2504
Anal. Chem. 1985, 57,2504-2510
Infrared Analysis of Low Temperature Ashed Coal Ashes and Their Classification by Application of Clustering Theory Gerard Platbrood* and Henri Barten
Laborelec, Laboratoire Belge de l’lndustrie Electrique, B.P. 11, B-1640 Rhode St. Genese, Belgium
I t Is of major Importance to recognize the orlgin of coal types burnt In power plants. At Laborelec elemental and X-ray diffraction mineral analyses of low temperature ashed (LTA) coal have been made for more than 3 years for the purpose of characterization of the coal In order to prevent foullng troubles In furnaces. The infrared analysis of LTA coal ashes provldes lnterestlng addltlonal lnformatlon on the nature of the mlnerals and extends the coal ash characterlzatlon. Further quantltative determination of phase concentrations was altempted through the use of factor analysls. Unfortunately, crystallinity differences between samples serlously llmlted the quantltatlve analysls. However, classiflcatlon uslng clusterlng theory dlrectly applied to the Infrared and elemental results has glven the best practical results. Cluster analyais has been used to split the coal ashes Into groups Characterized by low and high sintering strengths, an Index of deposit tendency In power-plant bollers.
In 1968 Estep et al. (I) reported that a low-temperature ashing (LTA) technique combined with an infrared analysis in the low-frequency range (up to 200 cm-l) was a valuable tool in coal mineralogical studies. After oxidation of the coal in a radio frequency electromagnetic field, the mineral matter is recovered unaltered. Infrared analysis thus became considerably easier because approximately 90% of organic materials are eliminated and no longer interfere with the infrared absorption of the mineral matter. For more than 3 years, Laborelec has been intensively using X-ray diffraction (XRD) analysis of LTA ashes to quantitatively determine the mineralogical composition of coal ashes, but there are still some analytical problems remaining. Therefore, the combined utilization of elemental, X-ray diffraction, and infrared analysis of LTA coal ashes may completely characterize the studied coals to identify those coal ashes that have a propensity for fouling. Elemental and mineralogical composition data have previously allowed us to find interesting relations between the chemical composition and the fouling properties (2). The relative importance of each constitutent, elemental or mineral phase, in the sintering strength of cylindrical coal ash pellets has thus been established. In 1981, an empirical linear expression relating the sintering strength and the elemental and mineral composition has been tested with good results (2). The seven phases considered as variibles were a-quartz, kaolinite, illite, gypsum, pyrite, calcite, and dolomite and the seven elemental oxides were SiOz, A1203,FezO3, CaO, K,O, MgO, and NazO. After calculation, we observed that illite, gypsum, pyrite, calcite, and some element oxides considered as fluxes, CaO, KzO,and Na20, increased the sintering strengths. In 1983, the same data bank, mineral and elemental composition of 53 coals, was also treated using the following techniques: principal component analysis, clustering, linear discriminant analysis, and multivariate normal distribution models ( 2 , 3 ) . After classification, it was possible to separate the coals according to their origin and their propensity to
fouling. Two groups were characterized by high sintering values: a group of coals with high illite, KzO, and CaO content (Belgium, Germany) and another one with high CaO content (South Africa). In spite of these efforts, some analytical problems remain since in X-ray diffraction quantitative analysis, the sum of the crystallized phases does not always equal 100% (more or less than 100%). This result can be explained in different ways: error in the phase determinations resulting from structure discrepancies between the mineral standards and those of the unknown coal ashes; amorphous phases present; nonuniform particle size distribution in the specimen; strong peak overlapping in the spectra that can be resolved with a program performing good peak deconvolution (4); preferred orientation that can be strongly reduced by spray drying of the sample, provided enough material is available. Consequently, it was found necessary to complete the characterization of coal ashes by another technique such as infrared spectrometry. In their paper Estep et al. (1)briefly reviewed some of the difficulties relative to the infrared quantitative solid state spectrometry, e.g., particle size and sample alteration directly related to the grinding procedure (mill, grinding time, ...), particle distribution requiring a high degree of uniform sample distribution in the matrix, sample preparation, pellet thickness, and choice of matrix, and strong band overlappings impeding a good quantitative analysis using a dispersive instrument. It is also essential to point out that the amorphous phase influences the absorbance and delivers a modified signal, stronger than in X-ray diffraction and more characteristic. Whereas the accuracy level in quantitative infrared spectrometry mineral analysis amounts to &5%or greater, it seems useful to supplement the X-ray diffraction results with infrared spectrometry and elemental analysis. Although elemental and X-ray diffraction mineral analysis are made anyway, this paper will focus on the results that can be derived principally from infrared analysis of coal ashes.
THEORY The absorbances measured on the pelleted samples are defined as log &/I), where I is the intensity of transmitted light and I, is the intensity of incident light. Two different LTA coal ash characterization approaches have been implemented. In the more ambitious approach (factor analysis), it is intended to determine the mineral phase concentrations in LTA coal ashes of very different origins from the infrared spectra. In this case, the base line is substracted and the “true” absorbance determined. The well-known “tangent-line” technique has been employed. The transmitted radiation (I)is measured at the point of maximum absorption and the value of the incident radiation (lo) is obtained by drawing a tangent to the shoulders of the band. In the more pragmatic approach (clustering method), it was essentially intended to characterize and classify LTA coal ashes according to their infrared spectra. It should be noticed the absorbance is used without background correction. Analysis of the Data Bank by Factor Analysis. Starting from a data matrix [D] composed of the infrared absorbances of p coals and w wavenumbers ( p X w ) (in this
0003-2700/85/0357-2504$01.50/00 1985 American Chemical Society
ANALYTICAL CHEMISTRY, VOL. 57, NO. 13, NOVEMBER 1985
case 40),the objective is to calculate the mineral concentrations through factor analysis
A,,
A,,
APW
w wavenumbers The symbol Akk.represents the “true” absorbance associated with the ith row designee and the kth column designee of the matrix. The goals of the factor analysis are the number of absorbing components (factors), the concentration of each component in each mixture, and the spectrum of each component. The first objective of factor analysis is to obtain an abstract solution wherein each point in the data matrix is expressed as a linear sum of product terms (5) i=n
where rkJis the j t h cofactor associated with row designee i, and cJk is the j t h cofactor associated with column designee k . The number of terms in the sum, n, is called the number of factors. An essential characteristic of the abstract solution is that i t denotes the number of factors required for correct repeatability of the data matrix. The procedure for calculating the abstract solution involves, in a first step, a mathematical method called eigenanalysis. A set of eigenvalues and the associated eigenvectors are the results of the, calculation. The first objective is purely mathematical; it is the calculation of an abstract solution represented by a matrix product
(3) where the subscript abs stands for abstract, [D] is the data matrix (p X w),[RIabsis the row matrix (p X n), [c],,, is the column matrix ( n x w),p is the number of coals, w is the number of wavenumbers, and n is the number of factors. The second objective is to convert the abstract solution into a real solution. An appropriate transformation matrix is sought (target testing)
where [TIis the target matrix (n X n). If the transformation succeeds, real matrices, having physical significance, are obtained giving a real solution to the problem
(5) where [RIreal is the molar extinction coefficient matrix ( p X n) and [CIreelis the concentration matrix (n X w). After factor analyzing the data matrix, we seek to discover how many of the n factors are physically important. The abstract factor can be divided into two sets: a primary set of m factors, that account for the real, measurable features of data, and a secondary set of n - m factors, that is associated entirely with experimental error. This theory of errors is thoroughly explained by Malinowski and Howery (5). It also provides an empirical indicator function (IND) that is able to suggest the proper number of factors. The IND is defined as
RE
IND = ( n - m)2 where n is the total number of factors corresponding to the eigenvectors, m is the number of factors representing phys-
2505
ically the studied case with the experimental error, and R E is a measure of the difference between the pure data and the raw experimental data. This function is composed of X j (eigenvalues), r (number of rows in the data matrix), and m and n (ref 5, p 72). By examining the behavior of the IND function as m varies, we can often deduce the true number of factors. A Hierarchical vs. NonhierarchicalClustering Method Using the Program MASLOC. The program MASLOC (6, 7) divides the set of p objects (here 21 coals) in a prechosen number of subsets, called clusters, each object being characterized by a pattern vector of w variables (here 40 wavenumbers). The starting data matrix thus consists of p (rows) X w (columns). The second step consists in calculating the dissimilarity between all p x (p - 1 ) / 2 pairs of objects. In this case the Euclidean distance, E l k , was used as the dissimilarity measure. It is given by j=w
Eik
=(
c (2, - zkj)2)”2
]=1
(7)
where 2, = (Ai, - A,)/s, (2 transformation) for the ith coal and j t h wavenumber and A, is the absorbance of wavenumber j in coal i and A, is the mean absorbance of the entire set of p coals at the wavenumber j
where s, is the standard deviation of the j t h wavenumber. A 2 transformation has been carried out to attribute the same weight to each wavenumber when q clusters are wanted. The q so-called centrotypes are selected among the objects (here the coals) in such a way that the sum of distances between each object and the nearest centrotype is as small as possible. Each object is then located in a cluster with its nearest centrotype. The MASLOC program computes all clusterings from 1 to p and selects among the obtained clusterings those clusters that are significant or robust. An Agglomerative Hierarchical Clustering by Single Linkage. The program is based on a theory developed by Anderberg (8). Using this clustering method, those two entities (coal ashes), having the smallest Euclidean distance are linked together step by step. In building clusters, there are three possibilities: when two entities to be linked are singletons, a new cluster is formed; when a singleton and an entity included in a cluster are linked, a larger cluster is formed; when two entities included in different clusters are linked, a larger cluster is also produced. Proceeding in this way, a tree, called a dendrogram (81, is built, the hierarchical branches going from branches to root. This clustering uses the same dissimilarity measure based on the 2-transformed data.
EXPERIMENTAL SECTION Preparation of the Coal Ashes. Ground coal samples (particlesize e149 pm) were asked in a Tracerlab Model LTA-500 low-temperature asher. The coal is oxidized in a stream of excited oxygen produced by a radio frequency electromagneticfield. Four samples, approximately one gram of each coal, are oxidized together. Every 8 h, the oxidation process is interrupted and in order to break the grains, the coals are slightly ground manually in a mortar to achieve a complete oxidation of the organic matter. Generally, 24 h are required to oxidize the organic matter. IR Pellet Preparation. The CsI pellets have to be prepared with caution in order to achieve the highest possible repeatability and accuracy. First, 50 mg of LTA coal ash is mixed with 450 mg of CsI. Then, this mixture is diluted by an amount of CsI in order to reach a percentage of 0.3% of LTA coal ash in the
2506
ANALYTICAL CHEMISTRY, VOL. 57, NO. 13, NOVEMBER 1985 2.0 W
y
8
1.8
4
1.6
m
a I.L 1.2 1.0
0.8 0.6
0.L 0.2
0.0
2 4 Kao
L
6
10 12 1L 16 18 20 22 2L 26 28 30 32 3L 36 38 LO 4 4 4 4 4 co 3 Kao+III Dol Gyp Si1 Cal PEAK Nbr.
8
4
Flgure 1. Absolute mode comparison: South African (sample 6) and Australian coals (sample 4): KaO, kaolinite; COB,carbonate, calcite, and dolomite; Ill, illite; Dol, dolomite; Gyp, gypsum; Sil, a-quartz; Cal, calcite; sample 6, (-) sample 4. (9)
pellet. The pellets have thus the following characteristics: weight of LTA coal ash in the CsI pellet, 0.3%; pellet weight, 200 mg; pellet diameter, 10 mm. A wet mixing (methanol) of CsI and coal ash is carried out carefully in order to achieve the best pellet homogeneity. Sintering Test. For the sintering test, coal ashes (ignition loss of the coal occurs at 500 "C) me pressed into cylindrical pellets by means of a powder press until pressure remains constant (70 MPa). After 1 h of heating the pellets in the furnace at four different temperatures (800 "C, 900 "C, 1000 "C, 1050 "C), the sintering strength was determined. The interpolated value at 950 "C was used as the fouling index. For each sample, three pellets are crushed at each temperature in order to average the results of the strength measurements (MPa). Program. A program based on an agglomerative hierarchical clustering by single linkage written in BASIC for a Tektronix Model 4051 computer enables clustering of up to 72 entities. The dendrograms however have to be drawn manually. The program MASLOC runs on a SEL 32 computer (System Engineering Laboratory, language Fortran 77+).
RESULTS AND DISCUSSION Problems Occurring during the Grinding. The LTA coal ashes are not ground but used as they are. The grinding process modifies the particle size distribution and changes the crystallinity of kaolinite by delamination (9). The evolution of the coal infrared spectrum vs. time also changes differently when coals of different origin are ground. In contrast to kaolinite, the carbonate absorbance increases with the grinding time. The CsI pellet repeatability estimated from the infrared absorbance is about 2.7% for 1 standard deviation. Preparation of LTA coal ashes for infrared analysis purposes thus raises a severe problem since grinding alters the crystallinity of such silicates as kaolinite, whereas absence of grinding might generate extremely large particles and prevent the display of an optimum absorption spectrum (IO). Infrared spectra therefore will be considered as fingerprints of the coal ashes after oxidation of organic matter. Comparison of Infrared Spectra. By use of our Perkin-Elmer 580 infrared spectrometer, a coal ash infrared spectrum is recorded in the 4000-200 cm-I range. However, the characteristic range of coal ash absorption is limited (3800-3000 cm-l and 1700-180 cm-l). Since the infrared spectrometer is not interfaced with the computer and only some bands are characteristic of minerals, some wavenumbers (40 in this case) corresponding to these maximum charac-
teristics for each phase were taken into account (3700,3620, 3550,3410,1620,1440,1430,1420,1410,1160,1120,1105,1085, 1035,1010,940,915,883,880,800,780,755,732,715,700,670, 600, 540, 510,470,460, 435, 400, 370, 365, 350, 315, 280, 264, 230 cm-l). Aware once more of the changes occurring in the crystallinity and the particule size distribution between different samples, the infrared spectra of the data bank should essentially be seen as fingerprints of unground coal ashes. In a first approach, each infrared spectrum can be compared with another using the computer display. The absorbances are displayed equidistantly and represent the infrared spectrum in a summarized form (Figure 1,absolute mode). Different procedures were used to compare the infrared spectra. The simplest one uses the sum of the absolute absorbance differences of two spectra for each wavenumber. Although particular care has been taken during the CsI pellet preparation, different degrees of opacity were encountered, which often leads to a shift and/or a deformation of the base line. A method to determine kaolinite can be based on a curve fitting procedure allowing the estimation of the base line (II), but this method only applies to one-phase determinations and appropriately characterized samples. In order to reduce this base line shift, we have adopted in this paper another way to represent the data: each point can now be defined as the difference between two successive absorbances. A relative factor EQlk is defined for each spectra comparison j=40
EQ12
=
C I(A1, - A16-1)) - (AzJ- Azg-l))I
3=2
(9)
where A,] is the absorbance of the ith coal for the j t h wavenumber, Ai,is the absorbance difference of the ith coal ash for two successive wavenumbers, and EQ12is the relative factor calculated for coal ash 1and 2. This relative factor EQ; is small when two coal ash infrared spectra are similar. The data resulting from the difference between two successive absorbances can also be represented on the display (Figure 2). Each time the comparison of two spectra is carried out, the relative factor based on eq 9 is printed. Analysis of the Data Bank by Factor Analysis. The 21 LTA coal ashes have been compared to each other in order to determine a-quartz, kaolinite, illite, gypsum, calcite, or/and dolomite. In the factor analysis of these 21 samples, Figure 3 suggests that only four components of the mixture can be
ANALYTICAL CHEMISTRY, VOL. 57, NO. 13, NOVEMBER 1985
Table I. Synthetic Mixture Analysis by Application of Factor Analysis
12
5
10
I
08
a
06
sample
%
%
OL
no
real
found
1 2 3 4 5 6
15.0 12.9 12.9 17.1 10.3 4.9
15.3 14.0 14.2 18.5 10.9 5.3
a w
y
W
U.
00
y
-02
z
2
a-quartz
02
IL
-0L
-06
m
a
2507
calcite 70
gypsum %
%
real
70 found
real
found
3.3 2.9 4.3 2.9 3.9 4.6
3.3 3.1 4.7 3.1 4.1 4.5
5.0 8.6 4.3 4.3 7.9 14.8
3.9 7.1 3.7 3.5 6.4 11.6
-08
-12
kaolinite
a-quartz
-10
,
I 2
L
6
8
10 12 1L 16 18 20 22 2L 26 28 30 32 3L 36 38
J
Figure 2. Relative mode comparison of South African (sample 6) and Australian coals (sample 4): (-) sample 6; (-) sample 4.
I
I05
illite
sample
%
%
%
%
%
%
no
real
found
real
found
real
found
1 2 3 4 5 6
4.8 9.1 16.7 23.1 9.1 4.8
5.3 6.2
4.8 9.1 12.5 15.4 22.7 28.6
4.6 8.5 11.6 15.1 23.0 29.2
28.6 40.1 16.7 11.5 9.1 4.8
34.6 42.9 20.0 11.2 10.6 2.2
__
18.0
23.5 10.2 6.7
- 22
*
8
2 20
s6
F
03 0.2
k I
l ! l l l l l l l l I I
18
16
z LL 0
1L
9 distinguished. Considering these 21 coals samples, four targets represented by the matrix [TI including the t (absorption coefficients) for kaolinite, illite,quartz, and calcite are selected. The concentrations obtained from calculation are found to be meaningless. This result corroborates that good quantitative analysis by infrared analysis is impossible since the discrepancies between the standard minerals and the minerals actually found in the samples are considerable. Nevertheless, this factor analysis gives good results when different synthetic mixtures are prepared and analyzed. Table I includes the results obtained after a full application of factor analysis using the molar extinction coefficients as targets. The factor indicator function shows an effective minimum for the threecomponent mixture: a-quartz, calcite, and gypsum (Figure 4). Similar good results were achieved when a mixture of a-quartzf kaolinitefillite was analyzed. Because of the poor results of factor analysis in the determination of mineral concentrations in real samples, the approach applying clustering has been used to characterize and to classify the coal ashes. The brief discussion below concerns the two clustering techniques implemented in order to correlate the infrared spectra, depending on phases concentrations, and the fouling and slagging properties of coal ashes. Application of Clustering. The 21 coals of different origins, South Africa, Canada, Germany, Australia, and Belgium, have been selected from 72 coals burnt in the Belgian powerplant boilers. The chemical and physical characteristics of some of the studied coals are presented in Table I1 (origin of the sample, chemical analysis, and sintering strength (2)
12
10
e 6
L
2
0
1
1 2
'
1 L
6
FACTOR Nbr
Flgure 4. Factor indlcator function (IND) vs. the factor number (syn-
thetic mixture). a t 950 OC,expressed in Mpa). The classification methods discussed above have been applied, with generally similar results. Table I11 gives the clusters identified from the classification with the program MASLOC (6, 7)carried out from two to six clusters. In Figure 5, a classification is carried out using an agglomerative hierarchical clustering by single linkage. The absorbances and the absorbance average calculated for each group that is determined through applications of the program MASLOC are presented in Table IV.
2508
ANALYTICAL CHEMISTRY, VOL. 57, NO. 13, NOVEMBER 1985
Table 11. Chemical and Physical Characteristics of the Studied Coals no. and origin of samples
Si02
A1,0,
Fe203
chemical analysis CaO SO3
%
%
%
%
2 South Africa 10 South Africa 6 South Africa 16 South Africa 14 South Africa 1 South Africa 5 South Africa 18 Canada 13 South Africa 7 Belgium (Zolder) 8 Germany 9 Belgium (Zolder) 12 Belgium (coal tip) 19 Germany 20 Germany 17 Belgium (Zolder) 11 Belgium (coal tip) 15 America 3 unknown 4 Australia
44 41.3 45 45 47 42.5 49.2 51.5 46 54.3 50 54.4 52 51 53 52.1 52 52 53.7 60
31 31.4 30 32 31 30.2 31.9 33.9 27 25.3 26 26.4 30 25.7 30 25.1 31 28.8 34.1 33
4.3 4.9 2.8 3.7 3.6 8.1 4.1 4.5 3.3 6.6 6.5 6 5.8 8.7 6.1 6.3 4.7 10.3 3.8 0.7
8.4 9.3 10.2 7.9 7.4 7.4 4.5 2.08 11.3 3.9 4 2.7 1.1 2.72 1.7 4.2 2.4 0.7 0.52 0.2
KzO
MgO
NazO
%
%
%
%
S.S.," MPa (950 "C)
4.2 4.05 4.45 3.88 4.98 4.3 3.55 0.7 4.1 2.15 1.93 0.98 0.8 1.35 1.58 1.7 1.08 0.8 1.04 0.21
0.43 0.54 0.69 0.82 0.35 0.73 0.39 0.49 0.65 3.86 4 4.2 4.44 3.95 3.95 4.1 5.1 2.6 1.36 0.38
1.4 1.17 2 1.8 1.6 1.5 0.50 0.11 2.8 1.11 1.3 1.2 1.3 1.00 1.3 2.2 2 0.9 0.46 0.03
0.1 0.20 0.25 0.23 0.15 0.14 0.24 0.1 0.95 1.58 0.65 0.52 0.13 0.85 0.64 0.46 0.49 0.3 0.26 0.10
12.1 40.4 45.7 20.1 9.4 8 7.3 19 50.8 46.9 46.7 41.2 54.7 24.5 12.4 37.2 54.3 19.6 13.5 9
Sintering strength (2) expressed in megapascal for a temperature of 950 O C . Table 111. Coal Ash Classification from Two to S i x Clufiters by the Program MASLOC
SOUTH AFRICA SOUTH AFRICA
n 1 2 6 10 13 14 16 5 1 8 3
SOUTH AFRICA 7 8 9 12 1 7 19 2 1 4
11 15 20
2
SOUTH AFRICA
16
A
SOUTH AFRICA SOUTH AFRICA SOUTH AFRICA CANADA SOUTH AFRICA BELGIUM I ZOLDER 1 GERMANY BELGI'JM IZOLDERI BELGIUM (COAL TIP)
Examination of Tables I1 and IV for comparison purposes raises some general remarks. When the number of different coal types considered is large, infrared analysis essentially provides information on the geographical origin of the coals by comparison of coal fingerprints. If further information is required regarding the propensity of coal ashes to sinter or to cause fouling or slagging, elemental analysis has to be added. The crystallinity of kaolinite differs strongly in the six groups formed by the program MASLOC. The ratio A3630cm-~/A370Scm-i, related to the crystallinity (9),varies according to the group, but keeps approximately the same value within each cluster. The group G4 has the most delaminated kaolinite in contrast to group GI. Thus the absorbance ratio also seems to be an excellent parameter to distinguish coal ashes. Coals 5 and 18 differ from coals 1, 2,6, 10, 13, 14, and 16 in their carbonate absorbance. Group G2has a lower carbonate content than group GI. This result is also confirmed by the elemental analysis (CaO values in Table 11). The groups GI and G2,including principally South African coals, have a high CaSOI content. Illite and a-quartz show peaks overlapping the other phases, e.g., kaolinite. This overlap prevents a quantitative estimation of the phases,
GERMANY
19
GERMANY BELGIUM
20 21
BELGIUM IZOLDER)
17
-
-
BELGIUM (COAL T I P ) AMERICA - -- -- - -
_ _ - -- - - s I
/I
UNKNOWN - - -- --- --- - - -
AUSTRALIA
Figure 5. Classification of coals by the clustering theory.
especially when their crystallinity changes to that extent. Illite plays a role in the sintering strength via its high alkali content. Consequently the elemental analysis also has to be taken into account when the propensity for coal ash fouling has to be determined. Two elements to be considered are certainly Ca (calcite, gypsum) and alkali, namely, K. It should be noticed that South African coals have a large CaO distribution (3 14%). Table V shows another presentation of results based on the high and low sintering strengths. For the South African coals when the CaO content exceeds 9%, the sintering strength is generally high (>35 Mpa). For European coal and especially Belgian coal, high sintering
-
ANALYTICAL CHEMISTRY, VOL. 57, NO. 13, NOVEMBER 1985
Table IV. Characteristic Absorbance and Wavenumbers of the Coal Infrared Spectra after Cluster Classification by
2509
MASLOC
carbonate
a-quartz
do1omite
(600
COS (1410
(780
(732
cm-')
cm-')
cm-')
cm-')
illite (510 cm-')
0.6523 0.6234 0.6066 0.6051 0.6829 0.8235 0.5688
0.3335 0.3565 0.3615 0.3615 0.3468 0.3468 0.3615
0.3354 0.3036 0.3565 0.3279 0.3615 0.3098 0.3152
0.2090 0.2076 0.2147 0.2111 0.2403 0.2255 0.2255
0.2013 0.1918 0.2007 0.2076 0.2090 0.1938 0.2111
1.0757 1.0555 1.0757 0.9586 1.0757 1.0969
0.3288 0.1249 0.1221 0.1261 0.1397 0.1146 0.1278
0.4851
0.6519
0.3526
0.3300
0.2101
0.2022
1.0483
0.1549
0.8894
0.5287
0.5944
0.3706 0.3716
0.2204 0.1938
0.2366 0.2396
0.1938 0.1871
1.1427 1.1549
0.1057 0.1046
18
0.8327
0.4789
0.5751
av G2 3 G3 7 8 9 12 17 19 21
0.8611 1.1135
0.5038 0.5850
0.5848 0.5254
0.3711 0.3737
0.2071 0.1152
0.2381 0.2182
0.1905' 0.1838
1.1488 1.3372
0.1052 0.0862
0.3188 0.3188 0.3010 0.2581 0.3054 0.3429 0.3098
0.3757 0.3686 0.3487 0.3107 0.3468 0.3556 0.3335
1.1785 1.1562 1.1585 1.2038 1.1356 1.0370 1.0765
0.2716 0.2596 0.2457 0.2757 0.2596 0.2840 0.2565
0.2336 0.2366 0.2262 0.1739 0.2526 0.2007 0.2240
0.2441 0.2291 0.2411 0.2218 0.2366 0.2381 0.1904
0.1759 0.1739 0.1675 0.1805 0.1772 0.1838 0.1643
1.1549 1.1739 1.1675 1.1871 1.1024 1.0706 1.1427
0.0862 0.0835 0.0706 0.0605 0.0862 0.0872 0.0820
av G4
0.3078
0.3485
1.1352
0.2647
0.2211
0.2287
0.1747
1.1427
0.0795
4 G5 11 15 20
0.9281
0.5100
0.5495
0.2798
0.1752
0.4202
0.1549
1.3979
0.0555
0.2328 0.5498 0.3107
0.3098 0.4271 0.3615
1.3308 0.7768 1.1635
0.2798 0.3206 0.3054
0.1918 0.2007 0.2062
0.2175 0.2480 0.2218
0.2007 0.1938 0.2034
1.0458 1.0605 1.1549
0.0830 0.0862 0.0862
av G6
0.3644
0.3661
1.0904
0.3019
0.1996
0.2291
0.1993
1.0871
0.0851
ratio kaolinite (3705 cm-')
kaolinite (3630 cm-')
3630f 3705
1 2 6 10 13 14 16
0.7282 0.7852 0.7959 0.8268 0.6676 0.5784 0.9066
0.4750 0.4895 0.4828 0.5005 0.4559 0.4763 0.5157
av G 1
0.7555
5
no
gypsum
calcite
1
(230
cm-')
Table V. Coal Ashes Classification Based on the Results Coming from Elemental and Infrared Analysis origin South Africa South Africa Canada unknown European
Australia Belgium (coal tip) America Germany
coal sample 6 10 13
high strength (>35 MPa) cluster no. % CaO 1 1 1
7 8 9 12 17 21
4 4 4 4 4
11
6
4
low strength (35 Mpa) are observed when the illite content is high (between 40 and BO%), KzO is >4.1%, and CaO is >1.75%. These results are confirmed when XRD and elemental results are also considered (2). Coal 19 (no. 4)has a sintering strength rather high although it is situated in the low sintering strength group. The Belgium coal tip samples (coal samples 11and 12) have high sintering strengths because of the high KzO content. It is noted that the results derived from the infrared and the elemental analysis are necessary to class the coal ashes in two subgroups: high strength and low strength.
CONCLUSION Thus the infrared spectrum of coal ashes can provide good complementary information to the elemental or XRD mineral analysis. In Laborelec, each coal has its ash characterized by
% K20
the elemental, mineral (infrared and X-ray diffraction) analysis, and the sintering strength. All these results constitute a data bank that is periodically updated. Each coal ash is already sufficiently characterized to recognize any coal ash submitted for analysis. If the elemental composition and the infrared mineral analysis are considered together, it is possible to distinguish the coal ashes characterized by a high sintering strength. The two clustering methods used here have similarly separated the coal ashes in logical groups based on the absorbances at 40 wavenumbers. It might be interesting to increase the number of wavenumbers in the digitalized spectrum in order to achieve better spectral comparisons. As the particle size influences considerably the infrared spectrum, more information on this point will be useful. For example, a scanning electron microscope analysis coupled with an energy dispersive
2510
Anal. Chem. 1985, 57, 2510-2516
X-ray system should enable us to determine the particle size distribution for each phase. The results obtained will certainly improve the comprehension of the peak shape in infrared analysis for the different minerals. A relation has already been found between the complete particle size distribution and the corresponding infrared spectrum of a-quartz (12). In the future, we hope to extend the relationship to a mixture of different mineral phases. In practice as a result of these analyses, the power plants are better informed on the coals burnt than would be possible with only the elemental analysis. Registry No. Kaolinite, 1318-74-7; gypsum, 13397-24-5; aquartz, 14808-60-7;dolomite, 16389-88-1;illite, 12173-60-3;calcite, 13397-26-7.
LITERATURE CITED (1) Estep, P. A.; Kovach, J. J.; Karr, C., Jr. Anal. Cbem. 1988, 4 0 , 358-363. (2) Randoux, M.; Platbrood, G. Paper presented at the ninth International
Conference on Modern Power Stations, Conference organized by AIM
(Association des Ingenleurs electrictiens sortis de I’institut Montefiore), Liege, October 7-11, 1985. (3) Detaevernier, M. R.; Platbrood, G.; Derde, M. P.; Massart, D. L. J .
Insf. Energy 1985, 5 8 , 24-30.
(4) Platbrood, G.; Quitln, J. M.; Barten, H. Adv. X-Ray Anal. 1982, 2 5 ,
261-265. ( 5 ) Malinowski, E. R.; Howery, D. G. “Factor Analysis in Chemistry”; Wiley, New York, 1980; Chapter 3, p 23. (6) Massart, L. D.; Kaufrnan, L. “The Interpretation of Analytical Chemical Data by the Use of Cluster Analysis”; Wley: New York, 1980. (7) Massart, D. L.; Kaufman, L.; Esbensen, K. H. Anal. Cbem. 1982, 5 4 , 91 1-9 17. (8) Anderberg, M. R. “Cluster Analysis for Appllcations“; Academic Press: 1973; Chapter 6, p 131. (9) Hlavay, J.; Jonas, K.; Elek, S . ; Inczedy, J. Clays Clay Miner. 1977, 25, 451-456. (10) Otvos, J. W.; Stone, H.; Haro, W. R . Specfrochlm. Acta 1957, 9 , 148-145. (11) Painter, P. C.; Snyder, R. W.; Youtcheff, J.; Given, P. H.; Gong, H.; Suhr, N. Fuel 1980, 59, 364-365. (12) Platbrood, G.; Laire, C.; Barten, H. Paper presented at the Colloquium Spectroscopicum Internationale XXIV, Garrnisch-Partenkirschen, 15-21 September 1985.
RECENED for review February 4,1985. Accepted June 3,1985.
Toward Automated Assignment of Nuclear Magnetic Resonance Spectra: Pattern Recognition in Two-Dimensional Correlation Spectra Peter Pfandler, Geoffrey Bodenhausen,’ Beat U. Meier, and R. R. Ernst*
Laboratorium fur Physikalische Chemie, Eidgenossische Technische Hochschule, 8092 Zurich, Switzerland
Computer analysis of the multlplet structure of cross peaks In phase-sensitlve two-dimensional (2D) NMR correlation spectra allows one to trace out networks of coupled splns, to measure the magnitudes and signs of the scalar coupllng constants, and to determlne the number of rnagnetlcally equivalent spins at each site. Appllcations to mlxtures of small molecules show that pattern recognltlon Is feaslble even If the slgnal-to-nolse ratlo Is low, If the multlplets are barely resolved, or If the patterns are partly dlsgulsed because of accidentally overlapping cross peaks.
High-resolution nuclear magnetic resonance (NMR) spectra of coupled spins such as protons in isotropic solution lend themselves to a rigorous theoretical analysis. Although onedimensional (1D) spectra can be analyzed with programs such as LAOCOON (1,2) if the number of spins in the system is known, it is far from trivial to interpret spectra of mixtures, particularly if one has no prior knowledge of the type of spin systems contained in the mixture (number of spins and magnetically equivalent groups). It is necessary to identify pairs of coupled nuclei with the aid of double resonance or two-dimensional (2D) spectroscopy and it is essential to distinguish multiplets from accidental juxtapositions of chemically shifted singlets. The analysis is facilitated if the system under investigation is composed of known “building blocks”, such as amino acids in proteins or nucleotides in nucleic acids. In such systems, it is possible to search for characteristic patterns in 2D spectra by comparison with a library (3). However, automated analysis should not be limited *Current address: I n s t i t u t de Chimie Organique, Universit6 de Lausanne, rue de l a Barre 2, 1005 Lausanne, Switzerland. 0003-2700/85/0357-25 10$01.50/0
to such systems, and mixtures of compounds with extended networks of coupled protons (steroids, porphyrines, etc.) should also be amenable to interpretation. Two-dimensional correlation spectroscopy (COSY) and related methods (4-9)have become established techniques for investigating complex systems. Much of the current research on proteins and nucleic acids relies on 2D NMR spectroscopy (10-16). The information content of 2D spectra is sufficiently high that most of the ambiguities inherent in one-dimensional spectra can be avoided. The distinction of multiplets and chemically shifted lines, for example, is rather straightforward in 2D spectra. Such spectra are amenable to a logical step-by-step analysis and are therefore suitable for the application of computer procedures. The processing of 2D spectra has much in common with standard image processing applied in the image sciences (17-22) to enhance sensitivity or contrast and to bring out characteristic features. As long as linear processes are applied, the specific origin of the 2D data is not relevant. However, as soon as the logical structure of an image is to be analyzed by nonlinear techniques,the distinctive properties of the image become important. In fact, we have found few similarities between 2D NMR spectra and the type of images that are normally considered in other applications of pattern recognition. Two-dimensional spectra fulfill some rather unusual symmetry properties, as discussed below. Furthermore, it is possible to “tailor” 2D spectra by defining suitable pulse sequences designed to yield characteristic patterns. Thus the experimentalist has more freedom to generate “images” of the spin systems than in most other situations. The reduction from patterns to spin systems involves an understanding of coherence transfer (9) and of the connectivity of spectral transitions. Our approach, which has evolved from a procedure described in a preliminary paper (23),combines features 0 1985 Amerlcan Chemlcal Society
