11. Note especially that the molecular weights exhibit continuous variations with rank as well as significant scatter. The weights of the monomeric unit are virtually identical with those based on lacNMR because both echemee identically satisfy the ultimate analysis. Aromatic nuclei weigh considerably less than monomeric units, especially for low-rank coals. On a mass basis, such coals consist primarily of aliphatic and heteroatomic material, not aromatic material. In this model, aromatic carbon is found in both nuclei and char links, so carbon numbers of nuclei are lees than measured values of AC/Cl. Molecular weights of labile bridges are considerably larger than those of nuclei for most samples through the hv bituminous, reflecting the abundance of aliphatic material. However, the relatively large values for all coal types are consequences of the imposed straightchain configuration. Recall that all aliphatic components are relegated to bridges and peripheral groups. At most, there are only two bridges per nucleus. Peripheral groups appear only on the ends of the fragments. In these respects, the monomeric units in FLASHCHAIN are unlike the highly substituted nuclei in actual coal macromolecules. Despite the unrealistically large bridges, the stoichiometric coefficients for gas production from peripheral group elimination (and spontaneous charring) ensure reliable molar evolution rates of gases, because measured molecular weights of gases are
- - e - -
incorporated. Bridges are resolved further into one char link and two peripheral groups, using a char weight which is 45% of the bridge weight for all coals. This reduction factor was identified in the data correlations for hv bituminous coals in part 2, and is fixed for all cases in this study. A rank dependence may be added to this parameter to predict the elemental composition of the noncondensibles in a future extension of the theory. The initial fraction of intact links, p(O),is assigned by matching the pyridine extract yield (from eq 3) to the sum of the initial mass fractions of metaplast and intermediate lumps (from Figure 4 in part 1). This aspect of connectedness varies only for medium- and low-volatile samples due to the interpretation of Ypy~in Figure 2. More striking rank variations appear in the values of the labile bridge fraction, I;b(O), and of the selectivity coefficient for bridge scission, VB. P(0)decreases linearly with increasing carbon content, consistent with the view that spontaneous condensation of labile bridge into char links is an aspect of coalification as well as devolatilization. Values of vB are linearly proportional to the O/C ratio for coals having carbon contents below 83%;otherwise, they are fixed. This behavior is corroborated by two important observations: First, cross-link formation during pyrolysis has been clearly related to measured C02 evolution histories.lg Second, the likely precursor to C02, viz., carboxyclic acid functionalities, are absent in ranks of hv bituminous or higher.20 Values in Table I1 are based on the following correlations: P(0)= 2.859 - O.O27(%C, daf) (4) for %C, daf < 83% V B = 0.730 - 2.952(0/C) (5a) for %C, daf > 83% V B = 0.50 (5b) These linear relations were discovered while preparing predicted values of tar and total yields for the eight APCSB samples. As seen below, they are used to specify input values of P(0)and vB for the simulations of other coals. In addition to the coal constitution parameters, reaction rate parameters are also required. The values in Table I11 are used for all of the simulations presented in this paper, except in one instance which is noted explicitly. These values are the same as those for the WRC case in the study of hv bituminous coals in part 2. Since the value of the saturated vapor pressure of metaplast is determined largely by ita molecular weight dependence, its presumed insensitivity to coal rank is plausible. In the bridge decomposition rate, the same parameters are used for all coals. Notwithstanding, the relative rates of scission and spontaneous condensation vary for different coal types, because the selectivity coefficient, YB, varies with rank, based on eq 5. Furthermore, a rank dependence for AB and cB in the bridge dissociation rate will be proposed below. Rank-dependent rate constants for the bimolecular recombination rate and peripheral group elimination rate appear to be unnecessary at present. In the forthcoming results, the operating conditions of pressure, temperature, heating rate, and/or time were varied to match those in the experiments, while the pa~~~
(19) Suuberg, E.M.; Lee, D.; Lareen, J. W. &el 1986,64, 1668. (20) Blom, L.;Edehusen, L.; van Krevelen, D. W. Fuel 1967,,?6,136.
V
vv v Mass
.
v
A
;
A -
v 0
65
" " 1 " " 1 " " 1 " " " " ' 1 " "
70
75
80
85
90
1
Energy & Fuels, Vol. 5, No. 5, 1991 677
FLASHCHAIN Theory for Coal Devolatilization
2
201
Recombined
v1
95
Carbon Content, W YOdaf
O F " " 70
I
75
'
I
I
I '
80
'
a
I
I 85
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'
I
t
1
90
I
J
95
Carbon Content, wt % daf
Figure 3. Ultimate valuea of total (open symbols) and tar yields (filled symbols)for heat up at 3000 K/s to 1037 K or higher at 0.1 m a . The source8 of data are identified with the symbol key identified in Figure 1. Model predictions, which are connected by solid lines, are based on the properties of the APCSB coals (in Tables I, 11, and 111).
Figure 4. Predicted distributionof aromatic nuclei for the eight APCSB coals at the conditions of Figure 3. Values of initial mass fractions of nuclei (dashed), number fraction of nuclei which appeared in tar precursors at any time (solid),and number fraction of nuclei appearing in fragments which participated in bimolecular recombination at any time (dotted) are shown.
rameter sets in Table I1 were changed for the various samples. A simulation of each thermal history requires less than three minutes on a 20-MH2, 386-type personal microcomputer.
petition between their flash distillation into tar vapor versus their recombination into larger nonvolatile fragments. Primary fragmentation appears to be the limiting factor for coals from either extreme of the rank spectrum. Only one-third of the nuclei in lignites ever appears in the metaplast lump. Even though nearly all of the linkages are labile (P(0)= 0.88) and, in principle, susceptible to scission, there is ample oxygen to promote spontaneous charring rather than scission (vB = 0.110). Spontaneous charring expels gases but does not reduce the fragment size. Consequently, most of the aromatic nuclei remain in the intermediate and reactant lumps. In fact, one fourth of the nuclei are retained in the reactant lump in the ultimate char. Since metaplast fragments are scarce, recombination is inconsequential in low-rank coals. Even though there is very little oxygen in low-volatile bituminous coals, their primary fragmentation is similarly suppressed. In fact, the explanation for the conversion of only 40% of the nuclei into tar precursors for the Pocahontas (Poc.) LVB sample is the same as for lignites, with the provision that the spontaneous charring of labile bridges has occurred during coalification rather than during devolatilization. Even though half of all bridge dissociations yield smaller fragments (uB = 0.5), too few bridges appear in the initial macromolecular structure to sustain its complete disintegration during devolatilization (P(0)= 0.37). As in lignites, one-fourth of the nuclei in the lv bituminous never leave the reactant lump. For high-volatile bituminous coals, most if not all nuclei appear in tar precursors at some point, so primary fragmentation is not a restriction. Rather, tar yields are governed by the competition between vaporization and recombination, as explained in part 2. Considering the poor mobility of large aromatic fragments, it is reassuring that bimolecular recombination is predicted to be important only for those coals which become highly fluid during devolatilization. (The postulated second-order recombination process is mechanically plausible only for softening coals.) Predicted values of the number average molecular weights of the ultimate tar sample from the eight APCSB coals appear in Figure 5. The predictions are bounded by measured values based on two independent calibrations of gel permeation chromatography in tetrahydrofuran? Although the experimental values are too disparate to be quantitatively useful, they do show a consistent trend of lighter tar from higher rank coals, which this theory ra-
Results Behavior of the APCSB Coals. The predicted product distributions are evaluated against the data for various coals in Figure 3. Simulation results are connected by the solid lines. They are based on the parameters for the eight APCSB coals and a heating rate of 3000 K/s to 1037 K a t 0.1 MPa, as in Xu and Tomita's experiments. The higher temperature (1275 K) and lower heating rate (lo3 K/s) in Freihaut et al.'s experiments have negligible impact on ultimate yields. All data in Figure 3 depict ultimate yields for rapid devolatilization at atmospheric pressure. FLASHCHAIN correctly displays the following continuous trends: (1)weight loss is nearly constant for coals of increasing rank through hvA bituminous and then falls sharply and becomes negligible for anthracites; (2) tar yields are nominally the same for lignites and then increase for coals of higher rank, reaching a maximum value for hvA bituminous coals; (3) tar yields diminish for medium- and low-volatile bituminous coals, although the tar fraction increases monotonically for these ranks; and (4) noncondensible gas yields decrease monotonically with increasing coal rank. All of these trends are within the range of measured values a t each carbon content, except that the predicted maximum tar yield of 40 w t 90 is higher than any reported tar yield for atmospheric devolatilization. Lower total and tar yields for the lv bituminous sample would also improve the quantitative agreement, as demonstrated in Figure l l d , below. The mechanisms underlying these trends are apparent in the associated distributions of aromatic nuclei in Figure 4. Predicted values of initial mass fractions of nuclei, cumulative number fractions of nuclei that appeared in tar precursors, and number fractions of nuclei that participated in bimolecular recombination at any stage of devolatilization are shown. The initial mass fractions of nuclei increase monotonically with increasing rank, reflecting the monotonic decrease in the labile bridge fraction (cf. values of P(0)in Table 11). Since labile bridges are the major precursors to noncondensibles, gas yields monotonically decrease with increasing coal rank. Tar yields are determined by the disintegration of the coal into metaplast fragments and the subsequent com-
a
c
4
400
-----n
1------.
t
E
z'
100 70
75
80
85
90
j
95
Carbon Content, % daf
Figure 5. Comparison among the number average molecular weight of the total tar sample from the conditions in Figure 3 based on FLASHCHAIN for the eight APCSB coals (01, and measured values for other coals based on gel permeation chroand model matography in tetrahydrofuran with polystyrene (0) compound).( calibrations.8 tionalizes. The extremely high values predicted for lignite tars reflect the suppressed fragmentation discussed above. Virtually no monomers or dimers are formed, so the average weight of tar must be high. For all other coals, the predicted rank effect is much weaker and in better accord with the trends in both data sets. For ranks from subbituminous through hvA bituminous, the tar weight correlates with the molecular weight of the initial monomeric unit in the coal. This is consistent with the predominance of tar evolution during the initial stages of devolatilization for these coals. Early tars are composed of monomers having intact labile bridges, because they are released before spontaneous charring has occurred. The converse behavior is expected for tars from higher rank samples. As seen later in Figure 8, tars are released from such coals during the later stages, so most of their linkages have been charred before they were freed from the coal matrix. Charring reduces the average weight of the monomeric units. Consequently, values of MWMoNfor the Fre. MVB and Poc. LVB are larger than for the Stock HVB and Pit. No. 8, but their tar M, values are smaller. The Impact of Operating Conditions. Broad domains of all operating conditions were examined for hv bituminous coals in part 2. In this section the predicted impact of heating rate and pressure are evaluated for coals from the ends of the rank spectrum. The heating rate study for hv bituminous coals revealed a tendency for overprediction, insofar as the predicted yield enhancement was 12 wt ?% for heating rates from 1 to lo3 K/s versus measured values of 6 and 9 wt 9% for two diferent samples (see Figure 7,part 2). No such flaws are seen in the comparison for the Wyd. subbituminous sample in Figure 6. The predicted enhancement for this sample is only half that for the hvA bituminous coal, consistent with the observed value. For the POC.LVB sample, an enhancement of 5 wt % is predicted versus roughly 3 wt ?% observed. Such small changes are too close to experimental uncertainty to be very meaningful. Most importantly, FLASHCHAIN does rationalize the suppression of yield enhancements by faster heating for both low- and high-rank samples. As elaborated in part 2,tar yields are enhanced by faster heating because the partitioning between tar and metaplast occurs at higher temperatures. Since so few tar precursors are generated from both lowand high-rank coals, less substantial enhancements occur as the heating rate is increased. In part 2, all aspects of pressure effects for hv bituminous coals were attributed to the coupling between the
Figure 6. An evaluation of the predicted impact of fester heating on the ultimate weight loss at 1.2 MPa from Wyd. Sub. and Poc. LVB coals from the APCSB, using the data of Gibbons-Maltham and Kandi~0ti.l~
20
i
10 0
0
20
40
60
Pressure, Atm
Figure 7. An evaluation of predicted ultimate weight loss from subbituminous and Iv bituminous samples after heating at l@ K/s to 1025 K for various pressures. The uppermost dashed and solid curves are predictions based on input parameters for the APCSB Wyd. sub. and ILL No. 6 coals, respectively; the lower curve is for the Poc. LVB. Subbituminous and lv bituminous data were reported by Heyd12and Bautista,12 respectively.
continuous scission of metaplast fragments and the competition between flash distillation and bimolecular recombination. These mechanisms are also fundamental to the impact of pressure on any type of coal, as evident from the evaluations for subbituminous and low-volatile bituminous coals in Figure 7. Note that these data were not acquired for APCSB samples. The Ill. No. 6 in Heyd's studies12has an ultimate analysis which is bracketed by those for the Wyodak and Ill. No. 6 in the APCSB, so both sets of predictions are shown; respective values of its H/C and O/C are 0.88 and 0.15. Ita behavior should be more similar to the subbituminous sample, based on the characteristics of the APCSB samples in Table I. Such is the case in Figure 7 in which the predicted yields for the subbituminous are within the experimental uncertainty for pressures from vacuum through 7 MPa. More data is needed to evaluate the impact of pressure on low-volatility coals, although the predictions agree with available measurements in Figure 7. According to FLASHCHAIN, pressure effects diminish for extreme coal ranks because relatively few tar precursors are generated, so the competition between flash distillation and recombination is less consequential. The predicted transient yields from four APCSB samples appear in Figure 8 for heatup at 3000 K/s to 1037 at O.1MPa. The transients for the four omitted cases all fall within the behavior of the lignite and Ill. No. 6. No data
Energy & Fuels, Vol. 5, No. 5, 1991 679 1 " " 1 " ' 1 " " 1 " '
50 LIG
I
0 . 5 1 , ,
600
700
800
900
1000
I 70
,
,
is shown because too few studies are available to establish quantitative reliability, although the relatively fast devolatilization of low-rank coals has been established.21 Certainly, the predicted trend is correct. Lignites devolatilize faster than any other coal type. But the predicted reduction in devolatilization rate for coals of increasing rank is significant only for low volatile coals. What is most remarkable is that all predictions in Figure 8 are based on identical reaction rate constants (given in Table 111). Hence, these predictions reveal for the first time yet another influence of macromolecular configuration in coal's devolatilization behavior. Lignites devolatilize faster than any other coal because most of its linkages are labile bridges, so precursors to noncondensibles are plentiful. As they are converted by spontaneous condensation into char links, a very strong flux of noncondensibles is established to sweep tar away. And because oxygen is abundant, most bridge decompositions are spontaneous charring reactions which generate gas (ve = 0.110). But charring suppresses fragmentation, so the smallest fragments present in the coal initially are the primary source of tar precursors. Consequently,tar formation begins early but is soon exhausted by the lack of new tar precursors. In contrast, for high-rank coals, few linkages in the coal are labile bridges, so gas precursors are sparse. And spontaneous charring is not any more likely than scission, which does not directly expel gases. Also, most of the potential linkages in the coal are intact initially (p(0) = 0.97),so there are relatively few small fragments in the coal. Moreover, so many char links are incorporated into the coal structure that proportionately more bridges must dissociate before smaller fragments appear. All of these factors account for the relatively slow devolatilization of low volatile coals. While the results in Figure 8 reveal these novel aspects, they do not guarantee that reaction rate parameters are independent of coal rank. To the contrary, an argument for systematic rank variations in the parameters in the bridge decomposition will be presented below. Extrapolations for Any Coal Sample. Results in the previous two sections establish that FLASHCHAIN represents coal's constitution and configuration sufficiently well to depict all observed trends in the ultimate weight loss, tar yields, and tar weights from coals across the rank spectrum. The performance for wide ranges of heating rate and pressure is also satisfactory. However, the reliance (21)Howard, J. B. In Chemistry of Coal Utilization, 2nd Suppl. Vol.; Elliot, M. A., Ed.; Wiley-Interscience: New York, 1981; Chapter 12.
,
,
,
,
,
, , , $ 100
90
80
Carbon Content, % daf
Temperature, K
Figure. 8. Instantaneousweight loss during heat up at 3000 K/s to 1037 K at 0.1 MPa for selected APCSB samples which are, in descending order, the lignite, Ill. No. 6, Fre. MVB, and POC.LVB. The behavior of the four other APCSB samples is bounded by the curves for the lignite and Ill. No. 6.
,
Figure 9. Carbon aromaticities from l9C NMR analysis of the APCSB samples (e),from Solum et al.,'6 and two sets of American coals (0, o),from Gerstein et ale1' The solid line is the regression of all data points, according to eq 6. The dashed and dotted lines are for samplfs from Australia22and the Upper Kittanning seam.22.23 25
I
I
/ 1
I
,
l
, , , ,
I
,
,
, ,
0
5 " ~ " " " " ' ~ " " 70 80
90
100
Carbon Content, % daf
Figure 10. The avera e number of aromatic carbons per monomeric unit based on f C NMR analysis of the APCSB samples (e),from Solum et al.,16 and structural analysis of various coals, reported by van Krevelenl' (0). on 13C NMR data is unwieldy. In this section values of f,' and AC/C1 for the APCSB samples are supplemented with data in the literature, to form populations for regressions like those for HfL and Y p n , given above. While more convenient, regression values also entail the liability that the loosely banded nature of coal constitution will not be accounted for. Such concerns are addressed with parametric sensitivity studies, as is the related issue of the predicted behavior for different samples of the same nominal rank. Carbon aromaticities for a broad range of coal rank appear in Figure 9. Values for the APCSB samples appear with the results of two studies reported by Gerstein et al." The linear regression of this data is described by f,' = 1.59 X 10-2(%C,daf) - 0.564 (6) where r = 0.896 and u = 0.1. In Figure 9, eq 6 appears as the solid line. The additional dotted and dashed regression lines are for samples from Australiazz and the Upper Kittanning seam.22123 Unfortunately they depend on carbon content on the dry mineral matter free basis, and the information needed to convert to the daf basis is unavailable. This explains why all three lines lie below eq 6. With this caveat in mind, all four correlations of the (22) Wilson, M. A.; Pugmire, R. J.; Karae, J.; Alemany, L. B.; Woolfender, W. R.; Grant, D.M.; Given, P. H. Anal. Chem. 1984, 56, 933. (23) Havens, J. R.; Koenig, J. G.; Kuehn, D.; Rhoads, C.; Davis,A,; Painter, P. C. Fuel 1983, 62, 936.
680 Energy & Fuels, Vol. 5, No.5, 1991
Niksa
Table IV. Input Parameters and Ultimate Yields for Selected Sensitivity Studies sample base casea
MWMON
299 304 296
MWA
CA
MWB/MWA
MWc/ MWA
MWs/MWA
do)
159 183 141
12.6 14.4 11.0
1.108 0.822 1.370 1.042 1.087 0.746 1.851
0.499 0.370 0.616 0.469 0.489 0.336 0.833
0.305 0.226 0.377 0.287 0.299 0.205 0.509
0.911
179 130 323 293
158 181
14.1 10.1 12.3 14.3
1.203 0.820
0.541 0.369
YPm = 26 w t % and f.' rank dependence in Figure 9 are probably within the experimental scatter. Similarly, in Figure 10, values of AC/C1 for the APCSB samples are supplemented with estimates based on structural analysis,14to form the population for the following regression AC/C1 = 0.373(%C, daf) - 15.09 (7) where r = 0.913 and u = 2.75. Among the data for the eight APCSB samples, the value for lignite departs somewhat from the proportionality between AC/C1 and carbon content. But values inferred from structural analysis are linearly proportional to carbon content, except for lowvolatile samples. This more definite proportionality also suggests that values off,' and AC/Cl are autocorrelated, although no autocorrelation is seen in Solum et al.'s data, perhaps due to experimental uncertainties. An autocorrelation is implicit in eqs 6 and 7, as expected, and will be implemented in the subsequent data comparisons. The correlations in eqs 1-7 supply all of FLASHCHAINS input data except for the ultimate analysis. As a step toward such a convenient formulation, consider first the parametric sensitivity results in Table IV to establish which regressions will most likely suppress the distinctive character of individual coal samples. The first nine entries examine variations in the four most important pieces of input data. Whenever possible, domains for each parameter were taken as the scatter in its data base. The reference case is based on the properties of the Pit. No. 8 in the APCSB. Blank entries in the other conditions indicate reference values. Product yields are ultimate values for rapid atmospheric devolatilization. Carbon aromaticity is the only parameter which simultaneously increases both tar and gas yields and the weight of tar. In the parametric cases for f,', average monomer weight is fixed at the reference value, as it should be, because the autocorrelation between f,' and AC/C1 was built in, as mentioned above. A higher value off,' necessarily entails a larger nucleus, but the lighter weight of labile bridges accounts for the reduced weight of tar. Similarly, since there are fewer precursors to gas, the flux to sweep tar away is diminished, so both gas and tar yields fall. Conversely, lowering f,' increases yields, but only by half as much as the decrease caused by commensurate increases off,'. This is because heavier bridges and peripheral groups are disproportionately abundant among tar precursors. Also, any factor which increases the weight of metaplast fragments inhibits their vaporization. In the next case, the large uncertainties in Ypm in Figure 2 show up as significant variations in p ( 0 ) in Table IV. There are also modest changes in the molecular weight ratios because of the stipulation that all fragment ends are terminated by peripheral groups initially. But total yields are affected by only several percent and tar yields by only OAdditional input values for the base case are
0.331 0.226
p(0) 0.60
YB
Wo
W,
MN
0.50
16.1 14.1 17.7 15.4 16.1 15.0 16.7 15.1 17.2 20 12.4
33.0 29.3 34.6 36.5 30.6 42.1 23.0 38.4 27.2 32.6 26.9
328 274 372 323 326 280 420 322 334 334 283
0.888 0.933 0.80 0.40 0.74 0.50
0.60 0.40 0.44
= 0.72.
lo%, at most. Tar weights are unchanged because the constitution parameters are insensitive to variations in
YPYR.
No analytical method is available to assign values of P(0)and vB, so their domains in Table IV were assigned arbitrarily. Actual values of P(0)would probably be scattered, like all other structural parameters; the 33% perturbation considered here is as large as in YPm,which has the worst scatter of any of the input parameters. Gas yields are very insensitive to P(0).But tar yields change in almost direct proportion to the variations in P(0); e.g., yields change by 25% for 33% perturbations in P(0).Tar molecular weights are nearly as sensitive. The selectivity coefficient U B is really not a measurable quantity, so its perturbation was set arbitrarily at 20%. As expected, relatively more scissions yield more complete fragmentation, hence a higher yield of a slightly lighter tar. Gas yields change by only 6%. The spread of values of tar and gas yields for similar coals in Figure 3 provides an appropriate perspective for these results. Yields from coals having carbon contents from 75 to 85% show the largest variations, of up to 15 wt % for both total and tar yields. Scatter in the tar yields is somewhat less if the data from any one laboratory is considered. Even so, predicted yields based on the ranges of values of ti, AC/Cl, and Ypm.in their respective banded populations do not produce this much scatter. The sensitivities for P(0)and % are greater, but there is no means to introduce scatter into these parameters. Of course, it would be especially convenient if the scatter among the measured yields was largely determined by the ultimate analysis, as is the band on a coalification diagram. This supposition is examined with the last two entries in Table IV. These results are for one hypothetical sample and one actual sample having extreme values of carbon and oxygen content among all hv butuminous coals considered in part 2. Except for the ultimate analyses, all input parameters were evaluated from the correlations in eqs 1-7. Hypothetical sample FNG has the lowest carbon content (77.7%) paired with the highest oxygen content (10.5%) and average values for hydrogen and nitrogen; sulfur was adjusted for closure. The actual coal sample BRC in Table I of part 2 contains the most carbon and least oxygen, so such adjustments were unnecessary. For the FNG case, the gas yield is higher by 25% than the base case, but the tar yield and molecular weight are unchanged. For the BRC case, the tar and gas yields fall by 20%. It is interesting that the predicted ultimate tar yield for the BRC case at 0.164 MPa is 24 wt % versus 29.3 wt % for the APCSB Pit. No. 8 case. This reduction does happen to match the prediction to the measured values (cf. Figure 2, part 21, but perhaps only by coincidence. Also, the total weight loss varied from 52.6 w t % for the FNG case to 39.3 wt % for the BRC case, covering the observed scatter. The
Energy & Fuels, Vol. 5, No. 5, 1991 681
FLASHCHAIN Theory for Coal Deuolatilization
601
600
800
"
"
I
"
"
'-
I 0
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1200
Temperature, K
Temperature, K I
io00
800
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1000
1000
Temperature, K
1200
4
0
,
,
,
,
,
,
,
,
,
I
,
,
1
,
J
-
i
600
800
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Temperature, K
Figure 11. An evaluation of predictions based on the regression values of input parameters from e s 1-7 and the ultimate analysis against measured total ( 0 )and tar ( 0 )yields for 4 s reaction after heat up at 3000 K/s at 0.1 MPa.*8 The respective names, carbon content, and O/C ratios of the four coals for cases a-d are South Beulah/71.8(0.200, Wandoan/78.5/0.140 Lidde11/83.5/0.076; and Keystone/89.4/0.026. The dotted lines in case d are based on AB = 2 X loe s- and UB = 6.4 kJ/mol, and &O) = 0.25; otherwise, all
reaction rate parameters have the values in Table 111.
spread in predicted tar yields is also satisfactory, falling from 33 wt % for the base case to 26.9 wt % for the BRC case. In summary, there are two ways that yields b a d on this theory will exhibit the loosely banded relation to coal rank that i s observed. First, scatter in the measured input values of I,', AC/Cl, and Ypmalso shows up in the predictions, but with low sensitivity. Second, H/C and O/C ratios inferred from measured ultimate analyses perturb the ratios of the molecular weights of structural elements, as seen in Table IV for cases FNG and BRC. These perturbations do change tar and total yields as much as the scatter in the data in Figure 3. In principle, predictions from FLASHCHAIN based only on the ultimate analysis accurately represents the loosely banded relation among yields and coal rank. Of course, its quantitative performance remains to be established in this respect. Data from a single experiment with a suite of similar coal samples is essential, yet currently unavailable. The final evaluation of the theory's performance examines predictions for a broad range of coal rank based only on the ultimate analyses. Data and predictions in Figure lla-d represents yields for 4 s following heat-up at 3000 K/s at 1 MPa. As fully described by Xu and Tomita,lo these four coals represent carbon contents from 71.8 to 89.4% and O/C ratios from 0.200 to 0.026. Predicted tar and gas yields for both low-rank coals are almost within the experimental uncertainty throughout. The only flaws are underpredicted tar yields for temperatures from 750 to 850 K,and underpredicted total yields at the highest temperature. This latter flaw is debatable, in that 4-s exposure to 1200 K is severe enough for significant product
evolution from relatively slow secondary chemistry, particularly graphitization reactions which yield methane and hydrogen, and the final stage of carbon monoxide evolution. FLASHCHAIN only depicts primary devolatilization at present. The predictions for both high-rank coals are not particularly accurate, but informative nonetheless. In Figure l l c for the Liddell coal, the predicted total yield for the highest temperature is reasonable, but the tar yield is overpredicted by more than 50%. Considering that the carbon content and O/C ratio for the Liddell coal are identical with those for the Pit. No. 8 in the APCSB, and that the rate parameters in Table 111are based on yields from the latter coal, this discrepancy can only be attributed to anomalous values of the other structural parameters. More importantly, in Figure l l c , total and tar yields are overpredicted at all temperatures. These same tendencies are amplified in Figure l l d in the predictions for the Keystone LVB coal, i.e., total and tar yields at the highest temperature are significantly overpredicted, and the predicted temperature range for devolatilization is too low. These two flaws are each associated with specific sets of parameters in the theory. Problems with the total and tar yields at the highest temperatures must be due to incorrect values for the structural parameters (listed in Table 11). Furthermore, changing the value of P(0)from its regression value from eq 4 of 0.415 to 0.25 is the only reasonable way to match the predicted and observed yields for the Keystone coal. A temperature range for devolatilization that is too low indicates that the parameters in the bridge decomposition rate should be adjusted to slow rates (either by lowering A B or raising EB). Note also in
682 Energy & Fuels, Vol. 5, No. 5, 1991
Figure l l d that the predicted slope is lower than for a correlation of the data points, so the standard deviation about the mean activation energy is also too large. The satisfactory fits in Figure l l d were obtained by reducing A B by 2 orders of magnitude from its base value and by lowering uB from 20.4 to 6.2 kJ/mol. (This is the only simulation in this paper which is not based on the rate constants in Table 111.) Future evaluations will determine if such adjustments are, in fact, the first indications of continuous rank dependences in the bridge decomposition rate. Indeed, they do have a reasonable physical interpretation. Large values of UB for lower coal ranks could indicate a wide range of decomposition rates, as expected for bridges that have many heteroatomic functionalities and aliphatic hydrocarbon chains. Conversely, the bridge structures in high-rank coals are much less diverse, hence U B should be smaller. Their lower decomposition rates, hence lower values of AB, are consistent with the view that coalification is a graphitization and that partially charred bridges are more thermally stable. Summary All influences on rapid coal devolatilizationtemperature, heating rate, time, pressure, particle size, and coal type-can be understood in terms of only four mechanisms: (1)the coal macromolecule disintegrates into fragments that are widely distributed in size; (2) a phase equilibrium establishes the mole fraction of tar fragments in a gas stream being convected out of the particle with no transport resistance (the flash distillation analogy); (3) reactions in the condensed phase convert labile bridges into char links and simultaneously expel noncondensible gases, establishing the convective flow and inhibiting the formation of tar precursors; and (4) recombination reactions in the condensed phase incorporate tar precursors into char. These mechanisms are fundamental to the devolatilization of any coal type, although their relative impact is strongly affected by variations in the characteristics of the parent coal. In lignites, most linkages are labile bridges, but, due to the cross-linking propensity of oxygen functionalities, most decompose into char links. This process expels noncondensible gases but does not fragment the coal macromolecule into tar precursors, so most of the aromatic nuclei in lignites remain associated with the coal lattice. These processes in the condensed phase are apparent in several aspects of product evolution. Yields of noncondensibles are maximized because most of the coal mass is aliphatic or heteroatomic matter, which decomposes into gases. Tar formation occurs during the early stages for two reasons: First, there are tar precursors present in the coal initially and, second, the spontaneous charring chemistry establishes a strong flux of noncondensibles to sweep tar away. However, tar yields are low because too few bridge scissions occur to replenish the tar precursors present initially. Even though there is very little oxygen in the highest rank coals, their primary fragmentation is similarly hindered. The only difference is that spontaneous charring of their labile bridges has occurred during coalification rather than during devolatilization. The absence of oxygen does mean that relatively many bridge decompositions are scissions, which generate smaller fragments. But there are too few labile bridges in the initial macromolecular structure to sustain its thorough disintegration during devolatilization. As in lignites, most nuclei remain associated with the coal lattice and never appear in tar precursors. Gas yields are low because heteroatoms and aliphatics are scarce initially. Tar yields are low because these coals are largely graphitic. Most of the potential
Niksa
linkages in the macromolecular lattice are intact initially, so there are relatively few tar precursors. Consequently, relatively many bridges in the coal structure must dissociate before tar precursors appear, so tar formation is delayed. For the first time, the observation that lowvolatile coals devolatilize slowly has been recognized as another influence of macromolecular configuration in coal's devolatilization behavior. For high-volatile bituminous coals, most if not all nuclei appear in tar precursors at some point, so primary fragmentation is not a restriction. Rather, tar yields are governed by the competition between flash distillation of precursors into tar vapor and the recombination of precursors into larger, nonvolatile fragments. It is reassuring that bimolecular recombination is predicted to be important only for those coals which become most fluid during devolatilization. Only for those coals is the postulated second-order recombination process mechanistically plausible. First and foremost, predicting the devolatilization behavior of any coal type is a matter of distinguishing aliphatic, heteroatomic, and aromatic constituents. In its submodel for coal constitution, FLASHCHAIN introduces the segregation of all oxygen and aliphatics into labile bridges and peripheral groups, and all aromatics and nitrogen into char links and aromatic nuclei. This crucial partitioning is implemented in balances based on the ultimate analysis, carbon aromaticity, and aromatic carbon number per monomeric unit. (Proton aromaticity is also included but found to be inconsequential.) These parametrizations and the theory's three reaction mechanisms quantitatively interpret, for the first time, all trends in the total weight loss and yields and molecular weights of tar from coals across the rank spectrum: (1) weight loss is constant for coals of increasing rank through hvA bituminous and then falls sharply and becomes negligible for anthracites; (2) tar yields are nominally the same for lignites and then increase for coals of higher rank, reaching a maximum value for hvA bituminous coals; (3) tar yields diminish for medium- and low-volatile bituminous coals, although gas yields fall even more abruptly, so the tar fraction increases monotonically for these ranks; (4) noncondensible gas yields decrease monotonically with increasing coal rank; and (5) the average molecular weights of tar decrease in proportion to the weights of the initial monomeric units in subbituminous through high-volatile bituminous coals, and continue to decrease for coals of higher rank. FLASHCHAIN also rationalizes the suppression of yield enhancements by faster heating and of pressure effech for both low- and high-rank samples. Both of these tendencies are due to the hindered production of tar precursors discussed above. Two novel ways to make the model predictions display the observed loosely banded relation to coal rank have also been identified. Scatter in the measured input values of f;, AC/C1, and YpyRalso shows up in the predictions, but with low sensitivity. The ultimate analysis is the better source of scatter, as seen in the band on a coalification diagram. Measured H/C and O/C ratios perturb the input parameters in the model. These perturbations do scatter the tar and total yields as much as observed experimentally, although the theory's quantitative performance on a sample-by-sample basis remains to be established. These aspects of parametric sensitivity also have an important practical implication. Predictions based only on the ultimate analysis and linear regressions of all other required input data are nearly identical with those based on the complete set of coal-specific input data. This
Energy & Fuels 1991,5,683-688 streamlined version of the theory paves the way for examinations of extensive literature data for coal rank effech, now underway. Studies of rank effects in more complex situations are facilitated by FLASHCHAINs minimal computational burden, as seen in our recent work on pulverized coal c o m b ~ s t i o n . ~ ~ FLASHCHAIN should also be identified by the structural features and mechanisms which have been deliberately omitted. No functional groups appear. Based on this model's performance, it is clear that functional groups are not essential to global reaction models for devolatilization. This is most fortunate, because incorporating functional groups entails prohibitive laboratory analysis for any practical application. Hydrogen abstraction reactions or (24) Lau, C. W.; Niksa, S. Paper presented at the WSS/CI 1990 Fall Meeting, University of California, San Diego, October 1990; Paper No. 90-47.
683
"donatable" hydrogen are also absent. Undoubtedly, they do appear in the free-radical chain chemistry which actually underlies devolatilization. But no current devolatilization model represents such elementary chemical mechanisms, so their appearance in global models is not firmly grounded, either mechanistically or empirically, with respect to their impact on yields and product characteristics. Finally, there are no transport resistances in this theory, because the observed absence of a size dependence in the yields from various coals contradicta models which include them.
Acknowledgment. This work was supported by the Electric Power Research Institute, under their Exploratory Research Program, and also by the US. Department of Energy, under the Advanced Research and Technology Development Program administered by the Pittsburgh Energy Technology Center.
Study of Ca Catalysis on Carbon Gasification with l8O2 Takashi Kyotani, Shinzo Hayashi, and Akira Tomita* Institute for Chemical Reaction Science, Tohoku University, Katahira 2-1-1, Sendai, Japan 980 Received February 13, 1991. Revised Manuscript Received June 10, 1991
Ca-loaded graphite was gasified with labeled O2 and gas analysis during the O2 gasification was carried out. C02formation in the gasification was greatly enhanced by the addition of Ca. The surface oxygen compounds, C(O), formed by O2 chemisorption during the gasification were analyzed by the subsequent temperature-programmed desorption. Furthermore, the surface analysis of Ca-loaded graphite after gasification was done with secondary ion mass spectrometer. From these data, the function of Ca on the C02formation was explained as follows: (1)formation of C(0) by active oxygen from catalyst surface, CaO(O), (2) further reaction of the C(0) with active oxygen to form C(O.O), and (3) ita desorption as COP The essential point of this mechanism is that such C02formation process intensively occurs only on the sites which are in contact with the catalyst particles. Many C(0) complexes are formed over the wide area apart from the catalyst, but they are not extensively involved in the C02 formation.
Introduction It is well-known that calcium is a very active catalyst for the carbon gasification reaction, as long as the dispersion state is good.'+ There are, therefore, many studies on the mechanism of Ca catalysis.w From these works, the following conclusions were derived. First, Ca addition (1) Hippo, E. J.; Jenkins, R. G.; Walker, P. L., Jr. Fuel 1979, 58, 338-344. (2) Radovic, L. R.; Walker, P. L.,Jr.; Jenkins, R. G. J. Catal. lSBS,82, 382-394. (3) Radovic, L. R.; Walker, P. L., Jr.; Jenkins, R. G. Fuel 1984,63, 1028-1030. (4) Hengel, T. D.; Walker, P. L., Jr. Fuel 1984,63, 1214-1220. (6) Linaree-Solano, A,; Hippo, E. J.; Walker, P. L., Jr. Fuel 1986,65, 776-779. (6) Sears, J. T.; Muralidhara, H. 5.;Wen, C. Y. Ind. Eng. Chem. Process Des. Deu. 1980, 19, 366-364. (7) Kaptaijn, F.; Porre, H.; Moulijn, J. A. AIChE J. 1986,32,691-696. (8) Freund, H. Fuel 1986,65,63-66. (9) Zhang, Z.-G.; Kyotani, T.; Tomita, A. Energy Fuels 1988, 2, 679-684.
does not affect activation energy for the gasification, but it effectively increases the number of active sites. Second, a probable mechanism for O2 gasification is the oxygen transfer in which an active oxygen atom from a surface species on the catalyst, like CaO(O), reach with the carbon. However, no one has identified such species. There are still many unknowns in the study of Ca catalysis in comparison with the study of alkali-metal catalysis, and the present state is far from a full understanding. Since the oxygen complex on the carbon surface is considered to act as an intermediate of carbon gasification,lOJ1it is essential to investigate the effect of Ca addition on the formation and desorption process of such complex. From this point of view, we have recently investigated the effect of Ca addition on the dynamic behavior of the surface complex during the chemisorption (10) Laine, N. R.; Vaetola, F. J.; Walker, P. L., Jr. J. Phys. Chem. 1969, 67, 2030-2034. (11) Ahmed, S.; Back, M. H. Carbon 198b, 23, 613-524.
0887-0624/91/ 2505-0683$02.50/0 0 1991 American Chemical Society
