Energy & Fuels 1988,2,567-573
567
Comparison of Tar Evolution Rate Predictions in Coal Pyrolysis from the Multiple Independent Parallel Reaction Model and the Functional Group Model over a Wide Range of Heating Rates Glen H. KO,William A. Peters, and Jack B. Howard* Department of Chemical Engineering and Energy Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 Received December 11, 1987. Revised Manuscript Received April 29, 1988
Tar evolution rates from coal pyrolysis predicted by the multiple independent parallel reaction model and the functional group model are compared to experimental data for Pittsburgh Seam bituminous coal. Model predictions were tested over heating rates from 0.05 to 1000 "C/s and up to a maximum temperature of 1050 O C . The multiple independent parallel reaction model can reliably predict tar evolution rates over the range of heating rates covered by the data from which the rate parameters used in the model were obtained but generally not at heating rates outside this range. Thus, the range of applicability is substantial when the rate parameters are fitted from data collected at two or more widely different heating rates. For the functional group model, several sets of parameter values have been published without always showing critical comparisons against data and without providing guidance as to which values are preferred for a given set of conditions. However, regardless of which of the published sets of parameter values is used, tar evolution rates predicted from the functional group model do not generally agree well with the experimental data, especially at higher heating rates. Also large discrepancies are found between experimentally observed maximum tar yields and those predicted by the functional group model.
Introduction Tar evolution from coal pyrolysis represents a complex chemical decomposition coupled with transport. Reaction conditions such as pressure, particle size, and coal type can significantly influence maximum tar yield and the observed tar formation kinetics.' A quantitative method to predict maximum tar yield under rapid pyrolysis conditions has been presented by KOet al.273in the form of a correlation independently relating the yield to coal type and pressure. Another important factor that influences tar formation kinetics is the temperature history of the coal, which can vary widely depending upon application. Heating rates, for example, can vary from -0.1 OC/s in coking to over 10000 "C/s in pulverized coal combustion and entrained-flow gasification. A reliable method to predict tar evolution rates over a wide range of heating rates would be valuable in modeling, design, and operation of many coal conversion processes including liquefaction, gasification, and combustion. Two commonly used coal pyrolysis models, the multiple independent parallel reaction (MIPR) mode1415and the functional group (FG) model: have been used to describe tar evolution rates by using a set of simple global kinetic parameters. Under conditions where the effects of physical-transport processes and secondary reactions are relatively unimportant but not negligible, both models approximate the complex chemical decomposition and any transport effects by global first-order decomposition reactions occurring uniformly throughout the particle. In addition, both models can also be used to represent only the chemical decomposition in descriptions that explicitly include mass transfer. Whether mass transport is treated explicitly or implicitly, a successful model must be able *To whom correspondence should be addressed at Room 66-454, MIT, Cambridge, MA 02139.
0887-062~/8S/2502-0567$01.50/0
Table I. Notations Used in the Multiple Independent Parallel Reaction Model and the Functional Group Model Both Models ko = preexponential factor, s-l E = activation energy, kcal/mol Eo = mean activation energy, kcal/mol u = standard deviation of activation energy, kcal/mol subscript i refers to component i or species i Multiple Independent Parallel Reaction Model V = cumulative amount of tar evolved up to time t , wt fraction or wt % V* = yield limit of tar; V approaches V* as t becomes large Functional Group Model
X
= fraction of coal that can potentially evolve as tar X o = X at time = 0 Yi = fraction of component i present in coal at time t Y,,, = Yi at time = 0 subscripts: x = tar-forming fraction; nvc = nonvolatile carbon
to predict reliably tar evolution rates over a wide range of temperature-time histories. This paper examines the predictive capability of the two models over a wide range of heating rates by comparing the tar evolution predictions to experimental data from Pittsburgh Seam bituminous coal. The experimental (1) Howard, J. B., In Chemistry of Coal Utilization, 2nd Supplemental Volume;Elliott, M. A., Ed.; Wiley: New York, 1981;pp 665-784. (2)KO,G.H.; Peters, W. A.; Howard, J. B. Fuel 1987,66,1118-1122. (3)KO,G.H.; Peters, W. A.; Howard, J. B. Twenty-Second Symposium (International)on Combustion; The Combustion Institute: Pitts-
burgh, PA, in press.
(4) Hanbaba, P.; Jntgen, H.; Peters, W. A. Brennst.-Chem. 1968,49, 368-376. (5)Anthony, D. B.; Howard, J. B.; Hottel, H. C.; Meissner, H. P. Fifteenth Symposium (International on Combustion; The Combustion Institute: Pittsburgh, PA, 1975; pp 1303-1317. (6)Solomon,P. R.;Colket, M. B. Seventeenth Symposium (International) on Combustion;The Combustion Institute: Pittsburgh, PA, 1979; pp 131-143.
0 1988 American Chemical Society
KO et al.
568 Energy & Fuels, Vol. 2, No. 4,1988
ture histories are commonly used in regression analyses to compute the four unknown parameters: Ito, Eo, u and V*. Alternatively, V* can be determined directly from the data, as the asymptotic tar yield at high temperatures or long holding times, thereby reducing the regression analysis to three parameters. As shown in Figure 1, the FG model divides coal into a potentially tar forming fraction (X,) and a non-tar forming fraction (1 - Xo). As in the first model, first-order reactions with respect to the amount of reacting material are used in all rate equations. The fraction Xo decomposes to produce tar as well as some gas. The final yield of gas + tar is fixed but the ratio of these two products depends on the temperature history and the relative rates of tar and gas formation. The final yield of char is fixed and is equal to (1- Xo) Y,,. The rate of tar evolution is described as
r--/ / CCAL
--
VEas
/
\
GAS
\ \
'.-
X
I
I
--~-VTAR-VGAS CFAQ
[ai
d(tar)/dt = CYi(-dX/dt)
(5)
I
where [--+
Coal is s u b d i v i d e d I n t o d i f f e r e n t f r a c t i o n s by t h e node1
p r o d u c t p a t h from s u b d i v i d e d f r a c t i o n s t o p r o d u c t ]
F i g u r e 1. Reaction scheme of (a) the multiple independent parallel reaction (MIPR)model and (b) the functional group (FG) model.
conditions are limited to 1 atm of pressure and small particle sizes where mass-transport limitations are small. Therefore the models are used without explicit treatment of transport and secondary reactions, thereby lumping the small effects of these processes with the primary decomposition.
Model Description Figure 1 shows the reaction schemes for the MIPR model and the FG model. Notation for the two models is shown in Table I. In the first model, coal is assumed to decompose into tar, gas, and char, the ultimate amount of each fraction being a fixed quantity obtained from experimental data. The yield limit of tar as t is the fixed quantity V*, and the tar is formed by multiple independent parallel reactions, first order with respect to the amount of reacting material remaining in the coal. The rate of tar evolution is expressed as the sum of the contributions from all the reactions, each of which is described by dVi/dt = ki(Vj* - Vi) (1)
-
ki = ko,i exp(-Ei/Rr)
(2)
where i denotes one reaction. The same preexponential factor is used for all the reactions, Le., ko,l = ko, and the activation energies are described by a Gaussian distribution with mean Eo and standard deviation u. Thus
f ( E ) = [ ~ 7 ( 2 a ) ~ exp[-(E / ~ ] - ~ - Eo)2/2~2]
(3)
where f(E) = dV*/ V* and V* is the sum of the Vi* for all i. Note that dV* is equivalent to Vi*, assuming i is large, and the integral of f ( E )dE from E = 0 to E = is unity. For any temperature history the cumulative yield of tar up to time t, i.e., V, is obtained by integration of eq 1over time and summation of the results for all i. The integral form of the summation is v*-v--
V*
Cumulative tar yield data collected over known tempera-
dX/dt = kx0[exp(-E,/RT)]X
(6)
dYi/dt = kio[exp(-Ei/RT)] Yi
(7)
and The early version of the models employed a single activation energy, but a later modification? used a Gaussian distribution of activation energies. Computation of tar evolution rate or cumulative yield from eq 5-7 requires considerably more effort than does computation from the MIPR model since concentrations of all functional groups (Yi) as well as the tar forming fraction (X) must be computed simultaneously over a distribution of activation energies at every temperature interval. Also, more effort is required to evaluate the fitted parameters for tar evolution in this model than in the MIPR model because tar and gas formation reactions cannot be separated, given the competition between tar and gas formation from the Xo(l - Y,,,) fraction (see Figure 1). Time-resolved rate or cumulative yield data on all gas species as well as tar are required to compute the fitted parameters. As evident from the reaction schemes in Figure 1, neither of the two models explicitly includes the effects of transport and secondary tar-cracking reactions. Therefore, unless additional steps are added to account for tar-transport and secondary reactions, the applicability of these models is limited to low pressures and small particle sizes where the effects of these processes are negligible or small enough so as to be adequately accounted for implicitly by effective primary decomposition parameters. These constraints are adequately satisfied by the conditions under which the data and model parameters referred to in this study were obtained.
Experimental Conditions Literature data from Suuberg? Serio: and Serio et al.'O on the pyrolysis of Pittsbuqh Seam bituminous coal under the conditions summarized in Table I1 were used for the assessment of model predictions in this study. The data include time-resolved tar evolution measurements and temperature-time histories of the coal. Of special interest here is the wide range of heating rates, (7) Solomon, P.R.;Hamblen, D. G.; Carangelo, R. M.; Krause, J. L. Nineteenth Symposium (International) on Combustion; The Combustion Institute: Pittsburgh, PA, 1982; pp 1139-1149. (8) Suuberg, E. M. &.D. Thesis, Department of Chemical Engineering, MIT, Cambridge, MA, 1977. (9) Serio, M. A.Ph.D. Thesis, Department of Chemical Engineering, MIT, Cambridge, MA, 1984. (10) Serio, M. A.;Hamblen, D. G.; Markham, J. R.; Solomon, P. R. Energy Fuels 1987,1, 138-152.
Tar Evolution Rate Predictions
Energy & Fuels, Vol. 2, No. 4, 1988 569
Table 11. Experimental Conditions Employed in Pyrolysis of Pittsburgh Seam Bituminous Coal Suuberg Seriob Serio et al.' screen heater packed bed TGA reactor type as received wet sieved, d pretreatment partially dry particle size 53-88 pm 250-350 pm d sample size 10-15 mg d -1 g 1 atm, He 1 atm, He (1-270 1 atm pressure Ar) maximum temp -1050 "C 550 O C 900 "C heating rate 1000 " C I S 0.05 O C / s 0.5 "C/s cooling rate -200 "C/S continuous continuous heating heating 30 min 0 holding time 2-10 s
0.15
0.14 0.13
-
Reference 8. 45.0 ,
Reference 9. 'Reference 10.
200
Not reported
35'0 30.0
6W
,
020
4004
400 TUIPEWNRE (C)
a
019-
b
017 0.16 0.15 0.14 0.18
1
010 0.13
0.12
0.11
009 008 0.07 0.06 0.05 0.04 -
-
-
400
200
600
200
TEMPERATURE(C)
400
600
TEMPERANRE (C)
30.0
-
25.0
-
20.0
-
15.0
-
10.0
-
50
I
00
F i g u r e 3. Comparison of predicted tar evolution rates from (a) the multiple independent parallel reaction model and (b) the functional group model to the data (curve marked by points)1° a t 0.5 "C f s continuous heatup. See Figure 2 for description of symbols. Rates are reported in arbitrary scale. The data curve is not shown in Figure 3b because it matches closely to the FG-87 curve.
-
200
/'400
aoo
TEMPERANRE (C)
Figure 2. Comparison of predicted tar yields from (a) the multiple independent parallel reaction model and (b) the functional group model to the data at 0.05 "C/s continuous heatup. See Table IV for definitions of acronyms. Key for part a: (- -) MIPR-SE1; (--) MIPR-SE2; (--) MIPR-SK (-) MIPR-SS. Key for part b: (--) FG-81; (--) FG-82; (--) FG-85; (-) FG-87. Data points are denoted by a box and were taken from ref 9. from 0.05 to lo00 "CIS, and the use of sufficiently small particle sizes (588 Mm at lo00 OC/s and 5350 pm at 0.05 "C/s) to insure essentially uniform temperature within the particles at the heating rates employed.11J2 In all cases, t h e pressure is 1atm and the maximum temperature is 1050 O C or less. Also included in Table I1 is information on data sets from which the model parameters used in the present study were obtained. The data of Seriog and Suubergs are reported as cumulative yields measured gravimetrically, whereas those of Serio et al.'O are reported as rates with an arbitrary scale. (11)Hajaligol, M. R.; Peters, W. A.; Howard, J. B. Prepr. Pap.-Am. Chem. Soc., Diu. Fuel Chem. 1987,32(3), 8-23. (12)Hajaligol, M. R.;Peters, W. A.; Howard, J. B. Energy Fuels, previous paper in this issue.
Proximate and elemental analyses of the coal are shown in Table 111. The coals are seen to be slightly different in terms of chemical composition, conditions of storage, pretreatment before pyrolysis, and mine location of sample origin. While coal storage for a long time under an oxidative environment can alter tarformation behavior,13J4the slight variations in histories of the present samples appear to have a negligible effect on tar production. Thus, the tar yield data of Suuberg8 have been reproduced by others1"17 using similar Pittsburgh Seam bituminous coal samples collected a t different times but still fairly fresh.
Fitted Parameters Table IV lists the values of rate parameters for the two models. For the M E R model, MIPR-SE1and MIPR-SE2 are two statistically similar sets of fitted rate parameters of Seriogfor 0.05 O C / s data. MIPR-SK data were obtained in the present study by using the 1000/200 "C/s heatup/cooldown data of Suuberg.8 MIPR-SS data were obtained in the present study using the low-heating-ratedata of Seriog combined with the high-heating-rate data of (13)Reitzen, T. R. M.S. Thesis, Department of Chemical Engineering, MIT, Cambridge, MA, 1978. (14)Neavel, R. C.; Smith, S. E.; Hippo, E. J.; Miller, R. N.
F'roceedcngs-Internationale Kohlenwissenschaftliche Tugung, 1981; Verlag GlGckauE Essen, West Germany, 1981; pp 1-9. (15) Franklin, H. D. Ph.D. Thesis, Department of Chemical Engineering, MIT, Cambridge, MA, 1980. (16)Darivakis, G.Ph.D. Thesis,Department of Chemical Engineering, MIT, Cambridge, MA, in preparation. (17) KO,G. H. Ph.D. Thesis, Department of Chemical Engineering, MIT; Cambridge, MA, 1988.
KO et al.
570 Energy & Fuels, Vol. 2, No. 4, 1988
Table 111. Characteristics of Pittsburgh Seam Bituminous Coal Used in Time-Resolved Tar Evolution Measurements Solomon Solomon Solomon and Hamblen'; Suuberg' Serid et al.8 et al." Serio et al.' pretreatment as-received wet-sieved, partially dry d d d volatile matter fixed carbon ash moisture
39.4 49.1 11.5 0.55b
41.6 48.1 10.3 1.4c
Proximate Analysis (wt %, dry) d d d d
d d d d
C H N
77.7 5.5 1.5 d 9.9"
82.0 5.7 1.4 d 5.6"
Ultimate Analysis (wt %, daf) 85.3 5.7 1.7 2.1 5.2
81.9 5.4 1.4 1.9 9.4
8.2
5.4
5.3
Total Sulfur (wt %, dry) d
d
d
so 0
*
By difference. As-received basis. 7. 'Reference 19. jReference 10.
Dried basis.
Information not reported.
e Reference
82.1 5.6 1.7 2.4
8. f Reference 9. BReference 25.
Reference
Table IV. Kinetics Parameters for Tar Evolution from Pyrolysis of Pittsburgh Seam Bituminous Coal data set MIPRMIPRMIPR-SK MIPR-SS FG-81 FG-82 FG-86 FG-87 SE1 SE2 fitted by Seriod Seriod present work present work Solomon et a l l Solomon et a1.h Solomon and Serio et al.k Hamblenj Seriod and Solomon and Solomon and Seriod Seriod SuubergO Solomon and Serio et aLk data source Suuberge Colketg Hamblen' Hamblenj 13.65 13.0 8.25 3.73 12.65 7.64 13.6 14.93 log (ko/S-') 52.457 34.1 55.2 35.4 17.585 52.457 Eo, kcal/mol 52.8 55.040 u, kcal/mol 1.10 2.50 0" 2.981 2.981 2.981 7.97 2.80 27.2 25.7 V*, w t % (daf) 26.5 26.7 0.30 0.43 0.43 O . l l b or 0.43c XO 0.582 0.562 0.562 0.562 Yll"C,O Single-reaction fit. bFor TGA-type reactors. cFor other reactors. dReference 9. eReference 8. f Reference 25. g Reference 6. hReference 7. 'Reference 18. jReference 19 kReference 10.
Suuberg.s For the FG model, FG-81 and FG-82 are fitted parameters of Solomon and Colkets and Solomon and Hamblen,ls respectively, from data obtained with grid reactors using fast heating under vacuum (heating rate not reported). FG-85 data are from Solomon and HamblenlB for low-heating-rate data, and FG-87 data are from Serio et al.1° for low-heating-rate data.
Comparison Figures 2-4 compare the model predictions to the data at 0.05 O C / s continuous heatup, 0.5 "C/s continuous heatup, and 1000/200 "C/s heatup/cooldown, respectively. Both models are seen to agree well with the data from which the parameters were obtained, namely, MIPR-SE1, and MIPR-SE2 at 0.05 "C/s, FG-87 at 0.5 "C/s, and MIPR-SK at 1000/200 "C/s. Agreement is generally poor, however, when these parameters are used at other heating conditions (e.g., MIPR-SE1, MIPR-SE2, and FG-87 at 1000/200 "C/s and MIPR-SK at 0.05 "C/s). This failure suggests that for tar evolution, neither model using a set of rate parameters fitted at one heating rate is generally applicable at other heating rates. The figures also show, however, that the MIPR-SS parameter set, which is fitted by using data from two widely different heating rates, works well at all three heating rates. This finding demonstrates that the MIPR model can be (18) Solomon, P. R.; Hamblen, D. G. Paper presented at the Conference on the Chemistry and Physics of Coal Utilization; Morgantown,WV, 1980. (19) Solomon,P. R.; Hamblen, D. G. In Chemistry of Coal Conversion; Scholsberg, R. H., Ed.; Plenum: New York, 1985;pp 121-251.
applied over a wide range of temperature histories to predict tar evolution rates, if the rate parameters are fitted using data from widely different heating rates. The work of Weimer and Nganmand Sprouse and Schuman21further supports the applicability of the MIPR model over a wide range of temperature histories. They report that the model works well to fit gas evolution data at heating rates from 0.5 to about 1000 "C/sZoand total weight loss data for lignite devolatilization at temperatures from 700 to 1827 "C and heating rates from 180 to 1000000 "C/s21. We did not attempt to obtain an improved set of parameters for the FG model by fitting it to the data at different heating rates because the model requires a simultaneous fit of rate parameters for tar and rate parameters for all gas species. Since the much simpler MIPR model works well over a wide range of temperature histories, the extensive computational effort that would be required to fit the FG model parameters to data at different heating rates is not warranted. The figures show a generally poor agreement between the tar yield data and predictions with the FG model using all four seta of parameters at both low (0.05 "C/s) and high (1000 "C/s) heating rates. A large overprediction of the yield limit by about 10 w t % daf for FG-82, FG-85, and FG-87 is particularly apparent in Figures 2b and 4b. Many difficulties were encountered here in trying to compare the predictions from the above parameters to the data from (20)Weimer, R. F.;Ngan, D. Y. Prepr. Pap-Am. Chem. SOC.,Diu. Fuel Chem. 1979,24(3), 129-140. (21)Sprouse, K. M.; Schuman. M. D. Combust. Flame 1981, 43, 265-271.
Energy & Fuels, Vol. 2, No. 4, 1988 571
Tar Evolution Rate Predictions
:I
1
45.0
a
30.0
I
"
I
150 140 130 120 110 100 90 80
70 60 51)
40 50
300
5w
20 10 0 -10 -20
900
700 TEMPER*NRE(C)
--
-40
45.0
- ---_--__
b 40.0
-
35.0
-
_---_
0
-
20.0
-
15.0
-
100
-
5.0
-
M
--
u
1
I
YIPR-SEI
30.0 25.0
I
-" 1 YIPR-SU
MIPR-SK
YIPR-SS
FG-81
FG-82
FG-85
FG-87
Figure 6. Comparison of AT, and AT,. from the multiple independent parallel reaction model and &e functional group model at 0.5 OCfs continuous heatup. AT,, and AT& are defined in Figure 5.
0.0 300
SO0
so0
700 TEMPERATURE (C)
Figure 4. Comparison of predicted tar yields from (a) the multiple independent parallel reaction model and (b) the functional group model to the data (o)sat 1000/200 OC/s heatup/ cooldown. See Figure 2 for description of symbols. 140 120 100
-160
I
YIPR-SEI
I
I
MIPR-SE2
MIPR-SK
I
YIPR-SS
1
I
1
I
FG-81
FG-82
FG-85
FG-87
Figure 7. Comparison of AT, and ATai from the multiple independent parallel reaction model and tie functional group model at 1000/200 O C f s heatupfcooldown. AT,, and AT* are defined in Figure 5.
-20
-40 -60 -80 -100 -120
-140
YIPR-SE1 MIPR-SB MIPR-SK
UIPR-SS
FG-E1
FG-67.
FG-85
FG-87
Figure 5. Comparison of AT, and ATBi from the multiple independent parallel reaction model and tie functional group model at 0.05 "C/s continuous heatup. T,, = temperature at which maximum rate occurs; AT- = T,,(predicted) - T-(experimental); TBk= temperature range in which the yield is between 15.87% and 84.13% of the final yield; AT,, = Tsig(predicted)- T,&(experimental). which they are originally obtained. The rate parameters in some cases are reported without showing any comparisons to the experimental data.7 In other cases where comparisons are given, the data are not quantitative as evidenced by an arbitrary scale used in the reported tar
rate data of Solomon and Hamblen.lg Furthermore, Serio et al.1° give too few data points in the increasing tar evolution region to make a critical evaluation of the model predictions (discussed in more detail below). It is also important to note that FG-81 and FG-82 were fitted from screen heater data obtained under vacuum at a fast heating rate (not reported), in which case the temperature measurements are more uncertain,22and their suitability for parameter evaluation is not established. More quantitative evaluations of the two models can be made from Figures 5-7, where the rate predictions are at which the characterized by the temperature (Tma) maximum rate occurs and the temperature spread (Tab) of the rate versus temperature curve. We arbitrarily define the temperature spread as the range of temperatures in which the yield is between 15.87% and 84.13% of the final yield (equivalent to 2 standard deviations for a rate versus (22) Oh, M. S. Sc.D. Thesis, Department of Chemical Engineering, MIT; Cambridge, MA, 1985.
572 Energy & Fuels, Vol. 2, No. 4,1988
KO et al.
40
E $,
1
20 O
P s L
1oj
0 0
0
I
l
heating rate of about lo4 "C/s and maximum temperature of 1100 OC. Comparison of model predictions to these data is of little kinetics meaning because of two few data points (only two) in the region of increasing tar yield. All the other data points essentially represent the yield limit, which, as described earlier, can easily be fitted with V* in the MIPR model or with Xoin the FG model. The data in the figure show that the yield limit is approximately 15 wt % (basis not reported), which represents only about 60% of the yield reported by other i n v e s t i g a t ~ r s ~ ~ ~ J ~ ~ ~ for the same coal at heating rates between 0.05 and about 15000 "C/s. The low yields seen in Figure 8 are said to be due to incomplete collection of tar,1°and the agreement between prediction and experiment is said to be fairly good if missing material, which is not defined, is added to the collected tar.lOJg However, in the rising portion of the predicted tar yield curve (i.e., for injector positions of about 5 and 15 cm), the missing material plus collected tar actually decreases with increasing distance, which is opposite to the trend predicted by the FG model (Figure 8). At injector positions exceeding 15 cm, tar formation as predicted by the FG model has essentially ceased and the tar already released is assumed to be consumed by secondary cracking reactions,1° the contribution of which is represented by the decreasing part of the predicted curve in Figure 8. Thus, the tar actually measured differs substantially from that predicted by the FG model, and the stated agreement apparently pertains not to the FG model but to a comparison between an invoked amount of tar consumption by secondary reactions and an assumed amount of tar consumption inferred from the decrease of tar plus missing material. Therefore, the FG model predictions do not appear to agree well with the data, to the limited extent that a comparison can be made for the region between the first two injector positions. Accordingly, the application of this model at heating rates of 104-105 "C/s requires further study. Although we have not been able to test the MIPR model in this same range of higher heating rates, its favorable performance over a wide range of lower heating rates as discussed above the encouraging findings of Weimer and Ngan20and Sprouse and Schuman2' also discussed above, add confidence to the use of the MIPR model with the MIPR-SS set of parameter values for applications at heating rates higher than 1000 "C/S. The limiting tar yields predicted from the two models are quite different, due partly to large discrepancies in the experimental data used for parameter fitting and partly to the fundamental differences between the two models. Figure 9 compares the limiting values predicted from the models for the three temperature histories used above. In the MIPR model the yield is equal to V*, which is one of the fitted parameters, and is independent of heating rate. Experimental values of ultimate yields reported by Suuberg,S Suuberg et al.,24Franklin,15 S e r i ~and , ~ Freihaut et al.23for Pittsburgh Seam bituminous coal, range from 26.5 to 29.5 wt % daf, and are found to be independent of heating rate in the range 0.05-15000 "C/s. Also reactor types (screen-heater and packed-bed-type reactors), and particle sizes between 53 and 350 pm are reported to have no observable effects on the yield limit. The FG model,
50
0
20
40
60
INJECTOR POSlTlON (cm)
Figure. 8. Tar yield in an entrained-flow reactor at high heating rate, -1OOOO "C/s: points, experimental data; curve, prediction using the functional group model for tar evolution combined with a secondary reactions model for tar destruction. The weight basis (e.g., daf or as-received)was not reported. Adapted from ref 10.
temperature curve in the form of a binomial distribution). The figures show AT,, and ATsig,defined as the differences between the predicted and experimental T,, and TBigvalues. At low heating rates (Figures 5 and 6), the predicted T,, and Tsigfrom FG-85 and FG-87 agree with the experimental values to within 50 "C. T,, from FG-81 underpredicts the experimental values by 125 and 55 OC at 0.05 and 0.5 "C/s, respectively. As mentioned above, we suspect that the large disagreement is due to uncertainties in temperature measurements under vacuum. Predictions from the MIPR model using fitted rate parameters from S e r i ~MIPR-SE1 ,~ and MIPR-SE2, agree well with experiment at the low heating rates. Figures 5 and 6 also show that the fitted parameters from high heating rate data, MIPR-SK, overpredict T,, and TSk,by more than 50 and 140 "C, respectively. A significant improvement is made when parameters are fitted with combined data of low and high heating rates. MIPR-SS prewithin 50 "C of the data at all three dicts both T- and TBig heating rates, except for Tsigat 1000/200 "C/s heatup/ cooldown, which is underpredicted by about 60 "C. Figure 7 shows that rate parameters for both models fitted from low-heating-rate data also fail badly when they are extrapolated to high heating rates. The FG model using FG-85 and FG-87 consistently underpredicts T,, by more than 80 OC and Tsigby more than 130 "C. The widely different T,, predictions from MIPR-SE1 and MIPR-SE2 at the high heating rate demonstrate a danger in applying the MIPR model to predict tar evolution rates at a heating rate that is very different from that at which the parameters were fitted. Both sets of parameters fitted the data equally well at 0.05 "C/s but produced very different predictions at 1000/200 OC/s, where SE1 underpredicts and SE2 overpredicts T-, both by more than 50 "C, and both underpredict Tsigby more than 100 "C. MIPR-SS predicts T- accurately at the high heating rate, as it did at lower heating rates. The TBi,prediction is slightly worse, about 60 "C too small. Adequate data are not available for comparison of the models at higher heating rates (lo4-lo5 "C/s). Figure 8 shows the only set of time-resolved tar yield measurements for Pittsburgh Seam coal found in the literature. The data were c01lected~~J~ from an extrained flow reactor at a
(23) Freihaut, J. D.;Zabielski, M. F.; Seery, D. J. Nineteenth Symposium (International) on Combustion; The Combustion Institute: Pittsburgh, PA, 1982; pp 1159-1167. (24) Suuberg, E.M.; Peters, W. A.; Howard, J. B. Seventeenth Symposium (International) on Combustion; The Combustion Institute: Pittsburgh, PA, 1979; pp 117-130. (25) Solomon, P. R.;Hamblen, D. G.; Carangelo, R. M. Paper presented at the AIChE National Meeting, New Orleans, LA, 1981.
Tar Evolution Rate Predictions 40
a
MIPS-SEI
Energy & Fuels, Vol. 2, No. 4, 1988 573
0.05 C/s
MIPR-SE2
UIPR-SK
MIPR-SS
FG-81
FC-82
iC-85
FC-87
Figure 9. Comparison of maximum tar yields predicted from the multiple independent parallel reaction model and the functional group model a t 0.05 and 0.5 OC/e continuous heatup and 1000/200 OC/s heatup/cooldown.
on the other hand, predicts a decrease in the ultimate tar yield at higher heating rates because the gas formation from the potential tar forming fraction, X o (1- Y,,,), is favored over tar formation at higher temperatures. The magnitude of the decrease varies from 29.2 to 24.5 wt % daf for heating rates from 0.05 to 100000 OC/s for FG-81 and from 40.5 to 39.9 w t % daf for the same heating rate range as above for FG-87. It is also important to note that the predicted yield limit, at 1000/200 "C/s heatup/cooldown, is higher by over 10 w t % daf using FG-85 and FG-87 compared to the experimental values reported by the five groups of investigators mentioned above. No reasons are given to explain such a large difference. The limiting tar yield predicted by the FG model depends on reactor type, because the parameter X o (Table I, Figure 1) does also. For example, ultimate tar yields predicted from FG-87 vary from 10.3 ivt % daf for parameters obtained by Serio et al.'O from thermogravimetric reactor (TGA) runs at 0.5 OC/s to 40.5 wt % daf for parameters from entrained-flow and screen-heater reactors and heating and cooling rates of lo00 OC/s and 200 OC/s, respectively (Figure 9). The 10.3 wt % daf predicted ultimate tar yield is much lower than the 27.8 wt % daf observed experimentally by Serio9 at a factor of 10 lower heating rate (0.05 "C/s). Thus, the smaller ultimate tar yields observed with the TGA cannot be explained by its lower heating rate vs the 103-104 "C/s of screen-heater and entrained-flow reactors. The fact that the FG model predictions of both rates and yield limits differ significantly between the four sets of parameters is of major concern. For example, at 1000/200 "C/s (Figure 7), T- varies from 566 "C (FG-87) to 780 OC (FG-81), TSi from 105 "C (FG-87) to 225 "C (FG-81), and the yield fimit from 27.0 (FG-81) to 41.1 wt % daf (FG-85). In contrast, the yield limits predicted by using the MIPR model and the different sets of parameters are consistent among themselves as well as with the experimental values reported by the five groups of investigators. Each set of parameters can reliably predict tar evolution rates over a narrow range of heating rates from
which the parameters were obtained but generally not at other heating rates. Furthermore, the MIPR model, with parameters fitted from data collected at two widely different heating rates, is shown to work well over a wide range of heating rates. A reason for the poor performance of the FG model against Pittsburgh Seam coal tar evolution data may be that it employs the same rate parameters for the evolution of a given product such as tar for all coal types regardless of whether the coal is softening or nonsoftening. The assertion that coal type has no effects on the kinetics of product evolution,7J9 which forms the basis of the FG model, needs to be examined. Our recent time-resolved tar yield data for six different coals ranging from nonsoftening lignites to softening bituminous coals indicate that tar evolution kinetics does indeed depend on coal type." A complete tar evolution model must consist of a model that can accurately describe the kinetics of tzr formation over a wide range of temperature-time histories, coupled with models that describe mass-transfer and secondary cracking reactions at high pressures and/or with large particle sizes. The present findings, from comparing the two models to the data from low pressures and small particle sizes where the transport effects are minor, suggest the MIPR model with rate parameters obtained from two or more widely different heating conditions would be the better choice for the formation-kinetics part of the complete tar evolution model. Use of the FG model for the tar formation kinetics would require a better set of parameter values than is now available for this coal, and fitting this model to data requires a much larger effort than does the MIPR model for the reasons given above. Furthermore, if different temperature-time histories are of interest, use of the FG model would give erroneous predictions of the limiting tar yield, and the predictive capability of this model for tar formation kinetics over a range of heating rates is not established.
Conclusions This work has demonstrated that the MIPR model with a given set of kinetics parameter values can be used reliably to predict tar evolution rates over the range of heating rates from which the data used in evaluating the parameters were obtained. Thus, the heating-rate range of applicability is wide if the parameters are fitted from data collected at two widely different heating rates but narrow if the data are from only one heating rate. The agreement between the FG model predictions and the data from which the fitted parameters were obtained is inconclusive due to uncertainties in the data used to obtain the parameters. Comparison of the FG model predictions with experimental data reveals large discrepancies in maximum tar yields and in the kinetics of tar evolution at heating rates above and below the one used in the data from which the parameters were obtained by fitting. Acknowledgment. We are grateful to the United States Department of Energy for financial support under Contracts No. DE-AC21-85MC22049 and DE-AC2284PC70768 and to Debbie Sanchez for help in preparing the figures. A 1967 (Centennial) Science and Engineering Scholarship from the Natural Sciences and Engineering Research Council of Canada was gratefully received by G.H.K.
