Ind. Eng. Chem. Res. 1990, 29, 2173-2180
2173
KINETICS AND CATALYSIS
Modeling and Predicting the Composition of Fossil Fuel Derived Pyrolysis Liquids by Using Low-Voltage Mass Spectrometry and Canonical Correlation Analysis Tanmoy Chakravarty Reaction Engineering Skill Center, Bechtel Corporation, P.O. Box 2166, Houston, Texas 77252
M. Rashid Khan Texaco Research Center, P.O. Box 509,Beacon, New York 12508
Henk L. C. Meuzelaar* Center for Micro Analysis and Reaction Chemistry, University of Utah, Salt Lake City, Utah 84112
Low-voltage electron ionization mass spectrometry (LV-EIMS) was performed on 25 fossil fuel samples (21 coals, 2 oil shades, 1tar sand, and 1 coal resin concentrate) and their respective pyrolysis liquids prepared a t Morgantown Energy Technology Center (METC) by means of a fixed-bed reactor. By using principal component analysis, the tar evaporation spectra and the solid fuel pyrolysis spectra were classified in terms of the underlying structural variables. In both data sets, all 4 non-coal samples, as well as 2 less typical coal samples, were found to be outliers. After removing the 6 outliers, canonical correlation analysis was performed on the remaining subsets of 19 coal samples in order to bring out the compositional similarities and differences between the fossil fuel samples and their pyrolysis liquids. By determining the common sources of variance between the two data sets by means of canonical correlation analysis, it was demonstrated that the canonical variate model enabled prediction of the mass spectrum of a given coal tar sample from the measured pyrolysis mass spectrum of the corresponding coal sample. Agreement with the experimental results was reasonably good.
Introd uction A t the Morgantown Energy Technology Center (METC), it has been demonstrated that relatively highquality liquid fuels (low sulfur, high H/C) can be produced by low-temperature devolatilization of coal (Khan, 1987a). It was found that the tar produced at a relatively low temperature using a fixed-bed reactor (slow heating rate organic devolatilization reactor, SHRODR)has a significantly higher H/C ratio and lower heteroatom content than pyrolysis liquids generated from the same coal in fluid or entrained-flow reactors (Khan, 1987b). It was proposed that at a rapid heating rate, the primary pyrolysis products, representative fragments of the parent coal structure, are devolatilized. At a slow heat-up rate, however, the primary coal fragments undergo additional cracking reactions in the coal bed, forming low molecular weight hydrocarbons of relatively higher quality. More details regarding the quality/yield relationships using slow and rapid rate reactors are presented elsewhere (Khan, 1987b). The literature data on the dependence of tar yield on coal type have been presented elsewhere (Khan, 1987a). It was shown that the tar yield is maximized when high volatile coals are used as feedstocks. The relationships between the sulfur content in products (tar, char, and gas) and in feedstock have been developed and reported (Khan, 1988a). Furthermore, the correlations between refractive indices of the liquids and the physical and chemical properties of the liquids have been studied (Khan, 1988b).
* Author to whom
correspondence should be addressed. 0888-5885/90/2629-2173$02.50/0
However, little has been reported in the literature on the relationship between the composition of pyrolysis liquids and the composition of the feedstocks (Khan and Kurata, 1985). Attempts have been made to model the kinetics of devolatilization and gasification by investigating the physical and chemical changes in coal during heat treatment. Kinetic schemes proposed to represent the mechanism of coal pyrolysis range from 2 independent parallel reactions to 42 reactions of 14 different functional groups in coal (Howard, 1981). Howard (1981) reviewed available models to describe the kinetics of volatile product evolution during coal pyrolysis. All available approaches suffer to varying degrees from a lack of reliable mechanistic and kinetic data. It is known (Howard, 1981; Scaroni et al., 1986; Serio et al., 1987) that the extent of devolatilization of coal depends on several parameters, such as coal type, heating rate, temperature, pressure, and “soak time” at the pyrolysis temperature. Product release is further complicated in a gasifier. For example, in a fixed-bed gasification process, there are several overlapping zones. At the top portion of the gasifier, the coal is dried and preheated by the existing hot product gases (Wen et al., 1982). In a subsequent zone, the coal is devolatilized, and gases and tars are released in the presence of devolatilized gases. Several computer codes were developed to simulate the physical and chemical processes occurring during gasification processes. In these models, outlet gas composition and flow are predicted, but tar chemistry and the influence of feedstock type on tar composition are not considered. In addition, the chemical kinetic rate expressions used by the models necessitate a knowledge of approximate kinetic 0 1990 American Chemical Society
2174 Ind. Eng. Chem. Res., Vol. 29, No. 11, 1990
parameters of various volatile solid reactions. Given the uncertainties that unfortunately detract from any effort to describe the kinetics of these individual processes, a n alternative predictive procedure t o asses the products of the basic deuolatilization reaction would be of immediate use. KOet al. (1987) recently published empirical correlations to predict the pyrolysis tar yield based on coal properties. However, correlations are not available in the literature relating product composition with feedstock properties for application in a devolatilization/gasification process. To better understand this relationship, a range of feedstocks were pyrolyzed in a fixed-bed reactor. The approach taken was to utilize a spectrometric method, viz., low-voltage electron ionization mass spectrometry (LVEIMS), combined with a vacuum micropyrolysis technique, viz., Curie-point pyrolysis, to characterize the feedstocks and their fixed-bed pyrolysis liquids. In this study, 25 fuel samples were pyrolyzed in a fixed-bed reactor at a slow heat-up rate. The feedstocks and the pyrolysis liquids were characterized by using Curiepoint pyrolysis LV-EIMS and Curie-point desorption LV-EIMS, respectively (Wen et al., 1982; Yoon et al., 1978 Denn et al., 1982; Joseph et al., 1984). Multivariate statistical analyses were performed on the feedstock data and corresponding pyrolysis liquid data, first separately and then together. The objective is to classify the feedstocks and the products by their structural similarities and differences and to answer the following question: Given a fossil fuel, for example, a coal sample, can we predict what kind of pyrolysis liquid (in terms of its composition) will be produced in this reactor under given process conditions?
Experimental Section Sample Collection and Prediction. Table I lists the samples used, their PSOC numbers, geological origin, and rank information. A range of feedstocks (primarily coal, but also oil shale and tar sand) were devolatilized in this reactor. Coal samples were supplied by the Penn State/DOE coal data bank. The origins of the two shale samples (Eastern and Western) and the Pittsburgh No. 8 coal have been discussed elsewhere (Khan, 1985). Sample preparation and avoidance of air oxidation of samples were considerations to this investigation as reported in previous studies (Khan, 1987~).The availability of fresh (well-preserved, not weathered) samples was the criterion used for sample selection. Primarily bituminous coals were used in this study as these are known to yield the greatest amount of liquid product during pyrolysis (Khan, 1987a). All sample preparation and handling was performed in an inert atmosphere. Table I1 presents the influence of feedstock type on the yield and composition of tar generated in the fixed-bed reactor. Generation of Pyrolysis Liquids. A fixed-bed reactor was used to generate pyrolysis liquids at 500 “C. More details on this reactor system as well as the experimental procedures used and the reproducibility of data are available (Khan, 1987a). Sample Preparation. Solid samples (25), about 4-5 mg each, were weighed into grinding vials with a Cahn electrobalance and ground by hand to approximately -200 mesh in 0.5 mL of methanol. The samples were transferred into conventional glass vials and made up to 1 mL volume in methanol. Liquid samples (25) were prepared as 0.8-1.3 mg/mL solutions in dichloromethane. Typically, 10-30-mg samples were weighed on a Mettler balance and made up to 2-8 mg/mL solutions, which were then diluted. All samples were stored at -10 “C in the refrigerator prior to analysis.
Table I. Type and Origin of Parent Fuel Samples PSOC sample no. geological origin of the samples” coal rank 1 Eastern/Appalachian, Ohio No. 6, hvAb OH 2 1481 Eastern/Appalachian, Upper Clarion, hvAb PA hvAb 3 Eastern/Appalachian, Kentucky No. 8, KY 4 1313 Eastern/Appalachian, Lower mvb Kittaning, PA hvAb 5 375 Eastern/Appalachian, Hazard No. 9, KY 6 267 Eastern/Appalachian, Clintwood, VA hvAb 7 1472 Eastern Appalachian, Lower Banner, hvAb VA Sub C 1520 N. Great Plains/Fort Union, 8 Wyodak, WY 9 Eastern/Appalachian, Pittsburgh No. hvAb 8, WV hvAb 1469 Eastern/Appalachian, Mary Lee, A1 10 11 123‘ Eastern Appalachian, Lower hvAb Kittaning, WV n.a. 1109d Rocky Mountain/South W. Utah, 12 King Cannel, UT 1471 Eastern/Appalachian, Pee Wee, TN hvAb 13 14 n.a. Colorado Oil Shale, CO Interior/Eastern, Illinois No. 6, IL hvCb 15 hvAb 16 306 Eastern/Appalachian, Ohio No. 12, OH hvAb 17 296 Eastern/Appalachian, Ohio No. 5,
OH 18 19 20 21 22 23 24 25
181 1492 1475 1323 275
Interior/Eastern, Upper Block, IN Interior/Eastern, Illinois No. 5, IL Utah Coal Resinite; UT Eastern/Appalachian, Elkhorn No. 3, KY Tar Sand (Asphalt Ridge), UT Interior/Eastern, No. 6, IL Eastern/Appalachian, No. 6A, OH Sunbury Oil Shale, KY
Sub A hvCb n.a. hvAb n.a. hvBb hvAb n.a.
Concentrated resina Coal province/region, seam name, state. ite fraction separated from Hiawatha seam coal. cPSOC 123: lithotype sample. d PSOC 1109: Cannel coal or bituminite.
Mass Spectrometric Analysis. Five-microliter aliquots of each sample were coated on the ferromagnetic wires used in Curie-point pyrolysis, air dried, and analyzed in the standard manner (Meuzelaar et al., 1982) under the following conditions: Curie-point temperature, 610 “C; heating rate, aproximately 100 K/s; total heating time, 10 s; electron energy (set value), 12 eV; mass range scanned, m / z 20-260; scanning rate, 1000 amu/s; total scan time, approximately 20 s. Each sample was analyzed in triplicate, and the spectra were stored in an IBM 9000 computer. All MS analyses were completed in a single day. Reproducibility aspects of coal pyrolysis data and coal liquid desorption data using Curie-point mass spectrometry have been discussed in detail in several reports (Meuzelaar et al., 1984a, 1986a). Data Processing. The stored mass spectra of the 25 parent fuel and 25 SHRODR liquid samples were first processed to normalize the mass spectra and to eliminate any nonreproducible spectra using an iterative approach described by Harper et al. (1984). Subsequently, multivariate statistical analysis was performed by means of the SIGMA (System for Interactive Graphics-oriented Multivariate Analysis) program package developed by Windig and Meuzelaar (1985). Each data set was first subjected to factor analysis, followed by discriminant rotation, and finally, canonical correlation analyses of the respective coal and tar data sets. Factor analysis (also called Principal Component Analysis, PCA) as used here results in independent (“orthogonal”)
Ind. Eng. Chem. Res., Vol. 29, No. 11, 1990 2175 Table 11. Tar Yield and Composition elemental anal. sample 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
PSOC no. or type (coalY PSOC 1481 (coal)a PSOC 1313 PSOC 375 PSOC 267 PSOC 1472 PSOC 1520 (coal) PSOC 1469 PSOC 123 PSOC 1109 PSOC 1471 (oil shale) (coal) PSOC 306 PSOC 296 PSOC 181 PSOC 1492 (resinite) PSOC 1475 (tar sand) PSOC 1323 PSOC 275 (oil shale)
tar yield, % 12.4 15.3 14.2 5.5 15.2 14.3 14.8 7.9 17.7 8.4 14.8 25.0 16.1 12.5 14.0 10.3 15.4 8.1 15.6 83.1 15.8 11.5 14.6 12.6 4.0
C
H
N
S
78.3 74.7 79.9 83.9 79.4 85.0 86.0 78.6 80.7 84.0 84.9 78.7 79.7 84.4 76.2 80.3 83.9 83.0 66.7 87.7 84.6 87.3 73.1 82.4 82.3
5.6 8.9 5.0 8.6 8.7 8.9 9.4 9.7 9.1 9.1 10.2 11.7 8.7 11.3 9.0 8.7 9.4 9.4 9.1 11.1 10.1 11.3 8.6 9.6 9.9
1.2 2.2 1.4 1.0 1.7 1.7 2.1 2.0 1.6 3.0 2.0 1.8 3.0 2.0 1.7 2.9 1.8 1.9 2.5 1.0 2.0 1.7 1.6 2.4 1.5
2.9 2.1 1.2 2.6 1.0 0.8 0.2 0.5 0.7 0.5 0.7 0.7 0.5 0.7 0.5 1.5 0.7 0.5 2.2 0.2 0.4 0.3 2.4 1.2 1.9
H/C ratio 0.85 1.43 0.75 1.20 1.31 1.26 1.31 1.49 1.35 1.30 1.44 1.78 1.31 1.61 1.42 1.29 1.34 1.35 1.63 1.53 1.43 1.56 1.41 1.40 1.44
he a ting value, Btu/lb n.a. 15 704 n.a. 17 437 15 796 17 026 16819 16 124 15 726 16 800 17 449 16 717 15 036 18300 14 532 14 188 16919 16 764 15 294 18427 15 368 18 975 15 741 13 726 17 538
aFor more detailed information on these coals, see Khan (1989a, 1989b).
linear combinations of the original variables according to the equation n
Fj = Cc~ijZi i=l
where Fi is factor j and aii is the correlation coefficient ("loading") of original variable Ziwith Fi. This generally results in substantial data reduction due to the correlated behavior of the mass variables. In other words, PCA allows one to estimate the intrinsic dimensionality of the data space. Discriminant rotation constructs a series of independent linear combinations of masses that discriminate between predefined groups by orthogonal rotation of the original factor axes in such a way that the rotated independent linear combination describes the maximum ratio of between-group to withingroup variance. In our application, a group consists of replicate analyses of the same sample. Within-group variance thus may be thought to represent "experimental noise". A comprehensive overview of the application of factor analysis, and subsequent discriminant rotation, procedures to LV-EIMS data on fossil fuels and other complex organic materials has been given by Windig and Meuzelaar (1987). Canonical correlation analysis maximizes the common variance between two or more sets of measurements on the same samples by suitable rotation of the corresponding factor (or discriminant) spaces, thereby defining a common, lower dimensional subspace spanned by one or more orthogonal canonical variate functions. Thus, each canonical variate function can be regarded as an orthogonal rotation of the original factors (or discriminant functions). A more detailed discussion of the use of canonical variate analysis for spectroscopic data can be found elsewhere (Hoesterey et al., 1988).
Results and Discussion Solid Fossil Fuel Analysis Data. The low-voltage pyrolysis mass spectra of the 25 samples were preprocessed in a standard manner (Harper et al., 1984) before performing factor and discriminant analyses. Factor analysis
resulted in five significant factors (determined by the slope of the eigenvalue plot) that accounted for 90% of the variance in the data. The goal of the multivariate data analysis process was to maximize the outer group variance (samples analyzed in triplicate are categorized as one group), and therefore, discriminant rotation was carried out. Four "significant" disciminant functions were obtained; the score plots of these are shown in Figure 1. The score plots reveal the similarities and dissimilarities between the samples. For example, the DI/DII score plot (Figure l a ) shows that samples 20 (Utah resinite), 14 (Colorado oil shale), and 12 (King Cannel coal) are quite different from the others. Figure 2a shows the mathematically constructed "discriminant spectrum" for the DI+ direction obtained by means of the numerical procedure described by Windig and Meuzelaar (1984). The characteristic variables of the concentrated resinite fraction (sample 20), namely, m/z 202,204,206,163, and 81 are all prominent in this spectrum (Simoneit et al., 1986; Crelling et al., 1990). The characteristic aliphatic hydrocarbon pattern shown in Figure 2b can be recognized as that of the Colorado oil shale (sample 14), shared also by the King Cannel coal (sample 12). The resemblance between the latter and typical oil shale patterns has also been noted in previous studies (Meuzelaar et al., 1984a). The score plot in Figure l b together with the discriminant spectrum in Figure 2c identifies the pattern in the spectrum as characteristic for Sunbury oil shale (sample 25). The pyrolysis mass spectrometry (Py-MS) pattern of related Eastern oil shales was known from an earlier study (Chakravarty et al., 1988a). The pattern shown in Figure 2c shows high relative abundances of m/z 34 (H2S), 44 (CO,), 60 and 74 (e.g., aliphatic acids), 94, 108, and 122 (phenols), and 110 and 124 (dihydroxybenzenes). Finally, the pattern in Figure 2d is related to the only coal of medium volatile rank, PSOC 1313 (Lower Kittaning seam, sample 4), and shows that this coal has relatively high levels of sulfur appearing as S2+/S02+ (at m/z 64) as well as aliphatic (CnHZn+) and aromatic (alkylnaphthalene) hydrocarbon intensities consistent with its higher rank
2176 Ind. Eng. Chem. Res., Vol. 29, No. 11,1990
P" 1
, -1.5
I
DISCRIMINANT FUUCTION DI
2.8
Figure 3. Discriminant function score plot (DI vs DII) of reduced pyrolysis MS data set (19 coals). Note rank trend (A) direction.
b)
-1.8
3b
DISCRIMINANT FUNCTION Om
Figure 1. Discriminant function score plots of (a) DI vs DII and (b) DIII vs DIV in pyrolysis MS data on all parent materials.
c.
b) DIP Component
ls( I
d) DIV- Component
Figure 2. Numerically extracted discriminant spectra of parent fuels. Compare component directions with Figure 1.
(Meuzelaar et al., 1984a). It should be noted here that chemical identification of the low-voltage MS signals is tentative only and based on extensive literature data on Curie-point Py-MS and Py-GC/MS patterns of coals (e.g., Meuzelaar et al., 1984a,b; Metcalf et al., 1987; Nip et al., 1985; Nip et al., 1988) and oil shales (Meuzelaar et al., 1986b; Chakravarty et al., 1988a; Larter et al., 1978). Since inevitably more than one ion species will be found at most nominal m a s values, only the expected dominant chemical compound or compound series is mentioned in the figures and text. Using factor analysis and discriminant rotation techniques, so far we have examined the characteristicsof only 5 out of 25 fossil fuel samples. Now, we will show that by proper selection of features (mass variables) and cases (samples) more detailed information on the structures of even closely related samples can also be obtained. On the basis of the results of factor and discriminant analyses, a smaller data subset containing only 19 coals out of the 25 samples was created in order to examine the coal data in the more detail. The Colorado (sample 14) and Sunbury (sample 25) oil shales, the tar sand (sample 22), the concentrated resinite fraction (sample 20), and two cod samples, namely, mvb rank PSOC 1313 (sample4) and King Cannel seam PSOC 1109 (sample 12), were shown to be outliers in the above multivariate statistical analyses and were eliminated. The resulting subset, consisting of 19 cases, each analyzed in triplicate, was later used for canonical correlation analysis and prediction of the tar spectra. Only mass variables with m/z values I90were selected since the more volatile lower MW pyrolysis products were lost during collection of the SHRODR liquids. Consequently, the patterns below m/z 90 are grossly different between the tars and the coals. The low molecular weight hydrocarbon products formed in the fixed-bed reactor were collected and analyzed separately (Khan, 1987a, Khan, 1988a). The 19 sample data subset, which contained 171 variables, was studied by factor analysis and discriminant rotation to reveal some interesting differences among the more highly similar coal samples. Four discriminant functions were found to be significant, accounting for 64.5% of the total variance in the data. Figure 3 shows the score plot in DI/DII space. The direction marked A in Figure 3 separates out several of these coal samples, mostly with regard to rank. As expected, sample 8, the
Ind. Eng. Chem. Res., Vol. 29, No. 11, 1990 2177
-
CI
2
I-
a) (A)- Component
94
124 I.
I
I
4
u
12
4’
m/r
Figure 4. Numerically extracted discriminant spectra representing rank trend A in Figure 3.
only subbituminous C coal, accounts for much of the variance in the negative direction of A. The characteristic variables are shown in Figure 4a, namely, phenols ( m / z 94 and 108) and dihydroxybenzenes ( m / z 110 and 124). The positive direction of A represented by the group of hvAb coals (sample 2, 3,5,6,7,9, 10, 13, 16, 17, 21, and 24) is primarily dominated by the presence of naphthalenes ( m / z 142,156,170, and 184)as shown in Figure 4b. The remaining variation in Figure 3 is likely to represent other sources of variance than rank, e.g., depositional environment or weathering status. In fact, the coal sample furthest away from the rank trend axis PSOC 123 (Lower Kittaning, sample 11) is a lithotype sample and thus is likely to represent depositional influences. However, in this paper, we do not intend to provide an in-depth analysis of the compositional and structural aspects of coal spectra. For such a discussion, the reader is referred to earlier reports (e.g., Meuzelaar et al., 1984a). From the solid fuel Py-MS patterns, it can be concluded that all non-coal samples, as well as two rather special coal types representing markedly different depositional environments, were found to be outliers. After removing these outliers, the differences between the spectra of the remaining 19 coal samples were primarily dominated by ran k-dependent chemical patterns. Tar Analysis Data. The LV-EIMS data set obtained from all 25 SHRODR liquid samples was treated similarly to that of the solid samples. Factor analysis resulted in five significant factors, accounting for 85% of the variance in the data. Discriminant rotation, carried out to maximize the between-group variance, yielded four significant discriminant functions. These will be discussed in some detail. Parts a and b of Figure 5 describe the score plots in the DI/DII and DIII/DIV discriminant function subspaces, respectively. The score plots, especially DI/DII, are rather similar to those for the coal data set. All in all, the same six samples earlier deleted from the solid fuel data set (nos. 4, 12, 14, 20, 22, and 25) are again found to be outliers. Figure 6a, the spectrum in the DI+ direction, shows the mass variables characteristic of the resin tar sample (no. 20). Comparison with Figure 2a reveals a similar pattern with the higher molecular weight sesquiterpenoid signals predominating as expected, in the tar. Typical sesquiterpenoid components, commonly observed in pyrolysis mass spectra of resinites (Simoneit et al., 1986), are seen at m / t 198,200,202,204, and 206, all of which are present here. Figure 6b shows the negative component of DII, which reflects the variables characteristic of samples 14 (tarsand) and 22 (Colorado oil shale), both of which reveal their strong aliphatic hydrocarbon character. The aliphatic hydrocarbon series, characteristic of Colorado oil shale (Chakravarty et al., 1988a), are also found in the original oil shale, Figure 2b. Contribution to these peak series can also come from long-chain hydrocarbons believed to be present in King Cannel coal (sample 12) (Meuzelaar et al.,
T2 -1.8 32]
1
DISCRIMINANT FUNCTION DI
4.5
b) A4
L
0
z c
0 0
5
l&
I-
t
2E
8
(1:
0
ln
n /
-2.8
-26
2.j
DISCRIMINANT FUNCTION DIN
Figure 5. Discriminant function score plots of (a) DI vs DII and (b) DIII vs DIV in desorption MS data on all SHRODR pyrolysis liquids.
c) DIP- Q”nt
L
. I
Figure 6. Numerically extracted discriminantspectra of SHRODR pyrolysis liquids. Compare component directions with Figure 5.
1984a), giving rise to its waxy nature. Figure 6c shows the negative component of DIV, reflecting the presence of sulfur-containing compounds (at
2178 Ind. Eng. Chem. Res., Vol. 29, No. 11, 1990
-
2 6-
3 .
a) DI- Component
b) DI+ Compmnt
B
m /z
t
I
4 9
.
,
1.7
-2 5 DISCRIMINANT FUNCTION
DI
Figure 7. Discriminant function score plot (DI vs DII) of reduced desorption MS data set (SHRODR liquids from 19 coals).
m / z 34 and 64) and the peak series at mlz 94, 110, and 124 (also seen in Figure 2c), which are more characteristic of sample 25 (Eastern oil shale; Chakravarty et al., 1988a). Finally, the positive part of DIV (Figure 6d) shows the naphthalene pattern characteristic of the higher (mvb) rank coal (at m/z 142,156,170,184, and 198),in addition to acenaphthenes/biphenyls (at m / z 168, 182, and 196) and phenanthreneslanthracenes (at m / z 178, 192, and 206). To focus attention mainly on the coal tar samples, a reduced data set was created similar in composition to the corresponding reduced data set for the coal samples (19 cases and 171 variables). Three significant discriminant functions, accounting for 47% of the variance in the data, were found. In comparison with the results of the coal data (Figure 3), the tar data in Figure 7 appear to exhibit more within-group variance. In other words, the tar evaporation spectra reproduced less well than the coal pyrolysis spectra, apparently due to variable losses of volatile components during introduction of the tar sample into the mass spectrometer. Figure 7 shows the score plot in DI/DII space. The relative orientation of the different coal tar samples in Figure 7 is rather similar to that of the corresponding coal samples in Figure 3 (when allowing for some rotation and reflection of the discriminant space). The main rank-related trend (although much less pronounced here) appears to coincide roughly with the direction of DI. The corresponding discriminant spectra are shown in Figure 8 and can be compared with the discriminant spectra of the reduced coal data set in Figure 4. The dominant peak series of aromatic hydrocarbons (such as naphthalenes, acenaphthenes/biphenyls, and anthracenes/ phenanthrenes), hydroaromatics and/or acenaphthenes in Figure 8b are mainly characteristic of hvBb and hvAb rank coal tar. The characteristic variables of the two subbituminous (samples 8 and 18) and the two hvCb coals (samples 15 and 19) are primarly phenols (at m / z 94, 108 and 1221, dihydroxybenzenes (at m / z 110,124, and 138),and other hydroxyaromatics, e.g., naphthols (at mlz 144,158) as seen in the spectrum of the negative component of DI (Figure 8a). Both in Figure 3 and in Figure 7 , sample 1 (Ohio No. 6) appears out of place with regard to rank. Since this was
Figure 8. Numerically extracted discriminant spectra representing DI in reduced SHRODR liquid data set (19 coals). Compare with Figure 4.
not a PSOC sample and detailed analytical data on this coal are unavailable, the possibility that this was an hvCb (instead of hvAb) rank coal or, alternatively, a strongly weathered coal sample cannot be ruled out. In summary, the compositional characteristics of the SHRODR tars, especially the similarities and differences, can be directly correlated with those of the parent coals as revealed by vacuum micropyrolysis techniques. However, the within-group variance in the tar sample data set is much higher than that in the solid fuel data sets. This results in relatively poor discrimination between some of the tar samples. Canonical Correlation Results. Since we are interested in assessing the relationship between each solid fuel and its derived liquid, canonical correlation analysis was performed on both reduced (19 sample) data sets from which samples 4, 12, 14,20,22,and 25 had been removed. The reason for selecting this data set for canonical correlation (and later for prediction) is to focus on a smaller, more homogeneously populated region of the data space. This will be clear by comparing the various discriminant score plots (Figures 1, 3, 5, and 7). On the basis of the results of discriminant rotation analysis, four discriminant functions accounting respectively for 55% and 53% of the variance in the coal and the tar data were used to perform canonical correlation analysis. Two significant (correlation coefficients > 0.9) canonical variate functions were obtained. Figure 9 shows the score plot in CVI/CVII space for both the coal and the tar samples. In each category, only the mean scores of the triplicate runs are plotted in order to obtain a less complex score plot. Except for samples 6, 21, and 23, the scores of the coal samples and the corresponding tar samples are quite close to each other; i.e., the average difference between the two scores is less than the uncertainty in the location of the individual scores. This means that in CVI/CVII space the SHRODR tar samples are very similar in composition (with respect to the 171 variables from m / z 90 to 260) to their parent coal samples. Also, it is clear that CVI mostly represents the rank trend whereas CVII is likely to represent differences in depositional environment. This type of data analysis brings out the compositional similarities between the coal and their tars while minimizing the experimental noise (Chakravarty et al., 1988b) in the process. Sample 15, an Illinois No. 6 hvCb coal, was chosen for predicting the SHRODR liquid pattern generated from it because of its location in a well-populated region of the CV space while still lying at quite some distance from the center of the space (and thus being markedly different from the average coal and liquid patterns). The experimental mass spectra of both the coal and its liquid tar are shown in parts a and b of Figurelo, respectively. As might be expected, the experimental spectrum of the SHRODR liquid tar sample (Figure lob) has a very different pattern than that of the parent coal (Figure loa). It should be
Ind. Eng. Chem. Res., Vol. 29, No. 11, 1990 2179
18
60 00
(u
0
-1.4
3.4
IST CANONICAL VARIATE
Figure 9. Score plot of first two canonical variate functions in combined coal pyrolysis MS and SHRODR liquid desorption MS data spaces. Note position of sample 15 (Illinois No. 6).
g 'o~,,'i".,'h*nol$ u) z
a) Pyrolysis Mass Spectrum of Parent Coal 'P2
W
t-
E b) Desorption Mass Spectrum
c) Predicted Mass Spectrum of SHRODR Tar
m/z
Figure 10. (a) Measured pyrolysis mass spectrum of Illinois No. 6 coal; (b) measured desorption mass spectrum of corresponding SHRODR liquid; (c) mathematically predicted mass spectrum of Illinois No. 6 SHRODR liquid (compare with (b)).
remembered that Figure 10a was obtained by vacuum micropyrolysis under conditions that allow near-instantaneous escape of pyrolysis products, thereby effectively minimizing the occurrence of secondary pyrolysis and condensation reactions. Batch autoclave reactors such as used in the SHRODR process, however, inevitably provide opportunities for primary coal pyrolysis products to react further, generally resulting in increased relative abundances of polycondensed aromatic compounds and reduced abundances of more reactive components such as hydroxy aromatics. The first step in predicting the sample 15 tar spectrum is the projection of coal 15 in the canonical variate space by considering it as an unknown sample and the rest as a training set. For both the coal and the tar data set containing 18 samples (without sample 151, the topology of the canonical variate space of CVI/CVII was found to be preserved (Chakravarty et al., 1988b, 1990). In other words, the CVI/CVII score plots for the 18 sample data
set (not shown here) were very similar to Figure 9. Subsequently, the canonical variate space of CVI/CVII was used to project the pyrolysis mass spectrum of Illinois seam coal (sample 15). The resulting spectrum is shown in Figure 1Oc and is quite similar to the experimentally measured SHRODR liquid spectrum in Figure lob. Moreover, the entire procedure can be repeated equally successfully with other coal/liquid pairs from the 19 sample data sets, as has been shown previously for sample 2 (Chakravarty et al., 1988b, 1990). Summary and Conclusions A series of fossil fuels and the corresponding SHRODR pyrolysis liquids prepared by a fixed-bed reactor were analyzed by means of Curie-point low-voltage MS. By using factor analysis and discriminant rotation techniques, 25 fossil fuel samples were compared on the basis of their low-voltage electron ionization MS data and the major underlying structural variables identified. Subsequent canonical correlation analysis revealed the compositional similarities and differences between a reduced set of 19 coal samples and their corresponding "fixed-bed" liquids. The mass spectral patterns of the fixed-bed liquids are shown to be statistically strongly correlated to those of the original coals in spite of major process-related differences. Numerical comparison of compositional data on coals and their corresponding SHRODR pyrolysis liquid enables the construction of empirical mathematical models capable of predicting pyrolysis liquid composition from pyrolysis mass spectrometric data of the parent coal. Although the Cuire-point pyrolysis mass spectra of the coals were composed mainly of primary pyrolysis products typical of vacuum micropyrolysis and thus were substantially different from the low-voltage mass spectra of the corresponding fixed-bed liquids, it proved feasible to model and predict the fixed-bed tar spectra from the vacuum micropyrolysis spectra of coal by means of factor analysis based canonical correlation analysis methods. The results of this study specifically relate the composition of a pyrolysis liquid generated in a particular reactor under a given set of conditions to those of the parent coals using multivariate statistical analysis as a modeling tool. Acknowledgment This work was funded by the Department of Energy through DE-AP21-87 MC05073 and through the Consortium for Fossil Fuel Liquefaction Science (Contract UKRF-4-23576-90-10). Matching funds were provided by the State of Utah. The expert assistance of Barbara Hoesterey in the preparation and analysis of samples and of Melinda Van in preparing the manuscript is gratefully acknowledged. Literature Cited Chakravarty, T.; Windig, W.; Taghizadeh, K.; Meuzelaar, H. L. C. Computer Assisted Interpretation of Pyrolysis Mass Spectra of Two Oil Shales and Their Corresmnding Kerogens. Energy -- Fuels 1988a, 2, 191-196. Chakravartv. T.: Meuzelaar. H. L. C.: Jones. P. R.: Khan. R. Prediction 0; the Composition of Coal Tars from the'Pyrolysis Mass Spectra of the Parent Coals Using Canonical Correlation Techniques. Prepr.-Am. Chem. SOC.,Diu. Fuel Chem. 1988b,33 (2),
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