&EFK
United States Industrial Environmental Research EPA-600/7-80-106
Environmental Protection Laboratory May 1980
Agency Research Triangle Park NC 27711
Physical and Chemical
Characterization of Coal
Interagency
Energy/Environment
R&D Program Report
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EPA-600/7-80-106
May 1980
Physical and Chemical
Characterization of Coal
by
D.G. Hamblen, P.R. Solomon, and R.H. Hobbs
United Technologies Research Center
East Hartford, Connecticut 06108
Contract No. 68-02-3116
Program Element No. INE624
EPA Project Officer: Frank E. Briden
Industrial Environmental Research Laboratory
Office of Environmental Engineering and Technology
Research Triangle Park, NC 27711
Prepared for
U.S. ENVIRONMENTAL PROTECTION AGENCY
Office of Research and Development
Washington, DC 20460
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DISCLAIMER
This report has been reviewed by the Industrial and Environmental Research
Laboratory, U.S. Environmental Protection Agency, and approved for publication.
Approval does not signify that the contents necessarily reflect the views and
policies of the U.S. Environmental Protection Agency, nor does mention of trade
names or commercial products constitute endorsement or recommendation for use.
ii
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FOREWORD
Under Contract 68-02-3116 sponsored by the Environmental Protection Agency,
the United Technologies Research Center (UTRC) is conducting a program to
develop several new automated coal analysis procedures employing a scanning
electron microprobe. The program is directed at the growing need for more
accurate and lower cost coal characterization methods precipitated by environ-
mental considerations and increased emphasis on coal conversion. The program
is divided into two main tasks. Under Task I, the new techniques of sample
preparation, data aquisitions, and data analysis are developed. In Task II,
the procedures developed in Task I are evaluated for speed, accuracy and
precision.
ill
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ABSTRACT
The MASC (Microprobe Analysis of in Sulfur Coal) procedure, which uses a
statistical analysis of the spatial distribution of sulfur and iron in coal to
determine the forms of sulfur (organic, pyritic, and sulfatic) has been
refined to account for the effects of mineral particle size, and the presence
of iron oxides, in order to obtain better quantitative estimates for the
mineral sulfur forms. These refinements include grinding the coal fine enough
that the particle size effects are eliminated, pressing the resulting powder
to provide a uniform smooth pellet, and adding the analysis of additional
elements to allow the determination of the clay constituents. Using the
procedures developed in this program it is possible to obtain measurements for
the sulfur forms and total sulfur which are reproducible to 0.1 wt. % and are
within 0.5 wt. % of the ASTM measurements for the forms and 0.25 wt. % for the
total sulfur. In addition, the pyrite stoichiometry is obtained (x in the
formula FeS , reproducible to 5%). Also obtained from the analysis is the
A
determination of the major inorganics (Al, Si, Ca, Mg, K, Ti), accurate and
reproducible to 5% of the measurement or 0.2 wt. %, whichever is greater.
Estimates of the pyrite particle size for two coals are obtained from the
spatial distribution of iron and sulfur in samples which were ground to 40
mesh top size, and these results are compared with washability studies for two
coals.
The time required for all of these measurements is less than 15 minutes,
and the procedure uses only a 200 mg sample of the coal.
In addition, the use of a commercial elemental analysis is evaluated for
use in the determination of nitrogen in coal, and found to give results
accurate and reproducible to within 3% of the measurement.
iv
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Physical and Chemical Characterization of Coal
TABLE OF CONTENTS
Page
SUMMARY . . 1
I. SULFUR FORMS 4
A. Introduction and Background 4
B. Experiment Procedures 4
C. Data Acquisition 6
D. Data Reduction 8
E. Discussion of Reproducibility 11
F. Discussion of Results 12
II. PYRITE PARTICLE SIZE ANALYSIS 19
A. Introduction 19
B. Theory 19
C. Results with Simulated Data 21
D. Results for Coals 27
E. Comparison with Washability Data 35
III. Task IV - TOTAL NITROGEN CONCENTRATION 38
IV. MAJOR INORGANICS 43
V. MASC RESULTS ON EPA SUPPLIED COALS 48
REFERENCES 59
APPENDIX A - CORRELATIONS AND EIGENVECTORS FOR CALIBRATION
COALS 60
APPENDIX B - CORRELATIONS AND EIGENVECTORS FOR WASHED COALS 78
APPENDIX C - RAW DATA FOR 330 AND 308 FLOAT COALS 96
APPENDIX D - CORRELATIONS AND EIGENVECTORS FOR EPA-SUPPLIED
COALS 145
APPENDIX E - DESCRIPTION OF COMPUTER PROGRAMS 153
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LIST OF FIGURES
Figure No.
1-1 Example of MASC Plot 5
1-2 Correlation of Sulfur Forms 14
\
1-3 Comparison of MASC for Total Sulfur 15
1-4 Comparison of Organic Sulfur Measurements 16
1-5 Comparison of Mineral Sulfur Measurements 17
U~l Numerically Generated Distribution Compared to the
Coal Data 23
II-2 Examples of Monte Carlo Distributions Fitted by
Particle Size Program 24
II-3 Example of Monte Carlo Distribution Fit by
Particle Size Program 26
11-4 ISGS Coal Fitted by Particle Size Program 28
11-5 ISGS Coal Analysis with Photomicrographs 30
II-6 Fitted Pyrite Distribution from ISGS Coal 31
II-7 PSOC 308 and 330 Pyrite Particle Size
Analysis 33
II-8 PSOC 308 and 330 Coals Fitted for Pyrite Particle Size . 34
II-9 Washability Study on PSOC 330 36
11-10 Washability Study on PSOC 308 37
I
IV-1 Comparison of MASC and ASTM Ash 44
IV-2 PSOC 212 Coal with 10% Kaolin 45
IV-3 PSOC 212 Coal with 10% Illite 46
IV-4 PSOC 212 Coal with 10% Montmorillonite 47
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LIST OF FIGURES (Cont'd)
Figure No.
V-l 1632A Coal MASC Plot 51
V-2 1635 Coal MASC Plot 52
V-3 PHS 408 Coal MASC Plot 53
V-4 PHS 506 Coal MASC Plot 54
V-5 PHS 534 Coal MASC Plot 55
V-6 PHS 546 Coal MASC Plot 56
V-7 PHS 578 Coal MASC Plot 57
vii
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LIST OF TABLES
Table No..
1-1 Reproducibility of Sulfur Forms for ISGS #72 Coal ... 11
1-2 Plot Symbols for Coals 13
II-l Examples of MAPS Fit to Monte Carlo Data from
Figure 11-2 25
11-2 Average Particle Radius; Versus Average Particle
Signal 29
III-l Elemental Analysis of ROSA Coal (Dry) 40
III-2 Nitrogen Measurements , 41
III-3 Elemental Analysis of Model Compounds 42
V-l Sulfur Forms for EPA-Supplied Coals 49
V-2 Mineral Mutler Analyses for EPS Supplied Coals .... 50
viii
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SUMMARY
Under Contract 68-02-3116 sponsored by the Environmental Protection
Agency, the United Technologies Research Center (UTRC) is conducting a program
to develop several new automated coal analysis procedures employing a scanning
electron microprobe. The program is directed at the growing need for more
accurate and lower cost coal characterization methods precipitated by environ-
mental considerations and increased emphasis on coal conversion. The program
is divided into two main tasks. Under Task I, the new techniques of sample
preparation, data acquisitions, and data analysis are developed. In Task II,
the procedures developed in Task I are evaluated for speed, accuracy and
precision.
Limitations on the acceptable levels for S09 and NCL. emissions from
£ X
power plants make the direct burning of 80 percent of our eastern and mid-
western coals (those most accessible to the majority of power plants) unlaw-
ful. Future restrictions on other emissions from major and minor mineral
components may further restrict the use of coal. The development and imple-
mentation of coal cleaning procedures requires extensive characterization of
feed coals and the products of the cleaning process, but many of the methods
for characterization are cumbersome and inaccurate. New methods for the
analysis of sulfur forms and the determination of pyrite particle size distri-
bution are needed. The ASTM sulfur forms analysis (Ref. 1) is time consuming
and costly. In addition, recent studies using the Mossbauer technique have
shown up to 50 percent error in the pyrite determination in some coals (Ref.
2, 3). The organic sulfur which is determined by difference suffers corre-
sponding errors. Development of coal analysis procedures which could improve
accuracy and cut cost and time would be highly desirable. In addition, the
development of rapid and inexpensive methods to determine the size distribu-
tion of pyrite and other minerals would be valuable for predicting the effec-
tiveness of mechanical cleaning procedures (Ref. 4).
Analysis of the organic sulfur concentration, S(0), in coal is difficult
because the organic material is intimately mixed with inorganic compounds
of sulfur. The standard ASTM method (Ref. 1) which calls for the determina-
tion of S(0) by subtracting the sulfate and sulfide contribution from the
total sulfur is complicated, and often inaccurate. The MASC (Microprobe
Analyis of Sulfur and Coal) method for the direct determination of S(0)
eliminates these deficiencies and is applicable to coal chars as well. In
addition, the elemental ratio X of iron sulfide compounds, FeSx, is also
obtained. By including other elements in the analysis, the MASC procedure
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also determines the major mineral concentrations as well. Other advantages of
the method include: the ability to use small samples, low cost, speed (a
complete analysis is currently being performed in approximately 10 minutes)
and the option of repeating results since the method is nondestructive.
In brief, the method is based on the differences in the spatial distri-
bution of mineral sulfur (clustered distribution) and organic sulfur (relative-
ly uniform distribution). The method uses a scanning electron microscope to
measure the spatial distributions of a number of elements of interest in
addition to iron and sulfur. The x-ray intensities for a large number of
subsamples are measured. Considering just the iron and sulfur intensities as
a first approximation if we plot these intensities in a scatter plot of iron
versus sulfur, it will be seen that the data lie along a line. The intercept
of this line on the sulfur axis represents sulfur which has no iron, i.e.,
non-pyrite sulfur; and the line has a slope which is indicative of the stoichi-
ometry of the pyrite. In addition, the average of the iron data represents
the amount of pyrite present. In this study, we have measured the spatial
distributions for five elements (S, Fe, Si, Al, Ca) in most cases. These
additional elements are needed to correct the first approximation results from
the iron and sulfur alone.
By analyzing the correlations in these spatial distributions we extract
the following information:
1. Organic sulfur - within 0.5 wt. % of ASTM measurements, reproducible
to 0.1%.
2. Pyrite sulfur - within 0.5 wt. % of ASTM, reproducible to 0.1%.
3. Pyrite stoichiometry - reproducible to + 5%.
4. Calcium sulfate - one sample was observed, accuracy not known.
5. Total sulfur - by adding the forms, a value within 0.25 weight
percent of ASTM measurements, reproducible to 0.1%.
6. Major inorganics including Si, Al, Fe, Ca, K, Mg, and Ti - values
for total major minerals were obtained by adding up these con-
stituents which were within 2 wt. % of the total values obtained
from low temperature a$hing.
For those elements which occur in coal in only one form (i.e., most of the
mineral matter constituents, such as Al^Oj), a precision of 1% and an accuracy
of 5% of the measurement can be obtained. These numbers are just those of a
typical quantitative analysis of a homogeneous sample using the microprobe.
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The data used in the above measurements requires a sample of coal weighing
200 mg, and the actual data acquisition time is 10 minutes for 121 subsamples
on the coal sample. To obtain information on additional elements requires an
additional 2 minutes per element. The time required is due to computer
analysis of each subs ample spectrum and may be reduced by using a faster
computer. These 121 subsamples provide enough measurements to determine the
correlations between the elements sufficiently well for the MASC analysis.
The MAPS (Microprobe Analysis of Particle Sizes) method for particle size
determination has evolved from the MASC analysis. There is a great need for
obtaining the pyrite size distribution in order to optimize mechanical clean-
ing procedures. There is obviously information about the size distribution
contained in the data. The MAPS method extracts this information through a
fitting procedure to determine an average particle number density (number of
particles per unit volume of coal) and a distribution function for the par-
ticle sizes. This information on particle sizes is found to be in good
agreement with data from washability studies on two coals.
In Section I, the MASC procedure for sulfur forms is described, and the
results for 17 calibration coals are discussed. Section II describes the
particle size (MAPS) procedure and evaluates the results in terms of washabil-
ity. Section III is an evaluation of the use of an elemental analyzer for
total nitrogen measurements. Section IV details the application of MASC to
the major minerals, and in Section V the results of these measurement tech-
niques are applied to seven coals supplied by EPA.
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I. SULFUR FORMS
\. INTRODUCTION AND BACKGROUND
The determination of the sulfur forms in coal using the electron micro-
probe is based on the correlation in the spatial distribution between the
various elements in coal. The method was originally described by Solomon and
tfanzione (Ref. 5). In the simplest case of a coal containing only organic
sulfur and pyrite sulfur (FeS2), with no other sources of iron or sulfur,
then the result of a measurement of those two elements over any small region
(subsample) in the coal will show a mixture of the organic sulfur and the
pyrite. If it is assumed that the organic sulfur is uniformly distributed,
and that the pyrite is spatially distributed in small, discrete pieces, then
a series of subsamples will each have the same amount of organic sulfur plus
a varying amount of pyrite sulfur. The iron measurement will show the same
variation in intensity as the pyrite sulfur, and if a plot is made of sulfur
vs iron for the various subsamples as in Figure 1-1, the data will fall on a
straight line whose slope determines the pyrite stoichiometry, and whose
intercept on the sulfur axis at zero iron represents the organic sulfur. MASC
(Microprobe Analysis of Sulfur in Coal) is an automated technique for perform-
ing these analyses, with additional elements added for determining the clay
components and for correcting the iron measurement for the presence of iron-
bearing clay. In addition, it is possible to distinguish between pyrite and
sulfate mineral sulfur, by observing the presence of a high correlation
between the sulfur and calcium.
B. EXPERIMENT PROCEDURES
The sulfur in coal analysis is performed on a Cameca scanning electron
microprobe with two wavelength-dispersive crystal spectrometers, a Tracer-
Northern TN2000 energy-dispersive spectrometer, and a Canberra stage and
column automation system, all operating under the control of a PDP 11/04
minicomputer. The MASC compute^ program acquires the data for several ele-
ments at 121 different subsamples on a coal sample and stores the data on a
magnetic floppy disc for further analysis.
Sample Preparation
The coal samples are prepared for microprobe analysis by grinding in a
tungsten carbide capsule to at least 100 mesh, and then pressing 200 mg
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FIG. 1-1
EXAMPLE OF MASC PLOT
0.123
79-10-115-5
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pellets in a standard 13 mm diameter pellet press at 20,000 psi. This provides
a reasonably smooth surface for the microprobe to analyze. A variety of
different grinding sizes were tried during the course of this program, and it
was found that the best results were obtained with coal so finely ground that
the high density particles (i.e., pyrite and clay) were small enough that
the electron beam could penetrate them completely. This eliminates particle
size effects from the microprobe analysis of sulfur forms. This requires that
the pyrite be ground to a top size of one or two microns. Since this is at
the limit of conventional dry grinding techniques, the samples were ground for
an hour.
The pressed pellets were then mounted on standard microprobe mounts with
silver Micropaint, and coated with approximately 200 A of carbon in a
sputter coating vacuum system. The carbon provides a conductive coating on
the samples to eliminate charging under the electron beam in the microprobe.
No effort was made to determine if this coating was necessary for coal sam-
ples. Previous work at UTRC used a gold coating on coal, but the gold M X-ray
lines interfere with the sulfur K lines which are used for analysis. Several
experiments were performed to verify that the carbon coating gave reproducible
and consistent results compared with the previous measurements at this lab.
C. DATA ACQUISITION
The computer program which controls the automated data acquisition has
two major phases: an initialization phase and an acquisition phase. During
the initialization phase, X-ray spectra to be used as references are acquired;
and the stage coordinates of the samples and the pyrite reference sample are
entered into the computer. The reference spectra for the energy spectrometer
consist of the relevant portion of the full X-ray spectrum for each of the
elements of interest. For example, for the sulfur standard, a spectrum is
taken on a polished pyrite flat for 30 seconds, i.e., long enough to acquire
a statistically accurate sulfur K X-ray line, and then the portion of the
spectrum containing the sulfur peak and about one peak-width of background on
either side is stored in the computer memory. Similarly, the iron peak from
the same spectrum is stored as the iron reference. In addition to sulfur and
iron, reference peaks are stored for Al, Si, and Ca, from Al^Oo, SiO^j
and CaWO, standards, respectively. The pyrite, alumina, and silica stand-
ards were used because these were considered to be most similar to the forms
of these elements usually found Jin coal. The calcium tungstate was the only
readily available Ca standard (it was already mounted in the microprobe).
Although the wavelength spectrometers were not used extensively in this
program, they can be tuned up on a desired standard, and standard intensities
and background intensities entered into the computer.
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Once this initial information is stored in the computer, the analysis
is started. For each sample, the computer moves the stage to the sample,
asks the operator to check the focus, magnification, beam current, and other
parameters of the probe, and then the computer initiates an analysis of the
first subsample. This consists of a full energy spectrum, the intensities of
both crystal spectrometers, the length of time of acquisition, and the inte-
grated absorbed sample current during that time. Because the subsequent
reduction of this energy spectrum takes about five seconds, the acquisition
time is set to five seconds. The stage is then moved to a different sub-
sample, and the acquisition cycle is repeated with the spectrum being stored
in a different part of the computer memory. While this data is being ac-
quired, the previous data is being reduced by doing a least-squares fit of the
reference peaks to the unknown spectrum to determine the ratio of the unknown
to the reference peaks including background correction (i.e., the usual
k-ratios) using the Tracer-Northern program 'XML1. This process repeats until
121 data subsamples have been analyzed, and then the stage moves to the next
sample and repeats the procedure. The fitting procedure will calculate
k-ratios for all references which have been entered during the initialization,
up to a maximum of 20.
Before every other sample, the same analysis is performed for nine sub-
samples on the pyrite flat to verify that the microprobe has not drifted in
energy calibration, beam current, etc.
Thus, for every sample, there is stored on floppy disc a set of nine or
more measurements (2 crystal spectrometer intensities, integrated beam cur-
rent, measurement time, and k-ratios for Fe, S, Al, Si, and Ca plus any
additional elements) at 121 subsamples plus the same data for nine subsamples
on pyrite.
The measurement time per subsample is subject to several constraints:
we wish to complete the measurement in as short a total time as possible
subject to the requirement of adequate statistics for each subsample point.
The subsample-to-subsample variation in the iron peak intensity, for example,
is typically comparable to the average value of the iron intensity. This
requires that the number of counts in the iron peak should be, say 10 or 20,
in order to observe the subsample-to-subsample variation with good statistics.
For a 1% pyrite sample under the conditions used, this corresponds to a 1/2 to
1 second sampling time. Thus, in practice, the acquisition time is limited by
the data reduction as mentioned above. The second important point here is
that the average intensities, for the coal sample as a whole, are determined
from the full set of 121 data points, which represents 10 minutes of acquisi-
tion time, which is more than sufficient.
Q
The beam conditions used are 3 x 10 ' amps beam current at 2000X magni-
fication for the finely ground samples used for the sulfur forms measurements.
At these currents, the crystal spectrometer gave essentially zero counts
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(luring five seconds, and so were not analyzed. The same sample current was
used in the particle size measurements, which employ 1000X for the 100 mesh
samples and 2400X for the 40 mesh samples. For some of the particle size
data, beam currents of 2 x 10-7 amps were used, and the data from the crystal
spectrometers was used instead of the energy data.
D. DATA REDUCTION
The basic measurement considered here is the measurement of the sulfur
concentration in coal. This is assumed, as first approximation, to be made
up of two parts: uniformly distributed organic sulfur, S(0), and spatially
distributed mineral sulfur, S(M), which is mainly pyrite and is correlated
with iron. We can then write that the total sulfur, S(T), is given by
S(T) = S(0) + S(M). (weight % dry basis)
If we now assume that these two constituents generate X-rays with different
efficiencies, so that
S<0) - aks(Q)
and
S(M) = bkg(M)
where the k's are the sulfur k-ratios for the two different sulfur forms, we
can solve the first equation as follows:
1 = a
k
SCO)
SCT)
kSCM)1
S(T)J '
Ci-1)
If we now have a procedure for separating the mineral and organic components
of k-ratios, we can use this equation with the ASTM values for total sulfur to
determine the values for the efficiencies a and b.
There are several possible ways of analyzing the data to determine the
relative amounts of the two sulffur k-ratios. In the event that a plot of the
sulfur data versus the iron data shows a straight line with no scatter, the
intercept of that line is kg/Q\ and the difference between the average k and
^SCO) *-s t*ie mineral ^SCM)* ^or some °f the very high pyrite coals, this is
nearly the case, but for most coals there is considerable scatter on such a
plot as seen in Figure 1-1.
There are several ways of fitting straight lines to data of this sort.
One of the most common is a conventional least squares regression of all but
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one of the variables as independent variables against the remaining variable
as a dependent variable. The drawback to this procedure is that it treats one
of the variables, the dependent variable, as special; that is, the procedure
assumes that all the variance observed in the measurements is contained in
this one variable, and that the remaining variables vary only because they
determine different amounts of the dependent variable. At first, we attri-
buted most of the scatter in the data to actual variations in the organic
sulfur in the different macerals making up the coal, and thus felt justified
in doing the least squares regression. Using the values for the organic and
mineral k-ratios obtained in this way in Eq. 1-1 gave values for a and b which
consistently overestimated the organic sulfur and underestimated the mineral
sulfur as determined by the ASTM methods, although they gave fair agreement
with the total sulfur measurements.
If it is assumed that the data for iron and sulfur are normally distri-
buted, then a mean and standard deviation can be calculated, and an ellipse
can be drawn about the data representing a contour of constant deviation from
the mean (see Figure 1-1). The least-squares regression of sulfur as a
function of iron is the line labeled £ in Figure 1-1, and it can be seen that
this line is the line of least slope that one might draw by eye through the
data, and is the line which gives the largest intercept and thus the least
mineral component for the k-ratios. Thus, it is not too surprising that this
least squares regression overestimated the organic sulfur. The other extreme
is the line labeled _b, which is the line going through the points of maximum
and minimum sulfur, and is the line calculated by assuming that all the
scatter is in the iron data. It could be argued that the iron data has all
the scatter, since there are random amounts of iron which are associated with
clay and not pyrite, but we expect that this effect is included by including
the clay through the Al-Fe and Si-Fe correlations. Thus we are led to select
a line which is intermediate between a_ and _b. At first glance, the line £
through the longest axis of the ellipse would appear to represent the line" of
highest probability, but unfortunately, this line is not independent of scale
changes in the two axes. This can be seen easily by considering data for
which the ellipse is very nearly a circle. By changing the scale of the
x-axis, one can convert this circle into an ellipse with its long axis par-
allel to either the Fe-axis or the S-axis. Unless there is a natural set of
units for the axes, it is difficult to determine what regression line to
choose. In the absence of any such well-defined axis units, we have chosen to
calculate the axis of the ellipse for the case where the data have equal
variances in all directions; i.e., the data are normalized by the sample
standard deviations.
Occasionally, a coal will have a very low iron-sulfur correlation, and
this two dimensional analysis is completely inappropriate. The PHS 408 coal
supplied by the EPA as part of this program is a case in point and is discussed
in detail in section V. This coal has mostly a calcium-sulfur correlation.
To deal with these situations, an analysis of the correlations of the other
-------
elements is required. A more complex problem is that presented by coals which
have significant correlations between more than a single pair of elements.
The Scranton and Upper Cliff coals illustrate this. The Scranton coal has
comparable correlations of 0.3 for the Fe-S and for the Fe-Al. The Upper
31iff coal has significant correlations for both the Fe-S and the Fe-Si, and
in addition has possible nonzero correlations for S-Si and Fe-Al. In order to
landle coals of this sort, we have considered the problem of doing the equiva-
lent of the two dimensional regressions in two or more dimensions.
If it is assumed that the various elements are linearly related, that is,
that the sulfur k-ratio is a linear function of all the other k-ratios, then
any of the regression lines discussed above can be calculated from the sample
means, the sample variances, and the correlations between the measurements.
This information is easily computed from the data sets for all the elements
and is collected in tables in Appendix A. The problem of finding the major
axis of an ellipse is easily generalized to the n-dimensional case of finding
the axes of an n-dimensional ellipsoid, and turns out to be a straight-forward
eigenvalue problem (Ref. 6). The eigenvectors calculated for these correla-
tion matrices are just the vectors parallel to the axes of the ellipsoid
through the means of the data, and the eigenvalues associated with each
eigenvector represent the relative amount of the total variance which can be
explained by assuming that the data fall on the line determined by the eigen-
vector.
An examination of the eigenvectors can be informative: the components of
the eigenvectors the relative amounts of each of the elements required to make
up the eigenvectors. Usually, one of the two most likely eigenvectors will
have large amounts of iron and sulfur, and small amounts of the other three
elements, indicating that iron sulfide is present in the coal. In these
cases, the other of the first two eigenvectors will usually have large compon-
ents of aluminum and silicon, indicating clay. That is, the analysis de-
scribes the coal as being pyrite for one vector and clay for the other. This
is the case for the PSOC 308 coal, for example. Another common situation is
for the first eigenvector to be made up of equal positive amounts of iron,
sulfur, silicon, and aluminum, indicating pyrite plus clay; while the other
eigenvector has comparable amounts of the elements, but the aluminum and
silicon are negative, indicating pyrite minus clay. That is, the analysis
describes the sample as having total mineral matter as the most likely vector,
with the difference between the two forms of mineral matter being the next
most likely. An example of this case is the Montana Lignite coal, labeled LIG
in Appendix A.
For the sulfur in coal, we are interested in determining the plane
surface which goes through the two largest dimensions of the 5-dimensional
ellipsoid. This is equivalent to stating that of the five dimensions measured,
three have little or nothing to do with the sulfur, and we find the surface
which is orthogonal to these three least probable eigenvectors. This gives
10
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three equations among the five variables, which can be solved to give an
expression for sulfur as a function of any two of the other four variables.
The computer program described in Appendix E is set up to choose the two
most significant variables to give the regression equations shown in the
tables in Appendix A. Since pyrite is so common in coal, iron is included
if it has any significant weight in the first two eigenvectors, even if it is
not one of the most significant variables. In the cases where iron is one of
the variables, we then assume that the remaining variable determines a correc-
tion to the iron for the presence of clay, and an amount of iron due to clay
is calculated by extrapolating the clay to zero, holding sulfur fixed at the
organic value. The organic sulfur is calculated by extrapolating both the
mineral variables to zero. Often the mineral and organic sulfur calculated in
this manner will be unreasonable in some respect (i.e., negative values), and
this is usually caused by including the clay regression when the correlation
with the clay is very small. In these cases, the clay constituent in the
regression is set equal to its mean value.
The point to doing the regressions in this manner is that it distinguishes
the cases where a simple two dimensional Fe-S analysis is not appropriate. In
the cases where a simple two-dimensional analysis is sufficient, this five-di-
mensional analysis gives the same answers; but when the coal has mostly
CaSO^, for example, the Fe-S analysis misses completely, whereas the five-
dimensional analysis rejects the Fe completely as in the PHS 408 coal, or
modifies it as for the Scranton coal, and the Upper Cliff coal.
E. DISCUSSION OF REPRODUCIBILITY
A series of measurements were performed on one coal, the ISGS coal, to
determine the reproducibility of the technique, and the results showed that
the sulfur forms are reproducible to about 0.1 percent in sulfur k-ratios, as
shown in Table 1-1.
TABLE 1-1
REPRODUCIBILITY OF SULFUR FORMS FOR ISGS #72 COAL
(average of 10 measurements)
Organic sulfur k-ratio 3.86 +_ 0.09
Mineral sulfur k-ratio 0.55 +^ 0.11
x in FeSx (k-ratios) 0.945 +_ 0.05
We have not summarized the data on the slope of the sulfur-iron regression
in this report because most of the coals considered have either had a slope of
11
-------
nearly 1.0 in the k-ratio plots (or 2.0 in the weight percent basis) indicat-
ing near stoichiometry in the pyrite, or else they have had a small enough
amount of pyritic sulfur that the regression is not meaningful. However, this
slope information can be very useful in considering the behavior of treated
coals and chars where the stoichiometry of the pyrite is modified. In addi-
tion to the reproducibility of these sulfur forms k-ratios, the correlations
for these data sets were reproducible at the +_ 0.1 level. Unfortunately,
these data were acquired before we had stabilized our procedures, and the data
are for 100 mesh coals, which makes the conversion to weight percent unrelia-
ble for these data.
F. DISCUSSION OF RESULTS
The calibration constants a and b in Eq. 1-1 were determined using a set
of seven Pennsylvania State Coal Bank coals and a coal from the US Geologic
Survey, ISGS #72, which was used in a round-robin interlaboratory compari-
son. These eight coals are well characterized as to sulfur forms, and yield
values for a and b of 0.63 and 1.12, respectively (Figure 1-2). The plot
symbols used are summarized in Table 1-2.
Using these empirically determined calibration constants, the sulfur
forms measurements from MASC are compared with the standard ASTM techniques
for total sulfur (Figure 1-3), organic sulfur (Figure 1-4) and mineral sulfur
(Figure 1-5) for the above eight coals plus nine additional coals whose
sulfur forms had been measured by a commercial laboratory. Except for the two
coals plotted as 1 and 3, the agreement between the ASTM method and the MASC
method is within about one-half weight percent sulfur. The ASTM methods for
sulfur forms (Ref. 1) use a dilute HC1 extraction to remove sulfate sulfur,
which is then measured by precipitation of the sulfate as BaSO^ using
Bad-. The sulfate-free coal is then treated with dilute HNO.J to remove
the pyrite, which is then determined by measuring the iron, since some of the
organic sulfur is removed by the nitric acid wash. Organic sulfur is deter-
mined by difference of the pyrite and sulfate from the total sulfur. The
total sulfur is determined by one of three methods (Ref. 1). These total
sulfur measurements appear to be quite reliable in practice, so that the
agreement between the ASTM and the MASC total sulfur is the most meaningful
comparison. The accuracy of the ASTM sulfur forms, however, has been the
subject of considerable controversy, suggesting that the scatter in Figures
1-4 and 1-5 could be in either !the ASTM or the MASC measurements. The agree-
ment for the total sulfur indicates that the MASC forms is the better one.
The agreement for the total sulfur is remarkable since the ASTM method is a
direct measurement for total sulfur whereas the MASC method uses the sum of
the forms.
12
-------
TABLE 1-2
PLOT SYMBOLS FOR COAL
Plot Symbol Coal
A PSOC 103
8 ISGS #72
G PSOC 170
J PSOC 212
M PSOC 268
T PSOC 308
V PSOC 330
4 MONTANA LIGNITE
9 BuMik 40659
E Scranton, ND
1 Buelah, ND
5 Montana Savage
7 Upper Cliff, AL
3 Rosa, AL
F Black Creek, AL
6 TRW #2 (WV)
2 Utah
13
-------
CORRELATION OF SULFUR FORMS
1C
I
O
C/)
<
O
i-
QC
CE
CO
CD
cc
O
0.8 1.2
MINERAL SULFUR K-RATIO/TOTAL SULFUR ASTM
1.6
2.0
P
rb
-------
COMPARISON OF MASC FOR TOTAL SULFUR
6.0
£
i
o
u
1.2
2.4 3.6
TOTAL SULFUR ASTM WEIGHT %
6.0
P
to
-------
COMPARISON OF ORGANIC SULFUR MEASUREMENTS
3.0
(D
I
O
01
0.6
1.2 1.8
ORGANIC SULFUR ASTM WEIGHT %
2.4
T]
O
-------
COMPARISON OF MINERAL SULFUR MEASUREMENTS
o
I
I
(D
ty 3.0
QC
CO
tr
1.0
2.0 3.0
MINERAL SULFUR ASTM WEIGHT
4.0
5.0
cn
-------
The two coals which do not agree are clearly high in pyrite according to
the MASC data. The No. 1 coal, Beulah, North Dakota, showed a very high
correlation between the iron and sulfur, with a slope that indicates pyrite.
The No. 3 coal, Rosa, also had a very high iron content, but it has a slope
between iron and sulfur that would be more indicative of FeS rather than
FeS2- In both cases, the samples are clearly not as low in mineral sulfur
as the commercial laboratory measurements would indicate.
18
-------
II. PYRITE PARTICLE SIZE ANALYSIS
A. INTRODUCTION
The MAPS (Microprobe Analysis of Particle Size) method for particle
size determination has evolved from the MASC analysis. There is a great need
for obtaining the pyrite size distribution in order to optimize mechanical
cleaning procedure and it is toward this use that the current pyrite particle
size analysis is targeted. It was also believed that a knowledge of the
sulfide particle size distribution would be necessary to correct sulfide
concentration from the MASC analysis. However, it has been found both easier
and more accurate to grind the coal very finely prior to the MASC analysis,
thereby exposing all the pyrite as small particles whose x-ray signal is
proportional to volume.
In this section, the basic theory of the MAPS method will be outlined.
A series of studies will be presented to show that the method is capable of
extracting the desired parameters from the noisy data sets. Limits will
be discussed on the amount and type of data needed for the analysis. And
finally, the analysis of several coal samples will be presented and the
results related to washability data developed for these coals.
B- THEORY
There is pyrite particle size information contained in the MASC analysis
data but extracting a size distribution is difficult because of several
factors. First, a subsample at one scan position of the scanning electron
microprobe can encompass several pyrite particles resulting in an x-ray signal
intensity that represents the sum of the signals from the individual particles.
The signals received are for elemental iron or sulfur and can have varying
amounts of other iron or sulfur containing compounds such as iron oxide or
organic sulfur containing compounds mixed into them, Variability in pyrite
stoichiometry as well as fluctuations in each of these other possible compounds
contribute to the noise in the data. Counting noise is always present in the
detection of the x-ray signals. The penetration depth, 5, of the electron
beam and the absorption of the emerging x-rays are such that for small par-
ticles (d£fi) the x-ray intensity is proportional to the volume of the
Particle while for large particles (d»5) the intensity is proportional to the
cross-sectional area of the particle. The coal has a rough surface and the
Pyrite particles are not all on the surface so that ones at greater depths see
a weaker electron beam and contribute a small signal.
19
-------
All of the preceding effects have been considered in the study and proced-
ures have been devised for dealing with them.
Considering a distribution of pyrite particle sizes f(r), it is possible
to relate the signal, s, from an individual particle to its radius, r, depth
D in the coal matrix, and the efficiency of the detection process, rj, by
the following approximate relations
S
_D/A
e ]
[n
f
i)
1 < r < 6
2 ~ ~
(II-l.
- r
We have assumed that the density of pyrite particles is sufficiently low that
the possible shadowing of one particle by another may be ignored. From these
relations it is possible to consider a distribution, <£(s) of signals from
individual particles. The efficiency and depth factors in square brackets in
Eq. II-l are taken into account in an overall normalization efficiency as the
data in each data set is normalized to pure pyrite signals extrapolated to
zero density.
Assuming the distribution <|>(s) to have a mean p and variance a the dis-
tribution of signals i//(S,n) expected from a single subsample can be constructed.
For a scan which contains pyrite particles randomly drawn from the distribution
for n » 1
and where
_ (S-nn)'
(11-2
n
E
1=1
(n-3
Since the number of particles in the subsample should be distributed according
to Poisson statistics, we expect the measured signal distribution to take the
form
20
-------
00
I//CS) £ N (n,n) MS.n) (II-4)
nmo
where
N (n,n) = nne"n
n!
and n is the average number of particles per subsample. The MAPS sizing
procedure consists of least squares fitting the distribution (s)) to a mean radius of the particle size distribution, Eq. II-l is used.
The normalization of the data already includes the terms in square brackets in
Eq. II-I as well as a factor of the area of the scan subsample.
The MAPS fits are accomplished by taking the MASC data, SirQn and Sgulfur
from a large number of subsamples, N, and forming a distribution by arranging
both S^r and S in from 10 to 30 bins. The least squares fitting
procedure would be expected to be more sensitive to M» CT, n for a distribu-
tion with a large number of sample bins. However, the number of subsamples in
the data set, N, sets a stringent limit on the number of bins that can ac-
tually be utilized and conversely the need for sufficient detail in the dis-
tribution sets a lower limit on the number of bins that can be used and there-
by a limit on the number of subsamples needed for a good particle size deter-
mination. Poisson counting statistics for the binning procedure itself im-
Plies that to retain 10% accuracy near the peak of the distribution, the bin
nearest the peak needs about 100 data points. To spread the distribution
over 10 bins then requires N=300 to 400 subsamples for a reasonable MAPS fit.
*n the coal sample fits presented later, N ~ 950 subsamples are spread over
a total of 20 bins including the tails of the distribution.
c- RESULTS WITH SIMULATED DATA
To examine the accuracy with which the parameters u, cr and n can be
extracted from a data set and to help develop the technique in using the MAPS
fitting procedure, a Monte Carlo simulation of the scanning electron micro-
Probe data acquisition procedure was developed. The simulated data is then
analyzed by the MAPS fitting procedure. The simulation produces data for an
assumed distribution with a selected /j, a and n by generating a random
number for the number of particles to appear in a simulated subsample chosen
with Poisson statistics with mean n and then chooses signal strengths at
random from the distribution (s) with the appropriate fj. and or. These indi-
vidual particle signals are summed as in Eq. II-3. To this SMonte Carlo
21
-------
additional signal is added to represent other compounds (e.g., iron oxide or
organic sulfur) and additional random noise is added to simulate the variabi-
lity in these quantities and counting noise in the x-ray detection procedure.
This variation could represent an actual concentration variation or it could
be due to a variation in the amount of coal actually contained in a given
subsample due to surface roughness effects. Also, examination of real data
sets shows many with a non-zero minimum for the iron signal which is attri-
buted to the presence of finely dispersed clay particles. Since the clay
particle sizes are typically an order of magnitude smaller than the pyrite
particles, it appears in the data set as a constant background.
After these additions, the data is normalized by an overall factor. No
such points are selected to produce a single data set. An example of such a
data set is presented in Figure II-l along with a real data sample from PSOC
308 coal. This simulated data set was generated by using parameters from a
MAPS analysis of the coal data set and shows that the Monte Carlo simulation
of the MASC data aquisition program can produce realistic data.
Many data sets were produced by the simulation by varying the basic param-
eters or by generating a series of different data sets (different random
numbers) all with the same parameters. Each data simulation was fitted with
the least squares MAPS procedure and the best fit parameters were compared to
the input parameters in the simulation. Three examples are shown in Figures
II-2(a), (b) and (c). In each figure, the upper histogram represents the
simulated data distribution function 4> (%onte Carlo^ ^or i-ron while the
lower histogram is for sulfur. The dashed lines through the histogram distri-
butions are the best fit of
-------
FIG. 11-1
NUMERICALLY GENERATED DISTRIBUTION COMPARED TO COAL DATA
DC
u
CO
IRON
a) PSOC 308 COAL
IRON
b) SYNTHESIZED DATA SET WITH ± 25%
VARIATION IN S(0)
79-03-32-1
-------
EXAMPLES OF MONTE CARLO DISTRIBUTIONS
FITTED BY PARTICLE SIZE PROGRAM
(a) j
tmi i LJ ir*
RSO 8.89(11
GflrtW- 8.88211
"4.652
3MMCM
GENERATED WITH
a = 0.004
o = 0.002
n = 5
(b) i.
W!
1
RSO 8.
GflttW- 8.8053*
5.313
NORMRLI2ED flfitfl 1
a = 0.010
a = 0.004
n = 5
(c)
RSQ 8.8832?
CfWMft- 8.88311
9.885
5 = 0.003
a = 0.003
TT = 10
24
-------
TABLE II-1
EXAMPLES OF MAPS FITS TO MONTE CARLO DATA FROM FIGURE I1-2
Data f Input V Best Fit g Input p Best Fit n Input n Best Fit
Figure II-2
(a) .0040 .0042 .0020 .0021 5 4.7
(b) .0100 .0091 .0040 .0040 5 5.3
(c) .0030 .0033 .0030 .0030 10 9.0
I1, ° are in arbitrary units
-------
EXAMPLE OF MONTE CARLO DISTRIBUTION FIT
BY PARTICLE SIZE PROGRAM
GENERATED WITH
a = 0.010
o = 0.005
~ = 1
e ooooat j; |RON
a) SULFUR VS. IRON PLOT
01
* OOCXVE c:
RSQ 0.ee996
* 0.985
b) IRON AND SULFUR DISTRIBUTION FITTED
26
-------
the limits 5 £ n 15. In practice, other considerations tend to push
the scan magnification ($ 1/subsample size) to high values. Measurements
are therefore made at the lower limits for n and for the validity of the
aPproximation of I (fr^s) by >p(s).
D- RESULTS FOR COALS
As a test of pyrite particle sizing procedure, an extensive data set
run on the microprobe on ISGS #72 coal was used. Figure II-4 shows the
results of two fits of ISGS coal by the MAPS sizing procedure. This data set
contains approximately 1000 subsample data points to reduce the statistical
sampling error. In the fitting procedure, the iron data (upper histogram) and
sulfur data (lower histogram), which are taken independently, are treated so
that the iron data may be fitted and the fit related by the best fit slope and
1ntercept from the MASC procedure to the sulfur data. Similarly the sulfur
data may be fitted alone and related by the slope and intercept to the iron
data. The data for iron and sulfur may both be used in the fit, thereby
doubling the effective number of data points contributing to the data distri-
bution for ₯(S). In Figure II-4(a) the iron alone was used for the dashed
fitted curve yielding \i = .00372, a = .00296, and n = 2.0. If the sulfur
d«ta alone is used (Figure II-4(b)) the results are almost identical: p =
00385, a = .00306, n = 2.3. The iron fit in (a) and the sulfur only fit
ln (b), each show slightly better fits near the peak of their respective
distributions. For this data set, the scan subsample size is lOOp by lOOy for
a magnification 1000X. The penetration depth in the pyrite at the accelerating
voltage used is & = 1.8 p. Using Table II-2 prepared using Eq's II-l for this
voltage, we find r = 2.7p for this ISGS coal data.
A series of 55 photomicrographs were made at the same time the data was
being taken on this ISGS cool. The 55 data points were identified in the full
J-000 point data set and are shown enclosed by squares in the standard sulfur
Versus iron plot shown in Figure II-5(a). The pictures represent a good
sampling of the full data set; so much so that a MAPS fit of the 55 point data
Set yields best fit parameters almost identical to the results shown in Figure
**~4. with so little data, both the iron and sulfur data were fit together to
lraprove the statistics. The histograms from these data together with the best
are shown in Figure II-5(b). Samples of these photographs are included as
II-6. These are taken using Fe x-rays, each dot on the photograph
rePresenting an emitted Fe x-ray. The three pictures at the bottom of Figure
*I~6 include very large areas with high Fe concentration which we call pyrite
r°cks. These rocks appear far out in the high pyrite tail of most data sets
atld are separated in the particle size fitting procedure as these rocks form a
Separate size distribution. The upper six photographs are representative of
the data included in the fit. They show a finely divided Fe background and a
of pyrite particles of varying sizes (similar photographs taken with S
27
-------
ISGS COAL FITTED BY PARTICLE SIZE PROGRAM
-ai
IRON-PYRITES IN COflL
1009 POINTS U PICTl*
RSQ - 0.083T2
GflMMfl- 0.80296
. - 1.967
iwtn er-«
a) ONLY IRON DISTRIBUTION FIT
mun-r 1*1 ic.0 11 tUH.
e.eoeeee-ei
-j.eoeeee-ei
POINTS u PICTUR i
RSO 8.60385
GflMMfl" 0.08386
. 2.266
b) ONLY SULFUR DISTRIBUTION FIT
23
-------
TABLE I1-2
AVERAGE PARTICLE RADIUS VERSUS AVERAGE PARTICLE SIGNAL
For Penetration Depth 6 = 1.8
n
(Microns)
.2
.4
.6
.8
1.0
1.2
1.4
1.6
1.8
2.0
2.2
2.4
2.6
2.8
3.0
3.2
3.4
3.6
3.8
4.0
5.0
6.0
S = M x subsample area
= p x (10 /Magnification)
.03
.27
.91
2.2
4.1
6.1
8.1
10.2
12.2
16.7
22.2
28.5
35.7
43.9
52.9
62.9
73.7
85.5
98.0
112.0
193.0
297.0
29
-------
ISGS COAL ANALYSIS WITH PHOTOMICROGRAPHS
0ee POINTS u PICTIJR
FESX- ».98E» _ // f-]
a) SULFUR VS. IRON PLOT WITH
POINTS WHERE PICTURES WERE
TAKEN INDICATED
-ei
IRON-PYRITES IN COflL
55 POINTS U PICTURES
RSQ 8.08383
Gflttlfi- 0.80283
* - 2.905
-ei
NORMRLIKO flKR
-Pnerk.
b) PARTICLE SIZE FIT OF POINTS
IN PICTURES
30
-------
FIG. 11-6
FITTED PYRITE DISTRIBUTION FROM ISGS COAL
'ROCKS" DISCARDED BEFORE FIT
79 06-136-1
31
-------
x-rays confirm that for this coal these are indeed pyrite particles).
The background Fe may come from highly dispersed fine pyrite particles or
other Fe compounds and is sometimes subtracted from the data. The sum of the
areas of all the remaining particles in one frame represent the data used in
the computer particle size fitting procedure. From the MAPS fit of the data
sets as shown in Figures II-4 and 5, we found average particle radii of 2.7y
or 5.4y diameters. This represents a very good fit of the full distribution in
both Fe and S and is also in excellent agreement with the photographic sample.
As a comparison, in Figure II-6, the bottom middle picture of the six contains
two nearby pyrite particles with diameters of 5y and 8y.
Two other samples of coal analyzed by the MAPS procedure are PSOC 308 and
PSOC 330 coals. The sulfur versus iron plots for the N=950 subsample data
sets are shown in Figures II-7(a) and (c). The ellipses shown in the figures
represent the 2.0 standard deviation ellipses for a bivariate normal distri-
bution with means determined by the data and a correlation coefficient of 0.76
for the 308 and .98 for the 330 coal. The lines represents the semi-major
axes of the ellipses.
The data points near or outside of the ellipses in Figure II-7 represent
subsamples containing pyrite "rocks" as shown at the bottom of Figure II-6.
The rocks are not well represented by the distribution functions *y(S) and form
a small but very long tail for the data distributions. Figures II-7(b) and
(d) show the data subsamples in the sulfur versus iron plot that are elimin-
ated by the 2.0 standard deviation pyrite rock cut. For the 308 data set they
represent 48 out of 968 subsamples or just 5%. However these large rocks
contain 23% of the iron signal and when the volume effect is taken into
account-possibly 50% of the total pyrite volume in the coal. Similarly, for
the 330 coal, they represent 6% of the data points, and 30% of the iron
signal. These large rocks seem to represent a very uniform distribution up
toward the limit imposed by the subsample size - a diameter of about 40y -
rather than the tail of say a log-normal distribution. Further, for washabil-
ity criteria, the large rocks represent an easily removed background. There-
fore to simplify the particle size fitting procedure by limiting the number of
bins that must be processed in the analysis, a cut is made in the data throw-
ing away all data samples outside this 2.00 ellipse. The data thus discarded
are shown in Figure II-7(b) and (d).
The raw data for PSOC 330 coal also contained a considerable gap on the
low side of the iron distribution, presumably from lightly dispersed iron in
clay particles. This gap represented 11% of the mean value of the iron signal
S~iron' This iron background has been substracted from each iron value
and the resulting data is shown in Figures II-8(c) and (d). This results in
moving the iron histogram in Figure II-8(d) over to the S£rQn = 0 axis and no
bins on the left have been lost in this renormalization process. The abrupt
32
-------
PSOC 308 AND 330 PYRITE PARTICLE SIZE ANALYSIS
FIG. 11-7
a)
308
U.
,j
j
CO
IRON
ALL DATA
C)
330
a
D
LL
D
00001 00
1.839
3P PflSS 48 fiPf« 1
IRON
b)
I
g
1 '«*>OE..oe
*»
IRON
REJECTED AFTER 2(7 CUT
d)
I OOOOE-00
l?P PflSf MP flPf.fl 1
cc
b nr
IRON
79_11_6_3
33
-------
PSOC 308 AND 330 COALS FITTED FOR PYRITE PARTICLE SIZE
I!
)
I
V)
VOPOOOt-Pl
IRON
70% Pft^';. H0
NOB«MLl?Eb
DATA USED FOR FIT
c)
330
LI
'
u
PARTICLE SIZE FIT
d)
PI
IRON
! '
34
-------
cutoff of this and many other coal distributions on the low iron side represent
good evidence for the dispersed iron. The apparent presence of iron in clay
for PSOC 330 and none in PSOC 308 is also borne out by the multivariate
Analyses including elements abundant in clay particles such as Si and Al and
is discussed further in other sections. No abrupt gap is observed in the PSOC
308 coal (Figures II-8a, b).
The data after the dispersed iron subtraction and the pyrite rock cutoff
was binned in 30 bins along the iron signal axis and the resulting distribution
fitted by the MAPS procedure. The MAPS fit for the PSOC 308 coal yields a =
°.0054, u - .0075, n = 6.3 (Figures II-8b). The fit for the 330 coal re-
sulted in a = .0086, U = .0108, and n = 2.2. These data were taken with a
magnification of 2400X or a subsample size of 42w x 42u. This yields an
average radius of 1.5H for the 308 and 1.9 for the 330.
E' COMPARISON WITH WASHABILITY DATA
There are clearly differences in the particle size distribution between
PSOC 308 and PSOC 330. The differences in the distribution of rocks is
evident in Figure II-7. PSOC 330 has a much higher number of large pyrite
rocks. PSOC 308 has fewer high sulfur-high iron particles and some of those
aPPear not to be pyrite as evidenced by their non-pyrite sulfur-iron stoichi-
°n»etry. The distribution of small pyrite particles is also different as
evidenced in Figure II-8. PSOC 330 has a wider distribution of sulfur and
lron intensities which is indicative of a higher concentration of larger
Particles. This indication is confirmed with the particle size analysis which
shows the average particle radius for PSOC 330 to be 1.3 times that for PSOC
08. This gives an average pyrite particle weight for PSOC 330 which is more
than twice that for PSOC 308.
A washability study was performed on these two coals to determine whether
predicted differences in particle size distribution was apparent in their
ease of cleaning. Samples of the 40 mesh coal were ground to pass 100, 200
and 325 mesh and subjected to centrifuging in 1.4 and 1.6 specific gravity
fluid. The results are shown in Figures II-9 and 10. Figure II-9 for PSOC
30 shows substantial scatter in the data but indicates excellent cleaning
characteristics for particle sizes of 100 mesh and below. In view of the high
c°ncentration of very large pyrite particles in this coal, the scatter in the
ata seems to be a sampling problem. Figure 11-10 for PSOC 308 shows a dif-
fefent cleaning behavior. There is a much more gradual fall off of pyrite
°oncentration with decreased particle size and incomplete cleaning even at 325
These data are consistent with the particle size distribution results
suggest the desirability of further work on these techniques to allow
prediction of the washability potential of a coal.
35
-------
WASHABILITY STUDY ON PSOC 330
3.0
2.5
2.0
u:
DC
03
cc
UJ
z
1.5
1.0
0.5
-------
WASHABILITY STUDY ON PSOC 308
3.0
f
2.5
2.0
1.5
CO
CL
LU
Z
1.0
0.5
u>
I
I
U1
to
0
RAW SAMPLE
A 1.4 FLOAT S(M)
O 1.6 FLOAT S(M)
t
_L
J_
AS RECEIVED
40 MESH
100 MESH
PARTICLE SIZE
200 MESH
325 MESH
O
-------
III. TASK IV - TOTAL NITROGEN CONCENTRATION
There is a need for improving the nitrogen concentration measurements in
coal. The standard ASTM method (Ref. ]) which uses a Kjeldahl digestion is of
questionable accuracy for tightly bound nitrogen compounds found in coal. The
method is also time consuming. The purpose of TASK IV was to evaluate an al-
ternative procedure for nitrogen analysis.
Elemental analyses of nitrogen in coal were performed with a Perkin-Elmer
Model 240 element analyzer. The Model 240 performs an automated Pregl-Dumas
analysis on approximately 2 mg samples. These samples are taken from a larger
sample which has been finely ground to insure that the small samples are repre-
sentative. The automated analysis takes 15 minutes per sample to perform and
yields the ash weight as well as the carbon, hydrogen, and nitrogen weights.
The Model 240 is connected to a PDF 11 computer as is the weighing balance so
that the recording of results and data reduction are all automatic. Access to
the balance and the Model 240 is through a dry box so that the samples are
always maintained within a dry environment. Table III-l illustrates the typi-
cal reproducibility of measurements. Reproducibility from one run to the
next is typically + 3% for nitrogen and hydrogen +_ 1% for carbon, and _+_ 5% for
ash and missing.
To evaluate the accuracy of the PE 240, coal samples whose nitrogen
concentrations had been determined by standard ASTM methods were measured.
The results for nine coals are summarized in Table III-2. Seven of the coals
were supplied by The Energy and Environmental Research Corporation as part
of EPA's Fundamental Combustion Research program, one coal comes from the
Pennsylvania State University coal bank and one (ISGS #72) was supplied as
part of a round robin test by the US Geological survey. The agreement between
the PE 240 results and results determined by ASTM methods is typically +_ 10%.
The question is, which results are mor^ accurate? The most carefully handled
and measured sample is the ISGS coal. 'But even here, the ASTM nitrogen value
ranges from 1.01% to 1.39%. The reproducibility of ASTM nitrogen determina-
tions on two other coals, Rosa and PSOC 170 also shows substantial variation.
To obtain an independent check on the PE 240 accuracy, 3 model compounds
were measured. The compounds were chosen to represent the kind of nitrogen
structures expected in coal. The results for the three compounds are listed
in Table III-3. The variation between the PE 240 value and the theoretical
value is within + 1%.
38
-------
In summary, it appears that the PE 240 gives accurate and reproducible
results for the kind of nitrogen compounds found in coal and probably for
coals themselves. The accuracy of the Kjeldahl digestion is in question and
needs further study.
39
-------
TABLE III-l ELEMENTAL ANALYSIS OF ROSA COAL (DRY)
SAMPLE AND ASH MT HEIGHT * ,_.^
SAMPLE WEIGHT ASH UT NITROGEN CARBON HYDROGEN ASM MISSING
SON'
Fresh
Sample
AU
234
235
236
237
238
239
8.8858888
232= ROS-S
233= ROS-S
ROS-S
ROS-S
ROS-S
ROS-S
ROS-S
ROS-S
2.269
1.856
2.582
2.421
2.486
2.124
2.865
2.988
8 243
9.195
9.259
8.248
8.242
8.248
8.212
8.293
8 DRY AUER 2 318 8.241
1.49
1.58
1.55
1.55
1.61
1.53
1.63
1.53
1.56
76.89
76.67
76 15
75.51
76.58
75 39
76 58
75.22
76.81
4.29
4.24
4 29
4.16
4.46
4.28
4.48
4.28
4.29
18.75
18.51
18.35
9.91
18 86
11.39
18.27
18.88
18.48
7.38
7.81
7.66
8.86
7.29
7.58
7.28
8.99
7.74
-------
TABLE III-2
NITROGEN MEASUREMENTS
AVG.
ULTIMATE
ROSA
ALABAMA
1.51
1.55
1.58
1.52
1.54
1.74 EER #1
1.53 EER #2
BLACK CREEK
ALABAMA
1.87
1.91
1.88
1.89
1.79 EER
UPPER CLIFF
ALABAMA
1.57
1.54
1.58
1.56
1.46 EER
TRW #2
1.42
1.33
1.39
1.38
1.35 EER
UTAH
1.49
1.54
1.51
1.62
1.54
1.41 EER #1
1.42 EER #2
ACRIDINE
7.77
7.82
7.82
7.80
7.82 THEO
AVG.
ULTIMATE
PSOC 170
1.36
1.41
1.42
1.40
1.41
1.48
1.31
1.43
1.43
1.41
.13 PSU #1
1.30 PSU #2
1.44 HAZEN
BEULAH
N. DAKOTA
1.00
.97
.87
.89
.86
.85
.90
.91
1.10 EER
SAVAGE
MONTANA
1.04
.97
.96
.99
.95
.96
.98
1.13 EER
OCTAHYDRO-
ACRIDINE ISGS CARBAZOLE
7.48 1.25 8.20
7.27 1.25 8.35
1.25 8.20
7-38 1.25 8.25
7.49 THEO 1.24 ILL.GS 8.38 THEO
l.Oi DOE-PETC
1.39 BROKEN HILL
AUSTRALIA
-------
TABLE 111-3 ELEMENTAL ANALYSIS OF MODEL COMPOUNDS
SAMPLE AND ASH NT
SAMPLE WEIGHT ASH UT
HEIGHT
-------
IV. MAJOR INORGANICS
The MASC data which was acquired to determine the sulfur forms can also
be used to estimate the total amount of inorganics in coal. In order to do
^is, it is necessary to assume that the inorganic clays are in the form of
oxides or carbonates. In particular, it is assumed that the aluminum is
Present as A1203, the silicon as Si02, the calcium as calcite (CaCO-j)
a°d the non-pyrite iron as Fe20-j. The reference standards for aluminum
and silicon were chosen to be A^Oo and Si02 so that no corrections for
a°sorption would be necessary to calculate a weight percent of each of these
constituents. Thus we predict that the total mineral matter on a dry basis
WlH be given by
MM(dry) (kfll + kgi + 0.285 * kCa + 0.7 * kFe) * 1.38 + Pyrite
where the 0.285 factor for the Ca is the calculated ZAP correction from MAGIC
(&ef. 7) for caC03 relative to a standard of CaWO^, and the 0.7 is the
actor for Fe203 relative to a pyrite standard, and the 1.38 is the roughness
actor found to be appropriate for the sulfur data. A plot of mineral matter
°und using this relation is shown in Fig. VI-1 for comparison with mineral
matter determined by Low Temperature Ashing or by calculating from the ash
nalysis. As can be seen from the figure, the agreement is within 2 percent
°r most coals, indicating that the assumptions used in generating the above
elation are reasonable.
It may be possible to estimate the types of clay in the coal from the data
Slnce the major clays have definite ratios of alumina to silica which is
aPparent in the MASC data. A plot of the aluminum versus silicon k-ratios for
a sample which was 'salted1 with kaolin is shown in Fig. V-2. Similar plots
°r coal salted with montmorillonite and illite are shown in Figs. IV-3,4.
r°n> the slope of the regression between the aluminum and the silicon, we
"°uld be able to distinguish between kaolin and the other two clays. The
8reement, however, between estimates of kaolin and the other clays using this
Pr°cedure, and measurements of these constituents using IR spectrometry in
lab is very poor. This MASC procedure for estimating clay components
more analytical work to determine if there is a regression which does
reasonable results for clays.
43
-------
COMPARISON OF MASC AND ASTM ASH
30.0
(D
I
O
I
24.0
X
o
o
I
in
6.0
12.0 180
ASH ASTM WEIGHT %
24.0
30.0
-------
FIG. IV-2
PSOC 212 COAL WITH 10% KAOLIN
0.146
SILICON INTENSITY
3205 DAT KAOLIN
0.146
79-10- 115 24
45
-------
0.224
PSOC 212 COAL WITH 10% ILLITE
0.000
SILICON INTENSITY
3206 DAT ILLITE
0.224
46
-------
FIG. IV-4
PSOC 212 COAL WITH 10% MONTMORILLONITE
0.268
§
".
-'
s
.
-J
-'
o.ooo
SILICON INTENSITY
3207 DAT MONTMORILLONITE
79-10-115-22
47
-------
V. MASC RESULTS ON EPA SUPPLIED COALS
The results of MASC measurements on the seven coals supplied by the EPA
are summarized in Table V-l, V-2, and Appendix D. There are several interes
ing features of these coals which are discussed below. In particular, some
these coals showed less correlation between the iron and the sulfur k-ratio
than most of the coals used for calibration, and one of them showed a high
correlation between the sulfur and the calcium.
Table V-2 shows each of the mineral constituents of these coals and con
tains data on titanium, magnesium, and potassium, which are not discussed l
Section IV. These data were obtained and reduced in the same manner as
described in Section IV. These elements are assumed to be present in coal
the forms TiC^, MgO, and K^O and were measured relative to standards of
Ti, Mg and KC1, respectively. This gives a relation for the total mineral
matter of
mm (wt. % dry) = pyrite +1.38 (kA1 + kgi + 0.285 kCa
+ 0.67 kFe (clay) + 1.79 kTi + 2.5 kMg + 0.48 kK)
where the numerical factors inside the parentheses are the ZAF corrections
for the assumed mineral relative to the reference standard actually used, a
the 1.38 is the roughness factor discussed in Section IV.
Figures V-l through V-7 are the MASC plots for the seven coals with ^
projection of the regression line from the multielement analysis onto the
S plane shown for all but the 1635 coal, for which there was no correlation-
In addition, where the analysis selected a reasonable clay-pyrite split, t"1
line is shown on the plot as a vertical line parallel to the sulfur axis.
PHS 506,534,546,578,1632A These coals all appeared to be "conventional"
pyritic coals. We accepted the split between iron bearing clay and pyrit6
two of them (PHS 534 and 578) since the correlations were reasonable and
consistent, and the resulting split was reasonable. The 1632A showed high
correlations between the iron and the clay constituents; so high that the
regression indicated that all the iron was clay. Because the pyrite corre
tion was also quite high, we rejected the clay in favor of a pyrite interp
tat ion for the iron. A reasonable fraction of the iron in this sample sno
probably be attributed to clay (perhaps 20%).
48
-------
TABLE V-l
SULFUR FORMS FOR EPA-SUPPLIED COALS (wt% dry)
S(0)
MASC ASTM
S(M)
MASC ASTM
1632A
1635
PHS 408
PHS 506
pHS 534
PHS 546
PHS S7fl
J /a
0.4
0.4
0.5
0.4
0.6
1.2
2.8
_
0.33
0.65
0.4
1.5
2.5
1.0
0.0
0.5*
1.4
2.5
1.9
2.1
x in FeS3
MASC
1.8
0.34 Sulfate
0.11 Pyrite 1.1**
1.0
3.2
1.7
3.3
1.9
2.1
2.2
2.0
This
c°al was mostly sulfate
**
in
49
-------
TABLE V-2
MINERAL MATTER ANALYSES FOR EPA-SUPPLIED COALS (wt.% dry)
Coal
CaO*
Ti0
MgO
K90
Total
Z & -»
MASC HTA MASC HTA
1632A
1635
S 408
14.6
1.7
6.8
7.2
0.6
2.5
MASC
0.2
0.5
0.3
HTA MASC HTA
0.
0.
0.
3
0
0
MASC HTA
0.0
0.0
0.3
MASC HTA
0.
0.
0.
6
0
1
^ _>
MASC HTA
1.25 -
0.4
1.2
MASC
24.2
3.2
11.2
HTA
-
-
10.5
PHS 506 6.3 5.1 4.4 4.4 0.4 0.4 0.0 0.2 0.3 0.3 0.3 0.2 1.8 1.8 13.5 12.8
PHS 534 12.4 10.0 7.6 6.6 0.1 0.1 0.0 0.2 0.0 0.2 0.9 0.8 3.4 4.6 24.4 22.9
PHS 546 12.7 9.6 6.5 5.0 0.1 0.1 0.3 0.3 0.0 0.1 0.5 0.4 2.4 2.7 22.5 18.6
PHS 578 8.8 8.3 4.0 4.1 0.5 0.7 0.0 0.2 0.0 0.2 0.3 0.4 3.0 4.9 16.6 19.6
* CaO for MASC calculated from .56*CaCO (MASC)
Fe00 for MASC calculated from 1.25* S (M)
2 "\
+ Fe90 (MASC)
-------
1632A COAL MASC PLOT
FIG. V-1
* **/ .
0.840
3235. DfiT
,
FG 2600X
79-10-115-14
51
-------
1635 COAL MASC PLOT
0.013
S
U
L
F
U
R
N
T
T
V
+
+
0.
00
3236.DAT
1635 FG 2900X
52
-------
FIG. V-3
PHS 408 COAL MASC PLOT
S
u
L
U
R
I
N
T
E
N
S
I
T
V
o.ooo
Ca ASSOCIATED WITH CLAY
CALCIUM INTENSITY
3237 DAT PHS 408 FG 2000X
79_10-115-16
53
-------
FIG.
PHS 506 COAL MASC PLOT
0.0
VST
3238.DAT
0.047
536 FG 2000*
54
-------
FIG. V-5
PHS 534 COAL MASC PLOT
3.2 3
S
u
L
P
U
R
I
N
T
E
N
i-s
i
I
T
V
Fe ASSOCIATED WITH CLAY
'IRON INTENSITY
3239.DAT
6.213
534 FG 2080X
7g_10-115-18
55
-------
FIG.
PHS 546 COAL MASC PLOT
0.0 3
3246.OAT
PHS 546 FG 2000X
0.078
56
-------
FIG. V-7
PHS 578 COAL MASC PLOT
0.099
S
u
L
R
U
I
N
T
E
N
S
I
T
V
8.
-ORGANIC SULFUR k-RATIO
-Fe ASSOCIATED WITH CLAY
INtENSlTY
3241.DAT
0-099
578 FG 2000X
79-10-115-20
57
-------
PHS 408 This is the only coal we have yet studied that had a clearly
significant correlation between the sulfur and the calcium, and as such we
have no reliable calibration for converting this data to weight percent.
organic sulfur is calculated as usual. For the calcium sulfur, we assumed
that it was present in the form of CaSO^, scaled up the k-ratio by the 1.38
"roughness" factor, and then used MAGIC to convert the k-ratio to weight
percent. We have chosen to accept the split between sulfate and clay calci
determined by the regression because although the correlations are smalli
are consistent, and the result is plausible. In addition, the final weight
percents of the constituents are not much changed by rejecting the clay fit-
The stoichiometry of the calcium sulfate is estimated to be CaS, ^Ox using
the slope of the S-Ca regression, using MAGIC to correct the slope.
1635 None of the correlations for this coal were considered significant; 3°
the five elements were treated independently as minerals plus organic sulfu
58
-------
REFERENCES
1976 Annual Book of ASTM Standards, Part 26 - Gaseous Fuels, Coal and
Coke. Philadelphia, PA.
r\
acobs, I. s. , L. M. Levinson, and H. R. Hart, Jr.: Workshop on Mineral
Matter in Coal, Urbana, Illinois, March 22-24, 1978.
j
Montano, p. A., and P. E. Russell: Workshop on Mineral Matter in Coal,
urbana, Illinois, March 22-24, 1978.
McCartney, j. T. , H. J. O'Donnell, and S. Ergun: US Bureau of Mines. RI
(1969).
Solomon, p. R. t and A. V. Manzione: A New Method for Sulfur Concentration
Measurements in Coal and Char. Fuel 56, 393, 1977.
orrison, Donald F.: Multivariate Statistical Methods. 2nd ed., McGraw-
HlU Book Company, 1976.
olby, J. W.: Proc. Sixth Nat. Conf. on Electron Probe Analysis. 1971.
59
-------
APPENDIX A
CORRELATIONS AND EIGEVECTORS FOR CALIBRATION COALS
60
-------
103 FG 2000X
FE
S
SI
AL
Cft
MEAN
SIGMA
3251.DAT
CORRELATIONS
FE S SI
AL
CA
0.1865 9.1764 1.8000 0.0643 -0.1631 -0.S428 -0.0339
1.1367 0.1934 8.0643 1.0000 0.0118-0.0681 0.1177
2.1250 0.5187 -0.1631 0.0118 1.0000 0,3962 -0.0411
1.3990 0.2334 -0.0428 -0.0681 0.3962 1.0000 -0.0644
0.7088 0.5820 -0.8339 0.1177 -0.0411 -0.0644 1.0000
EIGENVECTORS
VALUE
1.4643
1.1053
1.0050
0.8515
0.5739
FE
8 . 3029
-0.1706
-0 . 8062
0.4192
-0.2314
S
0.1615
0 . 6667
0 . 4236
0 . 5673
0.1679
SI
-0 . 6649
0.2198
-0.1205
-0 . 0497
-0.7021
AL
-0.6421
0.0397
-0.2933
0.2756
0.6513
CA
0.1667
0.6905
0.2649
0.6512
-0.0341
REGRESSIONS
S = 0.9098
ORGANIC S-
0.3209CA + -0.0003SI
0.910 MINERAL S* 0.227
-------
170 FG 2000X
MEAN
SIGMA
FE
S
SI
AL
CA
1 . 1590
3 . 8829
2.2316
1 . 4943
1 . 9890
0.7125
0.6519
0 . 6349
0 . 2563
0 . 8485
3252.DAT
CORRELATIONS
FE S SI
1.0000 0.3285 0.0435
0.8235 1.0000 0.1154
0.0435 0.1154 1.0000
0.0165 0.0059 0.4119
0.0320 -0.0712 -0.0509
AL
CA
0.0165 -0.9320
0.0059 -0.0712
0.4119 -0.0509
1.0000 0.0714
0.0714 1.0000
EIGENUECTORS
UALUE
1.8572
1.3962
1.0089
0.5715
0.1662
FE
0.6849
-0.1493
0.1074
0.0908
-0.6991
S
0.6940
-0.1181
0.0459
0.0321
0.7080
SI
0.1800
0.6759
-0.1718
-0.6886
-0.0838
AL
0.0973
0.7081
0.0924
0.6916
0.0481
CA
-0.0858
0.0741
0.9738
-0.1956
0.0244
REGRESSIONS
S = 2.6425+ 0.9211FE + 0.1156AL
FE< CLAY) * 0.0000 FE< PYRT > = 1.1590
ORGANIC S»
2.642 MINERAL S«
1.240
-------
212 FG 2080X
MEAN
3253.DAT
CORRELATIONS
SIGMA FE
S
SI
AL
CA
FE
S
SI
AL
CA
0 . 2725
1.1911
8 . 9670
0 . 7329
0 . 4744
0
0
0
0
0
.3269
.2136
.2327
.2230
.4172
1
0
0
0
0
.0000
.0226
0.
1,
.1710 -0.
.0097 -0.
.1740 -0.
0226 0 .
9000 -0.
0845 1 .
0785 0 .
9959 0 .
1710
0045
0000
6558
1631
0 . 0097
-9 . 0785
0 . 6558
1 . 0000
-0 . 0009
0
-0
0
-0
1
.1740
.0959
.1631
.0009
.0000
EIGENUECTORS
UALUE
1
1
1
0
0
.7193
.1521
.0292
.7920
.3074
FE
0.
0.
0.
0.
0.
2238
6120
3798
6447
1246
S
-0 . 0972
-0.1521
0 , 9059
-0 . 3727
0 . 0835
SI
0 . 6908
-0.1087
0.1003
-0 . 0593
-0 . 7053
AL
0
-0
-0
0
@
.6432
.3553
.0512
.0135
,6763
CA
0.
0.
-0.
-0.
0.
2229
6814
1496
6646
1479
REGRESSIONS
S = 1.2787 4- -0.1828FE + -0.0515AL
FE
-------
330 FG 2000X
3254.DAT
FE
S
SI
AL
CA
MEAN
2.1891
2.7932
1.4215
1.0293
0.3792
SIGMA
CORRELATIONS
FE S
SI
AL
CA
0.8412 1.0000 0.8798 -9.0713 0.0141 0.8387
0.7875 0.8798 1.0090 -0.1199 -0.8363 -0.0362
0.2234-0.0713-0.1199 1.0000 0.5453 0.0718
0.1999 0.0141 -0.0363 0.5453 1.0000 0.0523
0.3525 0.0387 -0.0362 0.0718 0.0523 1.0000
EIGENVECTORS
UALUE
1.9162
1 . 5303
0 . 9885
0 . 4493
3.1156
FE
0 . 6708
0 . 2276
0 . 0226
-0.0381
-0 . 7045
S
0 . 6829
0.1722
-0 . 0492
-0.0461
0 . 7068
SI
-0 . 2393
0 . 6537
-0 . 0954
-0.7113
0.0188
AL
-0.1607
0 . 6822
-0,1355
0 . 6998
0 . 0253
CA
-0 . 8266
0.1606
0 . 9847
0 . 0259
0 . 0567
REGRESSIONS
S = 1.0793 + 0.9349FE + -0.3184AL
FE(CLAY) = 0.3506 FE(PYRT) * 1.8385
ORGANIC S=
1.879 MINERAL S-
1.719
-------
268 FG 2QQQX
MEAN
SIGMA
FE
S
SI
AL
CA
0 . 9269
1 . 8528
5.1845
3 . 3307
0 . 6307
0 . 3478
0 . 3650
0 . 5657
0.3186
0 . 4352
3255.DAT
CORRELATIONS
FE
1.0830
0.4453
0.1107 0.9725
-0.0333 -0.0451
0.1415 0.0270
SI
AL
CA
0.4453 0.1107 -0.0333 -0.1415
1.0000 0.0725 -0.0451 0.0270
1.0000 0.5873 0.1064
0.5873 1.0000 0.0462
0.1064 0.0462 1.0000
Ui
EIGENVECTORS
UALUE
1.6166
1.4661
1 . 0026
8.5271
0 . 3876
FE
0.1850
0 . 6843
-0 . 0654
-0 . 6705
0 . 2090
S
0.1739
0.6516
0 . 2958
0 . 6764
-0 . 0249
SI
0 . 6998
-0.0816
-0 . 0552
-0.1031
-0 . 7000
AL
0 . 6539
-0 . 2358
-0.1932
0.1680
0.6718
CA
0.1349
-0.2120
0.9318
-0 . 2325
0.1203
REGRESSIONS
S = 0.9371 + 0.9989FE + -0.0020SI
FE = 8.0101 FE * 0.9168
ORGANIC 8=
0,937 MINERAL S=
0.916
-------
308 FG 2900X
FE
S
SI
AL
CA
MEAN
2.6031
4.5912
2.6495
:.0897
SIGMA
3256 . DAT
CORRELATIONS
FE
SI
AL
CA
1.6598 1.0000
1.7268 0.9528
0.5889 -0.0629 -0.0434
-0.0496 -
0.9528 -9.0629 -0.0496 -0.1531
1.0000 -0.0434 -0.0092 -0.1229
1.0000 0.6768
0.0933
0.'6768 1.0000 -0.1117
. . . . . . .
0.4557 0.4188-0.1531-0.1229 0.0933-0.1117 1.0000
EIGENUECTORS
UALUE
2.0113
1.6601
0.9891
0.2942
FE
0.6791
0.1408
0.1284
-0.0348
0.0454 -0.7080
S
0.6716
0.1649
0.1578
0 . 0486
0 . 7032
SI
-0.1827
0 . 6782
0.1714
-0 . 6904
0 . 0247
AL
-0.1412
0 . 6969
-0.1127
0.6921
-0.0512
CA
-0.1857
-0 . 0854
0 . 9574
0.2017
-0.0314
REGRESSIONS
S = 1.5803 + 1.0364FE + 0.1498AL
FE
-------
40659 FC 2000X
FE
S
SI
AL
CA
3257.DAT
CORRELATIONS
MEAN
SIGMA FE
S
SI
AL
CA
4.0692 1.1840 1.0090 0.8764-8.1104-0.0308 0.0196
6.1172 8.9732 0.8764 1.8000-0.1182-0.0828 0.0568
8.9316 1.0943 -0.1104 -0.1182 1.0000 0.5744 -0.0423
4.9426 6.4670 -0.0308 -9.0828 0.5744 1.8000 -0.0644
0.8440 8.4383 0.0196 8.0568 -0.8423 -8.0644 1.0080
EIGENVECTORS
UALUE
1.9621
1 . 5027
8 . 9982
8 . 4242
8.1207
FE
0 . 6345
0.3138
-0 . 8448
8.8281
-0 . 7047
S
8 . 6452
0 . 2832
-0.8861
-8 . 8786
8 . 7852
SI
-8.3175
8.6281
8.1869
-8 . 7885
-8 . 8365
AL
-8 . 2789
0 . 6545
8.8615
8 . 7883
8 . 8636
CA
8.8831
-8.8918
8.9914
8 . 8333
-0 . 8269
REGRESSIONS
S = 3.8091 + 0.8792FE + -8.8950AL
FE * 9.5341 FECPYRT) = 3.5351
ORGANIC S=
3.889 MINERAL 8-
3.188
-------
LIG FG 2808X
MEAN
SIGMA
FE
S
SI
AL
CA
0.3317
1 . 0067
3.1685
2 . 2769
9.1987
0 . 2656
9 . 2637
0.9881
0 . 3000
0 . 8759
3258.DAT
CORRELATIONS
FE
S
SI
1.6800 0.3890 Q.0985
0.3890 1.0000 -0.0277
0.0985 -0.0277 1.0000
0.0734 0.1885 0.4538
AL
CA
0.0734 0.1162
0.1885 -0.0322
0.4538 -0.0577
1.0000 0.0011
0.1162 -0.0322 -0.0577 0.0011 1.0000
oo
EIGENUECTORS
UALUE
1 . 5946
1 . 2798
1 . 0077
0 . 6853
0 . 4325
FE
0 . 4569
-0 . 5227
0 . 0347
-0 . 6053
0 . 3877
S
0 . 4586
-0 . 4983
-0 . 3277
0 . 4488
-0 . 4823
SI
0 . 4948
0 . 5204
0.1298
-0.4168
-0 . 5426
AL
0 . 5794
0 . 3702
0 . 0930
0.4716
0 . 5442
CA
0.0175
-0 . 2656
0 . 9305
0.1915
-0.1630
REGRESSIONS
S = 0.6644 + 0.9704FE + 0.0065SI
FE
-------
ISGS UF 2000X
MEAN
SIGMA
FE
S
SI
AL
CA
1
4
2
16
3
.5858
.8781
.2751
.9510
.4532
1
1
0
3
18
.0852
.0296
.5867
.0325
.3997
3098.GAT
CORRELATIONS
FE S SI
AL
CA
1.0300
0.9134
0.0607 -0.0224
0.1612 0.1434
0.9134 0.0697 0.1612 -0.0555
1.0000 -0.0224 0.1434 -0.0292
1.0000 0.5686 -0.0221
0.5686 1.0000 -0.0431
-0.0555 -0.0292 -0,0221 -0.0431 1.0000
VO
EIGENUECTQRS
UALUE
1 . 9995
1 . 5827
8 . 9942
0 . 4222
8.0815
FE
0 . 6575
-0.2416
0 . 0270
-0 . 1305
-0.7011
S
0 . 6444
-0 . 2904
0.0501
0 . 0032
0 . 7056
SI
0 . 2097
0 . 6867
0 . 0548
-0 . 6880
0 . 0902
AL
0.3210
0.6199
0 . 0372
0.7137
-0 . 0439
CA
-0 . 0738
-0 . 0398
0 . 9962
0.0146
-0.0198
REGRESSIONS
S = 3.6503+ 0.9511FE + -0.1233SI
FE< CLAY > » 0.2949 FEC PYRT) « 1.2999
ORGANIC S=
3.650 MINERAL S»
1,228
-------
SCRANTQN FG 2000X
3242.DAT
CORRELATIONS
MEAN
SIGMA FE
S
OT
Ol
AL
CA
FE 0 . 7937 0 . 3265
S 2.3954 0.2915
SI 1.3793 1.2893
AL 0 . 8228 0 . 7093
CA 9 . 3264 0 . 8369
EIGENUECTORS
UALUE
1 . 4205
1 . 2248
0 . 9934
0.8534
0 . 5079
FE
0 . 6863
-0.0143
0.2914
0.2458
-0.6192
1 0000 0.2909 -0.6363 0.3087 0.0331
0 2999 1.0000 -0.1601 -0.0569 -0.0798
-0 . 0363 -0 . 1 60 1 1.0000 0 . 0526 -0 . 0990
0.3007 -0.0509 0.0526 1.0000 -0.1446
0.0331 -0.0798 -8.0990 -0.1446 1.0000
S
0 . 5090
-0 . 4074
-0 . 4389
0 . 3535
0 . 5073
SI
-0.1554
0.6192
0.0156
0 . 7665
0.1253
AL
0 . 4670
0 . 5068
0 . 3340
-0 . 3982
0 . 5049
CA
-0.1665
-0 . 4523
0.7814
0.2618
0 . 2975
REGRESSIONS
S = 1.6726+ 1.1287FE + -0.3197AL
FE = 0.2330 FE * 0.5607
ORGANIC S=
1.673 MINERAL S«
0.633
-------
BEULAH FG 2000X
MEAN
SIGMA
3243.DAT
CORRELATIONS
FE S SI
AL
FE
S
SI
AL
CA
2.
2.
0.
0.
5.
5031
7246
9061
4865
7597
0.
0.
0.
0.
0.
6210
5853
4677
2081
7755
1
0
-0
-0
0
.0000
.7201
.0885
.0731
.0002
0.
1.
0.
-0.
0.
7201
0003
3374
0609
0483
-0.0885 -0.0731
0.0374 -0.0609
1.0000 0.4147
0.4147 1.0000
CA
0.0002
0.0483
0.0236
-0.0209
1.0000
EIGENUECTORS
UALUE
1 . 7490
1 . 3936
1 . 0022
0 . 5907
0 . 2646
FE
0 . 6872
0.1447
-0 . 0746
0.1422
-0 . 6935
S
0 . 6728
0.2319
-0 . 0034
-0 . 0929
0 . 6964
SI
-0.1649
0 . 6933
0.0361
-0.6816
-0.1623
AL
-0.2143
0 . 6659
-0 .- 0720
0 . 7065
0 . 0792
CA
0 . 0444
0 . 0347
0 . 9939
0 . 0S63
-0.0381
REGRESSIONS
S = 0.2044+ 0.9507FE -f 0.1551SI
FE = 0.0000 FE * 2.5031
ORGANIC S-
0.204 MINERAL 8-
2.520
-------
MONT SAUAGE FG 2808X
FE
S
SI
AL
CA
MEAN
0.1283
0.7837
9.6129
SIGMA
3244.DAT
CORRELATIONS
FE S
SI
AL
CA
0,1238 -0.1218
0.0641 0.0641
0.4854 -0.0009
0'5053 012092 0."1238 0.0641 0.4854 1.0000 0.1697
0.1525 1,8000 0.1173 0.0292
0.1798 0.1173 1.0000-0.1001
0.2387 0.0292 -0.1001 1.0000
9.8343 0.9503-5:1218 0:0641-0.0009 0.1697 1.0000
10
EIGENUECTORS
UALUE
TT ritMWh»
1 5268
A ^»rt*» ar*^
1 1442
1 0994
« * *ur*f mf 1
0 . 7726
0 . 4570
FE
0.1634
0 . 7427
-0.1672
-0.6106
-0.1471
S
0.0210
0 . 5583
0 . 5998
0.5551
-0.1415
SI
0.6581
-0.1343
-0 . 2758
0 . 2433
-0 . 6434
AL
0.7091
0 . 0299
0 . 0788
0.0362
0.6991
CA
0.1921
-0.3432
0 . 7283
-0 . 5092
-0 . 2358
REGRESSIONS
S = 0.7321 + 0.8931FE + -0.1246AL
FE = 0.0705 FE = 0.8578
ORGANIC 3»
0.732 MINERAL 8-
0.852
-------
UPPER CLIFF FG 28088
3245.DAT
FE
S
SI
AL
CA
mm
1.0930
1.5137
3.7549
2.4710
8.4624
SIGMA
CORRELATIONS
FE S
0.3358 1.0900 0,4576
0.2644 0.4576 1.0000
0.4423 0.3037 0.1183
0.2893 0.1102 0.0621
0.4302 -0.0493 -0.1000
SI
0.3087
0.1183
1.0000
0.4122
0.0309
AL
CA
0.1102 -0.0493
0.0621 -9.1000
0.4122 0.0309
1.0000 0.0523
0.0523 1.0000
EIGENUECTQRS
-s]
10
UALUE
1.7510
1.2212
0 . 9357
8 . 6246
0 . 4675
FE
0 . 5692
-0 . 3069
0 . 2402
0 . 3443
-0 . 6368
S
9.4712
-0.4891
0.2433
-0.5915
0.4776
SI
9.5317
0.3869
0.1671
9.5278
0.5110
AL
0.4189
0.5424
0.2895
-0.5908
0.3227
CA
-0.0496
0.4719
0.8783
-0.0495
0.0328
REGRESSIONS
S = 1.3075 + 0.8266FE + -0.2812AL
FE « 0.8486 FE * 0.2494
ORGANIC S=
1.308 MINERAL 3»
0.206
-------
ROSA FG 2000K
FE
S
SI
AL
CA
MEAN
4.6559
3.7536
2.9054
1.1911
9.2978
SIGMA
3246.DAT
CORRELATIONS
FE S
SI
AL
CA
2.2331 1.0008 0.6845 -0.0197 -0,8532 -0.0387
1.4855 0.6845 1.0000 -0.0735 -0.0866 0.0922
2.2528-0.0197-0.0735 1.8000 0.5901 0.0398
0.3171 -0.0582 -0.0866 0.5901 1.0000 -0.0404
0.3170-0.0387 0.8922 0.0398-0.0404 1.0000
EIGENMECTORS
UALUE
1 . 7675
1.5112
1.0148
0 . 4083
0 . 2982
FE
0.5719
0.4129
-0.1333
-0.1229
-0 . 6852
S
0.5981
0 . 3765
0 . 0492
0.1494
0 . 6897
SI
-0 . 3832
0 . 5965
0 . 0600
-0 . 6862
0.1510
AL
-0 . 4078
0 . 5749
-0.0633
0 . 6984
-0.1069
CA
0 . 0446
0 . 0377
0 . 9860
0 . 0625
-0.1431
REGRESSIONS
S « 0.7672 + 0.6675FE + -0.0417SI
FE(CLAY) = 0.1817 FE
-------
Ul
BLK CREEK FG 2080X 3247.DAT
CORRELATIONS
MEAN SIGMA FE S SI AL CA
FE
S
SI
AL
CA
EIGENVECTORS
0.2954
1.1142
1 . 5357
1 . 1756
0.3471
0 . 2038
0.2418
0.2810
0 . 2437
0 . 3538
1 . 0000
0.1807
0 . 0537
0 . 0607
0.0831
0.1807
1 . 0000
0.1269
6 , 0647
0.1452
0.0537
0.1269
1.0000
0 . 7579
0.1075
0 . 0607
0 . 0647
0 . 7579
1 . 0000
0.0613
0.0831
0.1452
0.1075
0.0613
1 . 0000
UALUE
1 . 8280
1 . 2099
0.9189
0 . 8049
0 . 2383
FE
0.1599
0.5674
-0 . 5837
-0 . 5579
-0 . 0233
S
0 . 2232
0 . 5987
-0 . 0876
0 . 7621
0 . 0576
SI
0.6731
-0,2061
-0 . 0047
0.0179
-0.7100
AL
0 . 6595
-0 . 2623
-0 . 0577
-0 . 0466
8 . 7006
CA
0.1914
0 . 4565
0 . 8052
-0 . 3247
0 . 0354
REGRESSIONS
S a 0.6458 + 1.2925FE + 0.0736AL
FE = 0.0000 FE(PYRT) - 0.2954
ORGANIC S=' 0.646 MINERAL 5= 0.468
-------
TRW FG 2000X
FE
S
SI
AL
MEAN
3248.DAT
CORRELATIONS
SIGMA
FE
r-
O
0 . 3744
1 . 3592
3 . 4360
0 . 2228
0 . 2283
0 . 5957
1 . 0090
0.2231
0 . 0597
0.2231
1 . 0000
0,0010
2.4947 0.3857 -0.0939 -0.0685
SI
AL
CA
0.0597 -0.9939 -0.1327
0.0010 -0.0685 0.0128
1.8000 0.6519 0.0617
0.6519 1.0000 -0.0118
CA 0>354 0^4520 -0!l327 0.'0128 0.0617-0.0118 1.0000
EIGENUECTQRS
UALUE
1.6611
1.2548
1.0177
0.7440
0.3224
FE
-0.0845
0.7160
-0.0375
-0.6696
0.1744
S
-0.0992
0.6044
0.4698
0.6356
0.8115
SI
0.6938
0,1728
0.0577
-0.0861
-0.6915
AL
0.7051
0.0296
-0.0787
0.1275
0.6924
CA
0.0672
-0.3021
0.8765
-0.3522
0.1090
REGRESSIONS
S = 1.0832 + 0.8746FE + -0.0150SI
FE = 0.0588 FE = 0.3156
ORGANIC S=
1.083 MINERAL 8=
0.276
-------
UTAH FG 2000X
FE
S
SI
AL
CA
MEAN
0.2836
1.0420
3.3745
1.8197
1.0055
SIGMA
0.2041
0.2006
2.0787
0.3999
0.5104
3243. DfiT
CORRELATIONS
FE
1.0900
0.1410
0.0216
0.1410
1.0008
0.0016
0.0937 -0.0430
SI
AL
CA
0.8216 0.8937 -0.0304
0.0016 -0.0430 -0.0490
1.0000 0,6934 -0.0653
0.6934 1.0000 -0.0462
-0.0304 -0.0490 -0.0653 -0.0462 1.0000
EIGENVECTORS
UALUE
1.7123
1 . 1546
0 . 9788
0 . 8543
0 . 3000
FE
0.1160
0 . 6437
0 . 3333
-0 . 6735
-0.0861
S
-0 . 0099
0 . 6985
0 . 0972
8 . 7062
0.0611
SI
0 . 6953
-0 . 0782
0 . 0209
0.1448
-0 . 6993
AL
0.7001
-0 . 0669
0 . 0768
0 . 0043
0 . 7068
CA
-0.1134
-0 . 2950
0 . 9344
0.1632
-0,0180
REGRESSIONS
S = 0.9192 + 1.0470FE + -0.0957AL
FE = 0.1663 FE = 0.1173
ORGANIC 8=
0.919 MINERAL S-
0.123
-------
APPENDIX B
CORRELATIONS AND EIGENVECTORS FOR WASHED COALS
78
-------
--J
VO
3@& 1.4FL GRD
MEAN
SIGMA
3172.DAT
CORRELATIONS
FE S
SI
AL
CA
FE
c-
SI
AL
CA
2.0963
3.9520
2.7599
1 . 8256
0 . 5631
3
0
0
0
0
. 7889
.5765
.3521
.2713
.4379
1
0
0
6
0
.8600
.7028
.0951
. 8669
.9916
0
1
0
0
0
.7020 0.
0008 8 .
. 1943 1 .
. 6233 0 .
.0012 Q.
9951
1843
6000
3418
9827
0.0669
0 . 8233
0.3418
1 . 0000
0.1249
0
0
0
0
1
.0916
.0012
.0827
.1249
.0000
EIGEHUECTORS
UAi
1
i
9
8
0
-UE FE
.7715
.3315
. 9531
. 6532
. 2997
0.
-ft.
0.
0.
-0.
6577
2487
9563
0787
7844
S
0 . 6438
-9 . 2385
-0 . 0644
0 . 0829
0.7816
S!
0.2838
8 . 5778
-0.3210
-9.6934
-8 . 0422
AL
8
0
-0
0
9
.2258
.6440
.1761
. 7977
.6485
CA
0.
0.
Q.
-0.
0.
1461
3170
9266
1103
0861
REGRESSIONS
= 2.5316 + 0.8171FE + -0.1682AL
FECCLAY) = 0.3579 FE » 1.7390
3
ORGANIC S*
2.531 MINERAL S«
1.421
-------
368 FL FG 188 1.4SG
3204 DAT
FE
S
SI
AL
CA
MEAN
2.7110
4 . 5309
2 . 5894
2.3991
0 . 5453
SIGMA
CORRELATIONS
FE
S
SI
0.4918 1.0Q00 0.6379 -0.0192
0.4848 0.6379 1.0060 -9.1407
0 3436 -0.0192 -0.1407 1.0009
0.2529 -0.0287 -0.1170 0.4254
AL
CA
-0.0287 -0.0336
"0.1170 0.0101
0,4254 0.0807
1.0000 0.0392
0'4508 -9.0336 0.0101 0.0807 0.0392 1.0000
EIGENVECTORS
co
o
VALUE
1 . 7229
1 . 3649
0 . 9893
0 . 5742
8 . 3437
FE
0.5916
0 . 4850
-0 . 8653
-8.8641
-0.6911
S
0.6419
0.2862
0.0426
0.0567
0.7879
SI
-0.3463
0.6049
~0.0785
-0.7007
0.1394
AL
-0.3352
0,5997
-0.1726
0.7056
0.0154
CA
-0.0754
0.1688
0.9788
0.0614
-0.0637
REGRESSIONS
S = 2 6844 + 8.9629FE + -0.2564SI
FE a 0.6896 FECPYRT? = 2.0214
ORGANIC S=
2.684 MINERAL S=
1.946
-------
388 FL FG 200 1. 4SC
MEAN
FE
S
SI
flL
Cft
8 . 9633
3.6213
2 . 8248
1 . 8265
3 . 5857
3JGMA
3305.DrtT
CORRELATIONS
FE S SI
CA
0.2446 i . mm 0.1279 0.2383 0.0693 -0.1424
0.2529 0.1279 1.6000 0.0607 6.1812 -0,1235
0.2483 8.2388 0.9607 1.0006 9.3968-0.0501
0.2761 0.9693 8.1812 0.3968 1.0000 -0.1326
8.5857 8.4239 -0.1424 -0.1285 -0.0501 -0.1326 1.0000
OS
EIGENVECTORS
UALUE
1 , 6408
1 . 0379
8.9313
8.8612
S . 528?
FE
0 . 4868
0.2161
-0 . 7585
0 . 3694
-0.3417
S
0*3532
0 . 4722
0.481?
8.6146
0 . 2863
SI
0.3515
-0.4914
-0 1548
-0 . 0380
9 , 6550
fiL
0.5515
-9 . 3272
0.4107
-0.1782
-0 . 6232
CA
-0.3185
-0.6179
0 . 0085
8 . 7024
-0.152?
REGRESSIONS
S * 3.0447
ORGANIC S*
0.1390SI -I- -0.5207CA
3.021 MINERAL S«
0.000
-------
308 FL FG 325 1.4SG
3206.DAT
MEAN
FE 1.2914
S 2.3650
SI 2.6927
*L 8.6737
CA 0.4558
SIGMA
0.3117
8.2368
Q.3172
0.4042
8.3793
CORRELATIONS
FE S
SI
1.0000 0.0129 0.0615
0.9129 1.0000 0.0684
0.0615 6.0684 1.0000
0.0448 0.0105 0.3922
0.1353 -9.0447 -0.0392
AL
CA
0.0443 0.1353
0.0105 -0.0447
G.3922 -0.0392
1.0000 -0.1561
-0.1561 1.0000
EIGENVECTORS
oo
UALUE
1 . 4487
1.1374
3 . 9970
0 . 8335
& . 5834
FE
0 . 0756
0 . 7309
9 . 0958
-9.6688
-0 . 0595
S
0.1462
-0 . 0492
0 . 9768
0 . 0923
0.1166
SI
0 . 6533
0.1868
-0.0415
0 . 3383
-0 . 5539
AL
0.6815
0 . 0248
-0.1865
0.0145
0.7071
CA
-0 . 2859
0,6541
-0.0146
0 6595
0 . 2352
REGRESSIONS
= 2.1772 + 0.2397FE + -0.2671CA
FE » 8.5079 FE(PYRT) * 9.7335
S
QRGAH1C S=
2.177 MINERAL S=
8.188
-------
30S 1.6FL GRD
FE
S
SI
RL
MEAN
SIGMA
3174.DAT
CORRELATIONS
FE S
SI
AL
CA
2 . 2638
4.1046
3.1400
2.1742
0 . 5653
0
0
0
0
0
.7841
. 639.9
.4646
.3314
.4313
1
-0
.0000
.7344
.1122
. 9233
.0531
8
1
0
0
0
. 7344
. 0880
.1840
. 1959
,0128
0.
0.
1.
0.
-0.
1122
1840
0080
5708
0769
0
0
0
1
0
. 8233
. 1059
.5708
.0000
.0050
-0 . 0531
0.8123
-0 . 0769
0 . 0050
1.0000
co
EIGENUECTORS
UALUE
i . 9229
1 . 4462
1.9644
8.4184
3.2080
FE
8 . 5837
-9 . 40S6
-9 . 0272
8.0444
-0 . 6998
S
0.S148
-©.3361
8.0661
0 . 9420
9 . 7092
SI
0 . 4062
0 . 5696
-0.8189
-0.7133
-0 . 0383
AL
0 . 3362
0 . 6269
0.1173
0.6914
-0 . 0463
CA
-0.0571
-0 . 0522
0 . 9983
-0 . 0971
-0 . 0613
REGRESSIONS
S = 1.6878 -I- 0.9001FE 4- 0.2112AL
FE = 0.8000 FE * 2.2633
ORGANIC S=
1.608 MINERAL S*
2.497
-------
338 FL FG 100 1.6SG
FE
S
SI
AL
CA
MEAN
. 2534
1
3.1966
1.4769
4 3827
0.5271
SIGMA
3207.DAT
CORRELATIONS
FE S
0.3032 1.8000 8.3768
0.3122 8.3768 1.8960
0.2458 3.0039 -0.0909
8.3493 -0.1160 -0.0111
SI
AL
CA
0.0039 -0.1160 0.0211
-8.0909 -0.9111 0.0853
1.9000 0.3774 0.0891
0.3774 2.8000 -0.0536
0.4024 0.0211 0.0853 0.0891 -0.9536
00
-p-
EIGENVECTORS
UALUE
1 4874
1 . 2895
1 . S037
0.7181
0J5913
FE
-9.5134
9 . 4482
-8 . 2050
-0.5231
0 . 4689
c;
-0.'5095
0 . 4684
-0.1319
6 . 5368
-0.4711
SI
0'. 4657
0 . 5457
0 . 8634
Q.4710
0 . 5094
AL
0 . 5033
0 . 4673
-0 . 249S
0 . 4530
8 . 5062
CA
-0.0816
8.2521
0 . 9350
0.1145
0.2061
REGRESSIONS
S = 1.8482 * 1.0459FE + 0.0316SI
FE< CLAY> * 0 - 0000 FE « 1.2534
ORGANIC S=
1.840 MINERAL 8«
1.356
-------
308 FL FG 325 1. 6SG
FE
S
SI
AL
MEAN
3.1759
SIGMA
3208.DAT
CORRELATIONS
FE
S
SI
AL
CA
0.8926 6.0443 -0.8261 -0.1567
1.8000 -9.1950 -0.0884 -0.0486
0.1050 1.0000 0.5421 -0.0329
8.4809 -0.9261 -0 0884 9.5421 1.0090 -0.2156
i . 1696
2 . 4763
2 . 6338
0 . 3000
6 . 2335
0.3715
1 . 0033
9 . 0926
9.0449
CA 3.4397 0.4129 -6.1567 -6.0436 -0.0329 -0.2156 1.0000
00
Oi
EIGENVECTORS
UALUE
1.6197
1 . 2827
Q . 8999
0 . 3634
0.4143
FE
0.0618
8 . 6423
-0 . 3423
8 . 6658
8.1527
8
-0.1769
Q . 5066
0 . 8430
-0 . 0362
-0.0128
SI
8 . 6483
-0.1226
0.2135
0.317Q
-0 . 6470
AL
9 6852
-0 . 0350
0.1711
-0.1016
0 . 6998
CA
-0 . 2743
-0 . 5609
0.3121
0 . 6668
0.2610
REGRESSIONS
S « 3.3924 + 0.7285FE + -0.1927AL
FE * 2.4271 FE * -1.2575
ORGANIC S=
2.476 MINERAL S*
0.000
-------
330 UF 2009X
FE
S
SI
MEAN
SIGMA
3270 . DAT
CORRELATIONS
FE
1.3107
1.9136
1.2200
0 . 8732
0 . 3249
0 . 7623
8 . 7289
0 . 2367
0 . 2480
0 . 3422
1 . 8003
0.8718
0 . 0424
0.1046
0 0777
SI
AL
CA
0.8718 G.0424 0.1046 0.0777
1.0000 0.0223 0.0956 0.0559
0.0223 1.0060 0.5944 -0.1070
0 0956 0.5944 1.0000 -0.1232
0.0559 -0.1070 -0.1232 1.0000
00
EIGENVECTORS
URLUE
1.9285
i . 5992
Q . 9426
8.4821
9.1276
FE
0.6621
-8 . 2295
-8 . 8772
-0 . 0465
-0 . 7077
S
0 . 6579
-0 . 2375
-0.1096
-0 . 0223
0 . 7959
SI
3 . 2220
9.6451
0.2135
-8 . 6990
0.0212
AL
0 . 2802
8 . 6227
0.1663
0.7127
-0.0041
CA
0 . 0323
-0.2951
0 . 9542
0 . 0299
0.0198
REGRESSIONS
S = 0.6951 + 0.9554FE + -0.0338AL
FE s 0.0354 FE = 1.2753
ORGANIC S=
0.695 MINERAL
1.218
-------
330FL FG1. 440 2K
MEAN
SIGMA
FE
S
SI
AL
CA
8.2286
0 . 8832
e.3163
3 . 8828
9 . 2823
8.1703
6.1707
6 . 2268
0 . 2852
0.3128
3263.DAT
CORRELATIONS
FE S
SI
AL
CA
1.0000 -0.1057 9.0080
0.1057 1.0000 -0.1486
0.0080-6.1486 1.0000
9.0434 0 9588 0.2927 1.0000 -0.1892
0.1857 -0.0484 -0.0196 -0.1892 1.0000
00434 0.1057
0.9588 -0.8484
0.2927 -8.0196
CD
EIGENUECTORS
UALUE
1.3602
1 . 2386
% . 9469
0.8817
8.6026
FE
-0.0141
-0 . 5470
0 . 7296
-0.3419
0 . 2266
S
0 . 0963
0.6180
0 . 560?
0 . 4263
0 . 3358
SI
-0.6168
-0 . 2858
-8 . 2238
0 . 3986
0 . 5793
AL
-0 . 6846
0 . 0930
0.3108
0.1169
-0 . 6422
CA
0.3761
-0.4781
0 . 0837
0.7315
-0 . 2965
REGRESSIONS
S * -i.9523
ORGANIC S=
0.8365AL + -1.2851SI
0.000 MINERAL 8*
0.889
-------
330 1.4FL GRD
3173. OAT
FE
S
SI
AL
CA
MEAN
CORRELATIONS
SIGMA
FE
S
SI
AL
CA
1 . 290?
1.8081
1 . 2273
0 . 7294
0 . 3808
0
0
8
0
e
.9400
.8069
.2572
.2196
.3898
1.
0.
0.
-8.
0.
0000
8431
1283
0109
1534
0
1
0
-0
0
.8431
0PI00
!l031
.0056
.9045
0.
0,
1.
0
0.
1203
1031
0000
5280
0681
-e
-0
0
1
0
.0109
.0056
.5280
. 9000
.0519
0.1534
0 . 0045
0.0681
0.0519
1 . 0000
CO
CO
EIGENVECTORS
UALUE
1.9029
1 . 5030
0 . 9889
6.4619
3.1435
FE
0 . 6730
-0 . 208?
9 . 9880
9 . 9376
-0 . 7086
8
8 . 6584
-0 . 2245
-0.1654
0 . 8843
0 . 6940
81
8^2639
0 . 6458
-0 . 0957
-0.7104
8.0219
RL
0.1505
0 . 693S
-0 . 0935
0 . 6976
-0 . 6254
CA
0.1462
0 . 0933
0.9771
0.0112
0.1231
REGRESSIONS
S = 8.7911 + 0.8450FE + -0.101QAL
FE< CLAY > * 0.0872 FE< PYRT > * 1.2035
ORGANIC S-
0.791 MINERAL S=
1.017
-------
338FL FG1.4 100 2K
MEAN
SIGMA
3264,DAT
CORRELATIONS
FE
SI
FE
S
S!
AL
CA
0 . 4899
1.0894
8.4681
3 . 7904
Q.3291
0 . 3996
0.3144
0.2614
0 . 3375
0 . 3005
0.7610
0 . 0926
0 . 0762
-0.0413
0.7610 6.0926
1.0000 -0.0923
~0 0023 1.0000
0.2125 0.3483
AL
0.0762
9.2125
9.3483
1.9000
8.0252 0.1662 -0.0497
CA
-0.0413
0.0252
0.1662
-0.0497
1.0000
00
VO
EIGENVECTORS
UALUE
i . S422
1 . 3862
1 . 9258
8 , S268
0.1989
FE
0 . 503
-0 . 2482
0 . 0986
-0 . 2644
-0.6602
S
9.6651
-0 . 2238
0.0943
0.1635
© . 6867
SI
0.1990
0 . 7082
0.0148
-0 . 6495
0.1919
AL
9.3084
0.5383
-0 . 4465
0 . 6099
-0.2095
CA
9.0091
0.3121
0 . 8842
-0 .' 1 085
REGRESSIONS
S * 0.6608 + 0.7964FE + 0.0474SI
FEfCLAY) - 0.8000 FE
-------
336FL FG1.4 209 2K
re
S
SI
SL
CA
MEAN
3265.DAT
CORRELATIONS
SIGMA
FE
S
8.5056 0.3204 1.9000 6.4967
1.0736 0.2665 0.4967 1.0000
0 4733 0 2462 -0.0630 -9.1264
5.8490 9.3631 0.1376 6.0950
S 3124 0.3498 -0.0332 0.0453
SI
-0.0630
-0.1264
1.0008
0.3655
0.1057
AL
0.1376
0.G950
0.3655
1.0000
-0.1316
CA
-0.0382
0.0453
0.1057
-0.1316
EIGENVECTORS
VALUE
1 . 5504
1 . 3672
1 . 0592
0 . 5443
0 . 4796
FE
0.6858
-0.9412
0.0312
-O . 4800
-0 . 5446
S
0.6750
-0.1322
0.1557
0 . 2220
0 . 6733
SI
-0 . 0797
0.7167
0 . 2579
-0 . 5453
0 . 3403
AL
0.2512
0.6817
-0.1702
0 . 6049
-0.2781
CA
-0 . 8674
-0 . 0508
0 . 9377
0 . 2389
-0.2378
REGRESSIONS
S = 1.2043 + 0.8588FE + -0.0966AL
FE = 8.6578 FECPYRT; = -0.1522
ORGANIC S=
1.074 MINERAL S«
0.000
-------
33&FL FG1.4 325 £K
3268.DAT
CORRELATIONS
MEAN
SIGMA FE
S
FE
S
SI
AL
CA
0.
<
4
I .
4.
8.
4498
8011
0522
8589
2905
0
0
0
0
6
.2116
2180
.3026
.4236
.3211
1
0
0
-e
0
.0000
.1419
,0233
.0165
.0934
9
1
0
0
0
.1419
.0000
.0945
.0168
.0151
SI
AL
6.0283 -0.9165
0.0945 0.9160
1.0D00 0.2934
0.2934 1.9000
CA
0.0934
0.0151
1987
1498
0
0
1.0000
EIGENVECTORS
UALUE
1.4617
1.1290
6 . 9274
0 . 7930
0 . 6839
FE
8 . 1835
0 . 6962
-0 . 4039
-0.5615
-0 . 0565
8
0.2174
8 . 6262
0 . 5995
9 . 3950
0.2125
SI
0.6153
-0.1345
0 . 2090
-9 . 0409
-0 . 7470
AL
0 . 5502
-0 . 3240
0.2156
-0.4369
0 . 5958
CA
0 . 4376
-0 . 0059
-0 . 6223
0 . 5798
0.1969
REGRESSIONS
S = 0.3831 + 0.9333FE « 0.0408AL
FE « 0.0000 FECPYRT) = 0.4498
ORGANIC S=
0.383 MINERAL 8*
0,618
-------
330FL FG1.6 48 2K
3266.DAT
CORRELATIONS
MEAN
SIGMA
FE
S
SI
AL
CA
FE
s
**f
SI
» *»
AL
4MM
CA
8.2486
0 9505
8 4875
4 2645
0 . 3332
0
0
0
0
0
.2130
1952
.2792
3756
.3246
1
0
0
0
. 0000
.0427
.1496
.1067
.9058
0
i
Q
0
0
. 0427 0 .
.0000 -0.
. 0646 1 .
.1733 0.
. 8356 0 .
1496
0646
0000
3789
0557
0 1967
0.1733
0 . 3789
1 . 0000
0.1460
3
0
0
0
1
.0053
.0356
.8557
.1460
.0000
EIGENUECTDRS
» if
1
1.
0.
0!
H-
,'_*C.
5879
0603
9917
mf ^r * i
$
-------
330 l.GFL GRD
MEAN
SIGMA
FE
S
SI
AL
1.4887
i.9397
1 . 3753
6 . 8538
8 . 5783
8.5418
0.2581
8 . 2489
3175. DftT
CORRELATIONS
FE
S
SI
1.0809 0.8431 0.0194
0.8431 1.0000 -0,6128
0.8184 -0.0128 1.8900
8.0472 6.0069 0.6482
AL
0.6472
0.0069
0.6402
1.0008
CA S.3658 0.3389 -8.0964 -0.1264 -0.8744 -8!1134
CA
-0.0964
-0.1264
-8.0744
-0.1134
1.0000
VO
EIGENVECTORS
'ALUE
1 . 8830
1.6531
3. 3581
8 . 3587
0.1551
FE
8 . 6767
-8.1588
8.1411
8 . 8225
-8 . 7846
8
0 . 6745
-8.1849
-8 '. 0437
8.7071
SI
9.1355
0 . 6886
0.1523
-8 . 7835
-0.8152
AL
8.1658
0 . 6774
8.1003
8 . 7879
8 . 8492
CA
-8.2331
-Q.1359
8 . 9684
8 . 8383
8 . 8387
REGRESS.! ONS
S = 0.6611 + 8.9556FE + -8.8808AL
FE = 8.0721 FEC PYRT> » 1.3286
ORGANIC S-
8.661 MINERAL S<
1.278
-------
330FL FG1.6 100 2K
FE
S
SI
AL
CA
3269.DAT
CORRELATIONS
IEAN SIGMA
6 . 4586
9 . 9866
8.1047
8.6631
8.3581
0
0
0
0
0
.2678
.2497
. 1353
,4783
.3683
1
9
9
0
-0
FE
. 0060
. 3728
.1787
.0937
,1489
0.
1.
~0
0.
-0.
S
3728
0000
0241
1813
1440
0
-8
1
0
0
SI
.1787
.0241
. 0068
.2680
.0395
0
0
9
1
0
AL
. 0937
.1813
.2680
,0000
.0758
CA
-0.1489
-0.1440
0 . 0895
0 . 0758
1 . 0000
>£>
EIGENVECTORS
UALUE
1 5633
1 . 2689
8.8551
0.7912
8 5215
FE
0.6002
-0.1795
-Q.I 480
-0.5160
-9 . 5652
c-
0J5660
-6 . 2908
0 . 4937
-0 . 03D5
0.5919
SI
0 . 3320
0 . 5704
-0 . 5889
-0.1329
0.4471
AL
0.4137
9.4728
0 . 2750
0 . 6324
-0 . 3602
CA
-9.1949
0 . 5782
0 . 5584
-0.5615
-0 . 0243
REGRESSIONS
S = 0.5653 + 0.9728FE + -0.2320SI
FECCLAY) = 0.0250 FE
-------
336FL FG1.6 325 2K
FE
S
31
AL
CA
MEAN
326?. DAT
CORRELATIONS
SIGMA
FE
1.0060 6.1582
8.1582 1.6000
0.0294 -0.8573
0.0724 -9.9266
0.3397 6.3936 -0.0526 0.0661
8 . 5964
1 . 1268
8 . 3784
7 . 9339
6.2913
0 . 2296
8 . 2628
0 . 4480
SI
0.0294
-0.0573
1.0000
0.3364
-0.2014
AL
9.9724
-0.8266
0.3364
1.9000
-0,9790
CA
-0.0526
0.0661
-0.2014
-0.0790
1.0000
VO
EIGENVECTORS
VALUE
1 . 4534
1 . 1623
0.9376
6.3127
0.6346
FE
8.1410
0 . 6973
-0.2419
-0 . 6482
-9.1236
S
-0.1369
0 . 7076
0.1097
0 . 6846
0.0381
SI
0.6551
-8 . 0388
0.1584
8.1790
-0.7157
AL
0.5911
0.0826
0 . 4959
-0 . 0867
0 . 6247
CA
-0 . 4295
^ > MMT ^m
8.0691
» *f*f
0 8114
-0 . 2678
-0.2841
REGRESSIONS
S * 0.7941 + 0.7821FE + -0.3612S1
FE - 0.1711 FECPVRT) 0.4253
ORGANIC S-
0.794 MINERAL S=
0.333
-------
APPENDIX C
RAW DATA FOR PSOC 308 AND PSOC 330 COALS
96
-------
PASS 40 AREA 1
H AMPS
TIME
FE
SI
A I...
CA
6
2
5
1
6
3
4
1
6
4
2
3 .
.1.
2
3
5
5
5
2
4
5
1
7
3
1
1
4
A
i
*..
?
6
0
2,
&
3
?
4
2
4
2
5
6
6
2
0
2
3
9
0
3 AC
36:1.
362
361
360
360
36:1.
3 6 :l.
361
1093
1130
1 1 1 4
1121
1128
1133
1138
1109
1135
1142
1121
1098
1090
1153
1079
1086
1139
1123
1142
1056
1107
1064
1148
1099
1077
1116
1114
1124
1137
1159
1081
1130
1076
1091
1116
1:1.33
1.1.06
1154
1133
1133
4
15
8
2
8
a
5
12
3
355
334
63
286
364
417
367
450
373
323
323
362
354
368
392
340
388
351
316
265
394
'> '.-> /.
£. A.. O
358
282
327
315
365
341
338
407
321
332
273
299
371
389
379
320
354
3 1 9
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
PYRIT'E
9953
9278
9843
9794
9659
9957
10253
9632
9258
1'iATA
411
526
534
490
170
137
666
278
609
544
176
690
2007
272
657
720
562
536
469
2236
368
2813
912
999
900
667
778
467
1087
118
601
2526
1943
1940
539
438
474
362
175
985
9712
10387
9389
9990
10068
10120
10412
9843
9579
697
1052
464
720
605
557
1.065
883
1123
865
458
1042
2177
511
835
828
714
843
729
1650
613
2647
1101
1179
1047
702
934
8.1.0
1731
491
857
2471
2219
2117
695
841
807
634
394
1268
0
0
109
0
0
0
0
33
51
') O '?
A» / A"
821
10046
1332
182
379
411
167
801
449
263
658
483
263
357
709
241
441
819
376
759
3761
223
569
827
818
539
393
520
299
460
727
53
704
892
f\ O '-'
£a*J
445
344
157
432
0
0
194
58
0
75
1 /N
.10
189
327
163
604
10457
.1.264
.1.78
342
239
4'')
»'
538
204
183
560
293
240
239
560
103
326
628
344
565
3895
191
488
434
375
534
225
317
212
356
575
0
404
525
':> o1";
f^. A,. W
345
216
0
292
0
/«\ f\ j>
t i /l
fit, A» T
59
"y !
/.L
107
148
158
276
105
163
123
70
A
o
243
149
0
59
156
0
73
347
403
0
244
0
334
0
189
197
180
164
378
0
284
0
0
0
1.1.9
0
287
303
66
0
163
151
97
-------
H
7
2
1
5
3
3
5
3
3
3
3
5
6
^
'5
9
5
1
3
4
6
2
3
K"
O
*..
3
K:*
3
2
ji
4
7
3
5
4
S
2
5
4
*!.
3
A
:l
^)
AMPS
1088
1135
1 1 1 8
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288
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300 .
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
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300
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300
300
300
300
300
300
300
300
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300
300
300
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300
300
300
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300
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1543
330
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455
634
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329
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363
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3 1 7
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220
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291
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392
322
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300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
FE
PYRITE
10130
9615
9767
10348
10015
9656
9412
9684
9615
DATA
720
286
566
930
554
430
256
2543
1 0 1 3
415
869
800
159
213
1678
786
257
1979
647
208
426
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661
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636
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432
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603
660
918
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4566
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1730
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94
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392
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2163
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607
265
189
192
192
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454
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246
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381
311
I ME
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
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300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
FE
341
424
275
386
1875
600
549
480
295
851
731
346
282
974
272
440
139
228
896
550
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416
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340
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1133
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610
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948
596
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966
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544
511
492
1106
311
856
1548
681
1230
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352
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3660
601
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264
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530
2470
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637
476
341
750
759
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342
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197
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538
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289
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154
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301
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TIME
SI
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280
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385
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330
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224
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327
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312
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3 1 9
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176
314
389
398
279
324
308
363
323
312
360
293
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
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300
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300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
PYRIT'E
10130
9615
9767
10348
1001.5
9656
941.2
9684
9615
DATA
931
1083
256
248
427
248
3203
1258
7 1 3
529
470
638
828
2365
1434
1539
904
451.4
847
635
826
440
1.003
708
516
380
734
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477
258
3422
670
693
625
589
548
514
1863
10291
1.01.07
9547
10467
1031.9
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1.01.1.7
1.0047
9921
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1.392
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1650
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1180
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757
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1.109
1162
710
823
1610
7500
1667
651
421
4051
1161
979
1061
865
661
876
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94
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1.5
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0
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0
540
1332
357
406
259
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410
954
553
486
390
3329
575
427
536
543
683
606
576
359
230
813
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564
1238
272
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417
695
79.1.
385
1720
56
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558
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364
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64
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300
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300
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F'YRITE
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555
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103
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311
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1679
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360
220
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'X <"i f\
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300
300
300
300
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354
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356
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356
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1179
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297
320
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316
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344
325
391
376
502
374
340
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369
336
312
297
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451
40.8
316
303
34?
432
361
TIME
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
FE
PYRITE
9727
10403
9443
9739
9827
9058
9672
9519
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DATA
859
915
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555
356
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396
355
1139
615
1107
936
902
1411
427
589
533
637
456
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253
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701
943
770
585
403
700
269
500
153
2842
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10213
9580
9633
9760
9653
976
1232
1157
924
519
960
779
634
1624
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1624
1137
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1825
801
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935
897
689
768
1233
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239
567
1647
508
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673
1103
593
717
798
3247
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0
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520
687
3369
334
203
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166
162
645
710
1027
481
585
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770
546
343
483
451
322
324
1288
963
345
120
605
641
267
617
509
464
2356
274
111
310
577
703
267
485
390
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0
0
343
73
42
103
139
115
310
312
388
1910
248
64
355
9
103
241
423
739
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528
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297
161
256
240
227
98
630
988
242
93
271
359
151
106
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415
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250
489
223
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17
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0
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0
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388
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450
497
TIME
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
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300
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300
300
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300
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613
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324
387
386
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373
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136
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1660
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760
4131
193
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147
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195
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108
260
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325
108
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267
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283
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300
300
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300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
326
359
506
648
348
1308
926
418
619
1127
635
346
1.152
163
1.048
17032
1247
21.8
210
319
419
543
240
1051
1467
1365
325
798
587
442
706
766
778
864
590
1273
921
597
923
1.232
1.083
759
1.684
521
1340
14943
1761
548
508
692
696
648
420
1417
1876
1513
753
1.1.62
945
779
751
315
279
376
41.7
31.73
6487
265
494
491
487
406
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392
32
560
214
129
759
577
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66
780
680
495
253
545
429
291.
31.9
285
134
1.24
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3137
5937
113
36.1.
293
34:1
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1263
152
97
412
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449
407
477
98
423
506
396
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519
278
105
157
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304
273
335
128
100
484
54
403
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260
62
238
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1
364
159
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0
0
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138
127
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117
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330
PASS 40 A RE;: A s
I-!
AMPS
V
TIME
FE
S
SI
AL
PYRITE
3
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6
5
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7
363
363
364
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6
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8
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9
300
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300
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9679
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212
DATA
7
A
6
6
6
7
7
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6
6
4
4
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4
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5
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1122
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1143
1141
1109
1148
1129
1165
1172
1146
1146
1130
1151
1142
1128
1136
1105
1157
1180
1164
1147
1152
257
168
292
329
216
230
379
230
126
300
274
383
286
354
476
337
209
376 '
333
260
290
362
262
312
328
313
384
359
312
265
375
414
409
350
362
353
383
396
303
302
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
2094
979
697
915
6935
2310
2468
8969
13631
4326
2892
310
941
3323
225
386
6155
230
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2413
742
820
1332
589
2936
835
878
1132
976
2936
270
358
694
472
482
378
533
337
2158
334
2536
1433
1223
1101
6243
2085
2562
7577
12535
4404
2373
770
2011
3374
45:1
698
6114
618
646
2900
1107
981
1543
855
3003
984
1029
1635
1537
3043
462
427
1022
1048
796
758
738
626
2343
809
1027
658
537
681
371
515
314
892
344
900
421
592
429
568
167
284
694
361
261
800
1217
1490
972
350
565
643
145
652
1870
1964
253
1 4 1
308
866
290
266
515
270
1247
1224
537
492
335
397
301
322
202
334
229
610
2 2 3
477
400
306
32
181
579
1 1 1
186
630
955
1040
601
316
588
567
77
216
1296
1678
154
24
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347
190
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178
245
979
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0
248
256
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271
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411
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221
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0
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340
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24-4
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$6
102
118
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TIME
FE
SI
AL
CA
5
5
4
2
11
2
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9
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6
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3
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1 79
454
339
305
514
607
142
281
648
184
511
606
428
263
1200
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212
308
781
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4
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539
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138
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294
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1079
1126
1122
1066
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1 1 1 9
1129
1 1 1 6
1118
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1139
1141
1056
1080
427
351
420
430
316
319
242
274
194
371
449
455
375
443
370
309
397
374
379
362
295
260
337
355
319
278
392
321
345
373
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
318
362
789
249
1765
1232
1290
1321
1932
821
665
897
583
175
728
1814
560
527
308
308
. 1618
759
508
640
717
909
697
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874
514
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2028
1716
1562
1935
2206
1299
844
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1078
512
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2021
769
525
636
523
1944
977
700
729
1566
1064
931
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1185
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150
529
465
215
722
518
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1127
570
488
323
360
358
434
4 1 1
292
431
195
251
237
468
237
342
1151
1820
379
5:1.5
750
382
71
381
265
129
394
182
403
339
835
387
460
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266
348
219
287
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297
135
104
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162
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151
0
33
0
92
359
213
148
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344
155
0
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97
100
12
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216
249
0
100
4
25
298
264
63
138
171
238
120
-------
TIME
FE
S
SI
AL-
5
10
12
10
5
9
5
5
9
3
2
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5
6
7
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3
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7
3
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7
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3
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360
360
360
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1160
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1181
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116(3
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1177
1126
1195
1124
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1205
1204
1118
1102
1059
1109
1091
1038
1075
1142
1137
1101
1114
1132
1157
1154
1043
996
4
3
8
3
5
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3
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3
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
PYRITE
9941
10008
9744
10238
9728
9630
9631
947.1.
9619
DATA
A.1 Ill''
1098
435
771
431
1415
822
787
632
401
368
683
396
378
495
258
928
514
580
809
310
541
556
292
337
224
441
769
684
266
1227
494
230
210
252
109
129
29
181
1658
971
10039
9837
10163
10203
10445
10080
9979
8940
10059
1815
1136
1594
.1.333
1942
1215
1372
1105
764
1244
1044
844
822
850
668
1431
1.160
975
121(3
1194
1612
1210
1162
1114
986
760
1357
1068
1100
1223
1219
998
1018
1179
1046
966
793
1069
1877
1430
33
0
0
0
0
0
0
0
0
955
400
675
1629
1287
2603
784
824
1401
263
391
360
382
389
112
263
723
471
477
297
1113
542
320
442
489
258
702
637
. 750
1089
219
234
71
179
43
58
1.07
182
966
2949
1
0
0
17
0
30
0
250
0
717
285
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835
062
2189
506
821
1650
327
410
239
242
310
100
190
528
359
319
197
1110
377
265
306
247
183
306
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342
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116
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55
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187
487
173
74
324
49
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144
264
4S7
106
107
121
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TIME
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6
4
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300
300
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300
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300
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300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
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300
300
300
300
300
300
300
318
425
72H
307
1063
595
35 B
1267
1064
643
669
598
802
676
198
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2194
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409
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649
1573
1 05 1
1418
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1144
613
703
319
367
463
548
1771
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849
385
453
1608
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378
881
929
944
637
389
839
467
1260
2122
1316
423
1051
1252
1301
1154
1695
1196
1509
1491
938
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1602
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1209
928
988
2210
1434
1249
1444
1524
1884
1315
1192
1129
1663
1338
1322
1090
736
1154
1292
1977
2059
17113
1550
1631
2706
1237
1422
3066
1761
1360
1168
1104
1272
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1904
1566
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497
670
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525
491
1773
1031
1154
1518
944
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274
101
290
582
1761
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1 4 1 8
1O99
623
622
63b
563
:L270
966
140
194
223
243
483
757
2176
929
748
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7 .1. 7
743
1083
1846
1736
687
226
407
394
652
517
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392
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4/4
742
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370
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1080
1 23 1
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313
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1180
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374
293
491
340
9S8
873
77
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126
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341
426
1 524
5151
510
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521
1680
1557
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385
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127
176
565
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508
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398
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137
198
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0
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161
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161
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119
189
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236
113
153
183
351
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3
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9
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2
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1.079 .
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1069
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loss
lose
1093
1033
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1133
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1114
1114
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1082
1088
1094
1103
7
5
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2
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3
5
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
400
679
1418
1290
2298
1473
4872
536
910
1399
13/1
3480
2439
2295
1783
526
289
721
364
315
412
531
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727
304
328
503
1263
1893
1264
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10A3
1486
1667
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1918
4692
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1863
3106
2819
2231
1681
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726
983
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83A
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1027
1104
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837
811
1406
1801
2379
1596
413
293
929
594
1129
914
646
667
801
650
1350
133
416
701
1301
439
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til til \f
E? AU^Ui
771
298
736
489
434
575
132
145
903
451
411
3£J3
364
168
6BS
404
897
6B7
706
509
501
568
969
23
328
565
90S
220
192
349
334
277
684
433
391
347
110
90
434
224
331
238
133
271
256
216
194
299
a
182
119
0
116
30
57
122
269
194
137
123
0
0
60
142
232
46
111
155
0
0
162
1S4
123
-------
308 PASS 40 AREA 2
H AMPS V
TIME
FE
S
81
AL-
4
6
:L1
15
4
8
10
5
3
4
7
5
8
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7
6
6
5
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9
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4
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4
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6
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364
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363
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364
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363
1 1 1 5
1125
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11 3 6
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1146
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1
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
PYRITE
9610
9823
9778
10030
9403
10045
9995
8706
9755
DATA
2422
1081
2455
359
1121
420
1820
1250
698
654
589
845
326
2116
346
1000
13:1.8
1275
741
1379
1004
3011
2015
542
6773
2569
1278
242
370
665
2087
616
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481
266
572
914
1274
364
701
9978
10074
9922
10091
10388
10187
9897
9278
10057
3005
4243
2523
867
1931
1733
1571 ,
1559
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PYRITE
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308 PASS 40 AREA 6
H AMPS
TIME
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300
300
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300
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300
300
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300
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300
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300
300
300
300
300
300
300
300
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300
300
300
300
300
300
300
300
300
300
300
300
PYRITE
10096
9649
9775
10021
10201
10202
10055
9647
9725
DATA
1748
2780
625
474
622
421
640
1981
1096
871
990
2783
1217
801
1204
1162
1264
792
2337
635
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755
1087
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670
1760
719
1312
1816
1079
744
723
1033
1465
1092
509
815
1420
1291
1183
10023
10333
10010
10459
10236
1029.8
10156
9412
10316
1956
2443
1288
1251
1295
1443
1584
2147
1715
1576
1434
2182
1342
1437
1564
1326
1544
1308
1935
1063
1235
1376
1479
1242
1066
1917
1160
1948
1653
1590
1072
1162
1370
1809
1559
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H AMPS
TIME
FE
S
SI
AL
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4
7
3
5
4
3
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3
4
4
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6
2
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PASS 40 AREA 7
H AMPS
TIME
FE
SI
AL
CA
5
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300
300
300
300
300
300
300
300
300
300
300
300
300
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PYRITE
10096
9649
97713
10021
10201
10202
10055
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597
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316
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108
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83
139
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H
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TIME
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5
6
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5
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1531
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2937
1408-
770
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660
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765
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2810
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1513
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903
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262:1
372
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498
198
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157
435
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40
141
-------
,308 PASS 40 AREA 8
H AMPS
TIME
FE
S
SI
A I...
CA
8
4
6
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4
7
4
8
6
y
8
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6
4
4
7
6
13
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6
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7
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359
358
359
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1066
1:1.39
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1103
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1104
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1093
1147
1148
1095
1107
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1137
1081
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1134
1166
1063
1086
1091
1106
1097
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1127
1132
1088
1108
1130
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1144
1117
1130
1117
1173
9
3
4
3
9
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6
6
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300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
PYRITE
9375
9746
10362
99 IS
9524
9820
8977
9205
9788
DATA
476
269
351
710
319
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684
318
769
394
259
345
170
336
0
350
354
750
360
400
251
152
221
465
588
556
311
367
376
324
443
95
116
678
1323
140
701
290
176
132
10501
10604
10080
10344
9648
9974
9730
9278
10053
1053
1217
990
1147
1013
1270
1441
1306
1582
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999
1008
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1210
876
708
1260
1519
848
1191
1115
1350
1069
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-------
H AMPS
TIME
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AL
5
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290
254
163
48
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0
123
144
-------
APPENDIX D
CORRELATIONS AND EIGEVECTORS FOR EPA- SUPPLIED COALS
145
-------
1632A FG 2080X
3235.DAT
CORRELATIONS
MEAN SIGMA
FE 1 . 9293 8 . 4023
S 1 . 5898 0 . 3365
$T 18.3755 1.3600
AL 4 . 9257 8 . 4769
CA 1 . 3249 8 . 5945
EIGENUECTORS
U£Lf IE FE
*f l» turn '»** «« * **'
i *>»" . .»* **t y*'*". "1 "T*
~7CTl,jS^ -yj C y 1 j
f * 1 "» * ?fj t Su'Lai tli *,«^
1 ' 224^ -0 . 3838
1 8119 -8.0279
0 6065 8.8435
0 .' 3975 -0 . 7203
FE
1 . 0008
0 . 5605
0.2181
0 . 2280
-0 . 0498
:"'
0J5042
~0 . 5447
0.1292
-0.85:19
0 . 6555
3
0 . 5605 0 .
1 . 8000 0 .
0.8411 1.
0 . 8489 0 .
0.6224 -0.
SI
0.4215
0 . 5389
-0.1670
-0 . 7928
8 . 1 088
SI AL
LH
2131 8.2288 -Q.0498
0411 0.0489 8.0224
0880 0.3841 -8.8224
3841 1.8800 8.8675
8224 8 . 0675 1 . 8000
AL
8 . 4268
8 . 5526
0 . 8537
0 . 6928
8.1752
CA
-0 . 0084
0.1253
0 . 9756
-0.1503
-0 . 8998
REGRESSIONS
S = 2.0229 + 9.9045FE + -0.2771AL
FE = 1.5098 FECPYRT> = -8.4797
ORGANIC 3=
1.589 MINERAL S=
0.000
-------
1635 FG 2000X
FE
S
SI
AL
CA
MEAN
9.4094
0.6606
1.5514
3236.DAT
CORRELATIONS
SIGMA
FE
S
SI
AL
CA
0.2272 1.8000 0.0178-0.0277 g.014| g-g£gf
0.1737 0.0178 1.00Q0 -0.0726 -g.g37g 0.0057
* ^-r 0.9734 -0.0277 -0.0726 1.0000 0.0012 &.0g§3
0'5342 01971 00149-0.0370 0.0012 1.0000 0.1260
34723 0.6153 0.0766 0.0057 0.0063 0.1260 1.0000
EIGENUECTORS
UALUE
V~L»Vfc»
1 . 1552
1 0893
0 9709
9ff ^f t ^^^f
0 . 9334
0 . 8511
FE
0.3885
0.3350
-0.7805
-0 . 1993
-0 . 2966
S
-0 . 0753
0 . 6645
0.1612
0.6871
-0 . 2338
SI
-0.0016
-0 . 6436
-0 . 3678
0 . 6429
-0.1930
AL
0.6115
-0 . 1749
0 . 4780
-0.0804
-0 . 6005
CA
0.6852
0 . 0377
0 . 0328
0.2617
0 . 6779
REGRESSIONS
S = 1.0583 + -0.0317CA + -0.185481
ORGANIC S= 0.661 MINERAL S= 0.000
-------
PHS 408 FG 2000X
3237.DAT
FE
S
SI
AL
CA
MEAN
0.6712
1.5243
4.7923
1.6665
SIGMA
CORRELATIONS
FE S
SI
AL
3 .'3401 1.0729 0.0302 0.5610 0.0812
CA
0 2935 1.0000 -0.0432 -0.0023 0.8600 0.0302
0'2964 -0.0432 1.0000 0.0392-9.0417 0.5610
1.'2726 -0.0023 0.9302 1.0060
0.3026 0.0600 -0.0417 0.5267
9.5267 0.0812
1.0000 0.0223
0.0228 1.0000
H
J>
00
EIGENUECTORS
UALUE
1 . 5978
1.5017
1 . 0039
0.4661
0.4314
FE
0 . 0260
0.0S51
0 . 9S89
-0 . 0376
-0 . 0809
S
0 . 5557
-0 . 4408
-0.0351
-0.0100
-0 . 7040
SI
0 . 4396
0 , 5452
-0.1190
-0 . 7035
0.0216
AL
0 . 3744
0 , 6023
-0 . 0972
0.6991
-0.0912
CA
0 . 5976
-0 . 3722
0.0818
0 . 0926
0 . 6994
REGRESSIONS
S = 0.7856 + -0.1110AL + 0.2765CA
ORGANIC S= 0.786 MINERAL 8-
0.739
-------
PHS 506 FG 2000X
MEAN
SIGMA
3238.DAT
CORRELATIONS
FE S
SI
AL
CA
FE 1 3164 0 5878 1.8000 0.6880 -0.0301 -0.0626 0.1237
S 1.9644 0.5540 0.6880 1.0000 0.0111 0-0811 0.1246
SI 4.1851 0.3815-0.0301 0.0111 1-0000 0.4660 -0-1274
AL 30753 03193-0.0626 8.0811 0.4660 1.0000-0.0106
CA 15401 Si 6117 0.1237 0.1246-0.1274-0.0106 1.0000
EIGENUECTORS
UALUE
1.7336
1 . 4880
0 . 9556
0 . 5298
0 . 2937
FE
0.6851
0 . 0395
-0.1907
-0.1391
-0 . 6880
S
0 . 6784
0.1476
-0.1342
0.1560
0 . 6897
SI
-0 . 0879
0 . 6952
-0 . 0049
-0 . 7068
0 . 0966
AL
-0 . 0429
0 . 6865
0 . 2484
0 . 6509
-0 . 2037
CA
0 . 2467
-0 . 1487
0 . 9402
-0.1816
0.0132
REGRESSIONS
S = -0.2305 + 0.9520FE + 0.2250SI
FET.CLAY) = 0.0000 FE(PYRT) = 1.3164
ORGANIC S=
0.800 MINERAL 8=
1.964
-------
PHS 534 FG 2000K
3239.DAT
CORRELATIONS
MEAN
SIGMA
FE
S
SI
AL
CA
FE
S
SI
AL
CA
2.3614
3.1607
3 . 6834
5 . 4745
0 . 6820
1.6575 1.0080 0.9683 0.0938 0,1196 -0.0723
1.7882 0.9683 1.0000 0.0676 0.0958 -0.0523
1.0559 0.0938 0.0676 1.0000 0.6537 0.2111
0.6211 0.1196 0.0958 0.6537 1.0000 0.2423
0 . 4368 -0 . 0723 -0 . 0523 0.2111 0 . 2423 1 , 0000
Oi
o
EIGENUECTORS
UALUE
2 . 0606
1.7142
0 . 8495
0 . 3448
0 . 0309
FE
0 . 6289
-0 . 3096
0 . 0769
-0.0151
-0 . 7088
S
0.6215
-0.3214
0.1162
-0.0139
0 . 7048
SI
0.3163
0 . 5634
-0.3113
-0 . 6967
0.0156
AL
0 . 3372
0 . 5603
-0 . 2445
0.7158
0.0127
CA
0 . 0665
0.4115
0.9077
-0 . 0430
-0.0214
REGRESSIONS
S - 0.9491 -I- 1.0785FE + -0.0612AL
FE = 0.3107 FECPYRT) = 2.0507
ORGANIC S=
0.949 MINERAL S=
2.212
-------
PHS 546 FG 2000X
FE
S
SI
AL
MEAN
SIGMA
3246.DAT
CORRELATIONS
FE S
SI
AL
CA
1,5530 0.7185 1.0800 0.8753 0.0018 0.0553-0.1117
3.6031 0.7851 0.8753 1.0090-0.1050 0.0296-0.1411
8.8520 1.0551 0.0018-0.1050 1.0000 0.2627 0.1305
4.8227 0.3774 0.0553 0.0296 0.2627 1.0000 0.1283
CA 0.6327 0.4594-0.1117-0.1411 0.1305 0.1283 1.0080
Ul
EIGENVECTORS
UALUE
1 . 9208
1 . 3469
0 . 8829
0.7313
0.1180
FE
0 . 6836
0.1439
0 . 0922
-0.1115
-0 . 7008
S
0.6931
@ . 0660
0.1167
-0.0192
0 . 7080
SI
-0.1051
0 . 6246
-0 . 3626
-0 . 6782
0 . 0860
AL
0 . 0050
0 . 6504
-0 . 2343
0 . 7225
-0 . 0073
CA
-0 . 2033
0.4023
0.8896
-0.0721
0.0129
REGRESSIONS
S = 3.1161 + 1.1089FE + -0.2561AL
FE< CLAY ) * 1.1138 FE< P YRT > «* 0.4392
ORGANIC S=
3.116 MINERAL S-
0.487
-------
PHS 578 FG 2000X
FE
S
SI
MEAN
2.2319
6.3396
6.3561
SIGMA
3241.DAT
CORRELATIONS
FE
S
SI
AL
CA
0.8378 1.0900 0.8667 -0.1013 -0,0751 -0.1215
0.8477 0.8667 1.0000 -0.1565 -9.1159 -0.1610
1.0952 -0.1013 -0.1565 1.0060 0.2484 -0.0364
AL 3!0251 0^2940 -0.'0751 -0.'1159 0^2484 1.0000-0.1615
CA 2.4743 1.0061 -0.1215 -0.1610 -0.0364 -0.1615 1.0000
Ln
EIGENUECTORS
UALUE
1 . 9650
1 . 2974
0 . 8922
0.7158
0.1297
FE
0 . 6645
0.1178
0 . 2339
-0.0601
-0 . 6974
S
0.6781
0 . 0864
0.1491
-0.0344
0.7136
SI
-0.2152
0 . 5278
0 . 6084
0 . 5508
0 . 0400
AL
-0.1615
0 . 6583
0.0341
-0 . 7338
0.0313
CA
-0.1617
-0.5167
8 . 7428
-0.3916
0.8421
REGRESSIONS
S = 4.4801 + 1.0207FE + -0.1384AL
FECCLAY) = 0.4101 FECPYRT) = 1.8218
ORGANIC 8=
4.480 MINERAL S=
1.859
-------
APPENDIX E
DESCRIPTION OF COMPUTER PROGRAMS
The computer programs for the MASC analysis and MAPS analysis are
variations of a single program and will be discussed in this appendix.
source listing for the programs will be available from the authors upon
request.
The program can be divided into three logical units: (1) input-output
and normalization, (2) stoichiometric analysis and multivariate analysis,
and (3) particle size fitting.
The first section of the program accepts the data which was encoded
°n mass storage files by the scanning electron microprobe control programs.
The data for each element analyzed by the microprobe can be normalized in a
variety of ways. The data is normalized to unit microprobe current. The
first nine points of each data set are taken on a pure pyrite sample and
the average of these points is used to reduce the iron and sulfur data to
^-ratios. A correction for the effectiveness of the pyrite standard signal
relative to a zero density pyrite standard is either applied to the raw data
°r alternatively to the output of the programs. Plots of sulfur versus iron
'or other combinations of elements) are generated. The main program has
provisions for several different kinds of cuts to the data set. Backgrounds
°f several kinds (dark currents, dispersed distributions, etc) may be subtrac-
ted from each element, particularly iron and sulfur. An ellipse respresent-
lr»g any given concentration (standard deviation) is computed from the data
means and correlation for the bivariate distribution of sulfur and iron and
the ellipse is plotted on the data set. For the purposes of the MAPS parti-
cle size analysis fits, data points representing large pyrite rocks, lying
°utside of a 2.0 or 3.0 standard deviation ellipse are discarded. This also
eliminates points which are extraneous glitches in the data as sometimes
occur. The results of the correlation analysis (elemental means and standard
deviations, bivariate and multivariate correlation coefficients, pyrite
stoichiometry, and organic sulfur content), are recorded in tabular form on
mass storage for later analysis and review.
The correlation phase of the programs takes the normalized data vectors
prepared in section one and computes means, standard deviations, bivariate
correlations, etc. A multivariate components analysis (Ref. 1) is applied to
Multielement data sets to separate the pyrite, clay and organic contributions
153
-------
to the various elements by calculating the eigenvalues and eigenvectors of t
correlation matrix.
The third major subdivision of the program implements the MAPS particle
size analysis. The data vector for iron and sulfur are binned in a given
number of bins and the resulting histogram data distributions plotted. The
least square fitting subroutine is called with either the iron, sulfur or
and sulfur data distributions as input. In the present analysis, each data
bin is weighted equally but there is provision in the program for different
weights for each point. A generalized least squares fitting procedure yields
multiparameter fits of a functional form, here V (S) as given in Eq. II-^i to
the data base. The residual from the fit is computed and the results plotted
along with the data histograms.
154
-------
TECHNICAL REPORT DATA
(Please read Inunctions on the reverse before completing)
NO.
.
EPA-600/7-80-106
2.
3. RECIPIENT'S ACCESSION NO.
4- TITLE AND SUBTITLE
Physical and Chemical Characterization of Coal
6. REPORT DATE
May 1980
6. PERFORMING ORGANIZATION CODE
. AUTHOR(S)
D. G. Hamblen, P. R. Solomon, and R. H. Hobbs
8. PERFORMING ORGANIZATION REPORT NO.
9. PERFORMING ORGANIZATION NAME AND ADDRESS
United Technologies Research Center
East Hartford, Connecticut 06108
10. PROGRAM ELEMENT NO.
TJSTE624
11. CONTRACT/GRANT NO.
68-02-3116
12. SPONSORING AGENCY NAME AND ADDRESS
EPA, Office of Research and Development
Industrial Environmental Research Laboratory
Research Triangle Park, NC 27711
13. TYPE OF REPORT ANC
Final; 5/78-1/80
NO PERIOD COVERED
14. SPONSORING AGENCY CODE
EPA/600/13
15. SUPPLEMENTARY NOTES IERL_RTP project officer is Frank E. Brlden, Mail Drop 62,
919/541-2557.
e. ABSTRACT
rep0rj. describes an automated scanning electron microprobe method
of analyzing sulfur forms and mineral matter in coal. The microprobe is used to
measure the spatial distribution of a number of elements (including Fe, S, Si, Al,
Ca, and K) on a scale where individual grains can be observed. These data are then
analyzed to extract the following information: organic sulfur concentration; mineral
sulfur concentration; total sulfur concentration; major mineral concentrations,
including A12O3, SiO2, K2O, and CaCO3; and stoichiometry of iron sulfide, FeSx.
The procedure is fully automated: all of the above information is obtained on a 200 mg
coal sample in less than 15 minutes. Sulfur forms and total sulfur were reproducible
to within 0. 1 wt %. In addition, estimates of the pyrite particle size for two coals
were obtained from the spatial distribution of the Fe and S data, and these estimates
are compared with the results of a washability study. A commercial elemental ana-
lyzer, evaluated for use in determining nitrogen in coal, gave results that were
reproducible and accurate to within 3%.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b.lDENTIFIERS/OPEN ENDED TERMS
c. COS AT I Field/Group
Pollution
oal
Properties
Analyzing
Automatic Control
canning
Electron Probes
Sulfur
Minerals
Stoichiometry
Nitrogen
Pyrite
Pollution Control
Stationary Sources
Scanning Electron Mi-
croprobe
Characterization
13B
08G 07B
14G
14B 07D
3. DISTRIBUTION STATEMENT
Release to Public
19. SECURITY CLASS (ThisReport)
Unclassified
21. NO. OF PAGES
163
20. SECURITY CLASS (Thispage)
Unclassified
22. PRICE
EPA Form 2220-1 (9-73)
155
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