Energy & Fuels 1990,4, 748-754
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Shale was placed in a screw-top vial and covered with benzene. The vial was shaken vigorously and allowed to stand for 1 day. The benzene was decanted from the vial and evaporated with a stream of nitrogen. The resulting residue was then dissolved in T H F and electrosprayed on a nitrocellulose-coated Mylar target. The initial PDMS results, while weak, indicated the C29through C,, vanadyl geoporphyrins to be the most abundant components (in agreement with the PDMS results obtained from the polar fraction). These preliminary results suggest that PDMS may be a viable simple and fast method for screening oil samples for metalloporphyrins due to the apparent transparent nature of many of the other components present in the extract. Further studies are needed to optimize the simple cleanup procedure thereby improving the PDMS spectra and to investigate the levels of detection of metalloporphyrins in oil samples using this simple
methodology. Also better control of the BIOION instrument parameters, which are designed for high mass determinations, may enable the two geoporphyrin components, DPEP and ETIO, to be resolved, enabling DPEP/ETIO ratios to be calculated. This would further enhance the screening capability of PDMS as relative maturation values could be determined.6 Karl V. Wood,* Connie C. Bonham Departments of Chemistry and Biochemistry Purdue University, West Lafayette, Indiana 47907 Mei-In M. Chou Illinois State Geological Survey Champaign, Illinois 61820 Received May 2, 1990 Revised Manuscript Received July 12, 1990
Articles Automated Image Analysis of Minerals and Their Association with Organic Components in Bituminous Coalst W. E. Straszheim*J and R. Markuszewskis Department of Civil and Construction Engineering, Department of Geological and Atmospheric Sciences, and Fossil Energy Program, Ames Laboratory, Iowa State University, Ames, Iowa 5001 1 Received April 16, 1990. Revised Manuscript Received September 26, 1990
Samples of 100-mesh Upper Freeport, Pittsburgh No. 8, and Illinois No. 6 seam coals from the Argonne Premium Coal Sample Program were analyzed by scanning electron microscopy based automated image analysis (SEM-AIA) for mineral composition, particle size, and association of the minerals with the organic matrix. The association results were used to predict cleanability, i.e., anticipated coal recovery versus mineral rejection, for density- and surface-based cleaning processes. Distributions showing the association of each mineral with coal indicate preferential liberation for calcite in all three coals and for some of the pyrite in Illinois and Pittsburgh coals. Generally, the Pittsburgh coal appeared to be the most easily cleaned, based on association considerations only. The predicted cleanabilities of the Upper Freeport and Illinois coals were similar for density-based processes, and the Upper Freeport coal appeared to be slightly more cleanable than the Illinois coal for surface-based processes.
Introduction Mineral matter in coal presents problems on a number of fronts. Of greatest significance during combustion is the emission of sulfur dioxide that results mostly from pyrite in the coal. In addition, mineral matter leads to wear of components in the coal-handling circuits, to boiler slagging and fouling problems, and to waste streams requiring disposal. Therefore, physical coal cleaning is em'This paper was originally scheduled to appear with the papers from the Symposium on Research with Argonne Premium Coal Samples [ E n e r g y Fuels 1990, 4 ( 5 ) ] , but missed that issue due to delays in the Editorial office. Ed. Department of Civil and Construction Engineering. !Department of Geological and Atmospheric Sciences.
ployed as a means for addressing these issues prior to combustion. Current planning and implementation of physical cleaning are typically based only on indirect or incomplete characterization data for the coal. Float-sink tests are often performed for a range of particle sizes and specific gravities to determine the cleanability of a coal. Laboratory-scale cleaning tests may also be run under a variety of conditions. However, it is not general practice to characterize the mineral matter in coal directly in order to determine the potential for its removal, i.e., the cleanability of the coal. Yet, physical separation fundamentally depends on the association of the individual mineral matter grains with the coal matrix. Large, liberated mineral particles may
o a a 7 - o s z ~ ~ ~ o ~ z ~ o ~ - o 7 ~ a0 ~ o1990 z . 5American o~o Chemical Society
A I A of Minerals in Bituminous Coals
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be easily removed from the desirable organic portion of the coal, while very small or finely disseminated mineral particles may be difficult to separate from the coal. For a given particle size distribution, there will be some portion of the mineral matter that will be completely liberated from any associated coal, another portion of the coal that is completely liberated from the mineral matter, and a third class of particles that consists of both coal and mineral matter in various degrees of association. Depending on the amount and spatial distribution of the coal and mineral matter, these particles can be expected to take various paths through the coal processing circuit depending on the operating conditions. For example, a cleaning process that separates particles on the basis of particle density would reject particles having more than a certain weight fraction of mineral matter in the particles. A process that uses differences in surface characteristics to separate particles would sort particles largely on the basis of the nature and relative amount of the phases present on their surfaces and thus would reject all particles (including coal particles) having a large fraction of their surface covered by minerals. Electron microscopic techniques are uniquely suited to determine the association of the coal and minerals from cross sections of the particles. Particle size and the association of coal and mineral phases are readily determined from backscattered electron images. Characteristic X-ray emissions from the sample excited by the electron beam are used to obtain elemental analyses of mineral grains which in turn are used to identify the mineral phases. The entire technique has been automated under computer control so that statistically significant numbers of particles can be characterized. Such approaches have been employed in the mineral industry for years,1i2but due to the low profit margin on coal and because procedures had not been well developed for measuring relevant characteristics, such techniques have not yet gained wide usage in the coal industry. The association of mineral matter and coal has been studied previously, primarily by optical microscopy.M A few studies attempted to use SEM-based AIA techniques, but they were limited in their approach and capabilities.6 However, for several years, we have been developing SEM-AIA techniques at the Ames Laboratory and applying them to mineral matter in coal with particular emphasis to coal beneficiation.'-'O In this work, the mineral matter in samples of 100-mesh Illinois No. 6, Pittsburgh No. 8, and Upper Freeport coals ~
(1)Petruk, W. In Mineral Processing Design; Yarar, B., Dogan, Z. M., Eds.; Martinus Nijhoff Boston, 1987;pp 2-36. (2)Reid, A. F.; Gottlieb, P.; MacDonald, K. J.; Miller, P. R. In Applied Mineralogy; Park, W. C., Hausen, D. H., Hagni, R. D., Eds.; TMS-AIME Warrendale, PA, 1985. (3)Harvey, R. D.;Demaris, P. J. Size and Maceral Association of Sulfide Grains in Illinois Coals and Their Washed Products; Illinois State Geological Survey: Champaign, IL, 1985. (4)Vleeskens, J. M.; Bos, P.; Kos, C. H.; Roos, M. Fuel 1985, 64, 342-347. (5)Smit, F. J.; Odekirk, J. R. Quarterly Progress Report DOE/PC/ 72007-2;AMAX Extractive Research and Development: Golden, CO, 1984. (6)Moza, A. K.;Austin, L. G.; Johnson, G. G., Jr. In Scanning Electron Microscopy/I978/I; Johari, O., Ed.; SEM Inc.: Chicago, 1978;pp 473-476. ( 7 ) Straszheim, W. E.; Younkin, K. A.; Markuszewski, R.; Smit, F. J. Prepr. Pap.-Am. Chem. SOC.,Diu. Fuel Chem. 1988, 33(2),64-72. (8)Straszheim, W. E.; Yousling, J. G.; Markuszewski, R. In Mineral Matter and Ash in Coal; Vorres, K. S.,Ed.; American Chemical Society: Washington, DC, 1986 pp 449-461. (9)Straszheim, W. E.; Markuszewski, R. In Process Mineralogy I X Petruk, W., Hausen, D., Hagni, R., Pignolet-Brandom,Eds.: TMS-AIME Warrendale. PA, 1990;pp 155-166. (10)Straszheim, W. E.; Markuszewski, R.Prepr. Pap.-Am. Chem. SOC.,Diu. Fuel Chem. 1989, 34(3),648-655.
Table I. General Characteristics of the Coals Examined from t h e Argonne Premium Coal Samole Program' PittsUpper Illinois burgh Freeport No. 6 No. 8 proximate analysis (AR) moisture, % 1.13 7.97 1.65 ash, 90 13.03 14.25 9.10 volatile matter, % 27.14 36.86 37.20 heating value, Btu/lb 13 313 10999 13404 ultimate analysis ( % MAF) C 85.50 77.67 83.20 H 4.70 5.32 5.00 N 1.55 1.37 1.64 0.74 0.89 2.38 S (erg) 0 7.51 13.51 8.83 forms of sulfur (% dry) pyritic 1.77 2.81 1.37 sulfate 0.01 0.01 0.01 organic 0.54 2.01 0.81 total 2.19 2.32 4.83 a
Adapted from ref 11.
from the Argonne Premium Coal Sample Program was characterized for particle size, mineral phase, and the extent of association with the coal matrix in order to provide basic insights to the mineral matter in these coals, especially in predicting the behavior of mineral matter during physical cleaning.
Experimental Section Materials. Ampules of 100-mesh Illinois No. 6, Pittsburgh No. 8, and Upper Freeport coal were obtained from the Argonne Premium Coal Sample Program. In Table I the general characteristics of these coals are listed." Ash contents ranged from 9.1% in the Pittsburgh to 14.3% in the Illinois coal (on an asreceived basis). Total sulfur content varied from a little more than 2% in the Pittsburgh to nearly 5% in the Illinois sample (on a dry basis). Slightly more than half of the sulfur in all three coals was pyritic. The mineral content in these samples ranged from about 11%in the Pittsburgh to almost 19% in the Illinois sample, measured either as a low-temperature ash or estimated by using a modified Parr formula.12 Methodology. Samples were prepared for microscopic analysis by embedding about 2 g of coal in carnauba wax.13 The coal was mixed with the molten wax and poured into a pellet press to cool and harden under 4000 psi applied pressure. The solidified pellet was sliced along its axis, and the two resulting faces were polished by using standard petro raphic procedures. The pellet halves were coated with 150 of carbon prior to SEM examination. The pellets were examined with an image analysis system consisting of a JEOL 840A scanning electron microscope (SEM), a KEVEX DELTA-V energy-dispersive X-ray analyzer (EDS) equipped with a QUANTUM light-element detector, and a LeMont Scientific DB-10 automated image analyzer (AIA). The SEM was operated at a beam voltage of 15 kV, with a beam current of 0.7 nA, and at magnifications ranging from 50X to 2000X in the backscattered electron imaging mode. The LeMont Scientific AIA system was used to raster the electron beam across the sample at a resolution of 512 points in x and y directions. Coal and mineral particles were identified by comparing the brightness of the backscattered electron signal with two thresholds, one set between the average brightness of coal and carnauba wax corresponding to an atomic number factor (ANF) of 5.8 and the other set between the brightness of coal and most minerals, ANF = 8. Each of the coal and mineral features comprising a composite particle was characterized for area, area-equivalent diameter, and
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f
(11)Vorres, K. S. Users Handbook for the Argonne Premium Coal Sample Program; Argonne National Laboratory: Argonne, IL, 1989;pp
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11-12
(12)Given, P. H.; Yarzab, R. F. In Analytical Methods for Coal and Coal Products; Karr, C., Jr., Ed.; Academic Press: New York, 1978;Vol. 11, pp 3-41. (13)Straszheim, W.E.;Younkin, K. A,; Greer, R. T.; Markuszewski, R. Scanning Microsc. 1988,2(3),1257-1264.
Straszheim and Markuszewski
750 Energy & Fuels, Vol. 4, No. 6, 1990 Table 11. Mineral Matter (MM) in 7'0 of the Three Coals PittsUpper burgh Freeport Illinois No. 6 No. 8 ASTM Results 11.15 18.84 MM by Parro 15.72 2.58 5.30 3.33 pyriteb ISGS resultsC 18.1 10.9 MM by LTAd 15.3 2.4 5.5 3.4 pyrite 1.7 3.4 quartz 1.5 0.5 1.9 calcite 1.0 AIA resultse 22.4 13.6 12.8 MM by AIA 2.7 8.8 2.6 pyrite 1.6 2.9 0.8 quartz 0.8 1.5 0.6 calcite "Parr formula: MM = 1.13(ash) + 0.47(pyr S) + 0.5(C1) (from ref 12). bPyrite = (pyr S)/0.53 (from stoichiometry). cAnalyses by Richard Harvey, Illinois State Geological Survey (from ref 11). dLTA = low-temperature ashing. 'This work. the amount of perimeter in contact with coal, mineral matter, or mounting medium. In addition, the X-ray intensities of 21 elements of interest, ranging from oxygen to zinc, were recorded for each of the mineral particles. The relative intensities of these elements were compared to a list of allowable ranges of intensity for up to 18 minerals normally found in coal. Such classification has been described previously.8 The mineral particles were identified according to the first phase for which the measured intensities fell within the specified ranges. Handbook values of mineral densities were then used to convert area fractions to weight fractions. Such measurements were repeated for about 6000 composite particles which included about 2000 mineral grains. Analyses required about 15 h of instrument time per coal sample. D a t a Presentation. Particle data were classified according to several characteristics of interest to coal preparation and utilization. In the first presentation, coal and mineral grains were classified according to particle identity and area-equivalent diameter. Such size distributions for each mineral, which have been available and used for some time,8J4provide an initial indication of the potential for removing these minerals from the coal. However, due to the different natures of the minerals, it is possible for some mineral grains to have a very fine size distribution and yet be liberated from the coal. Therefore, a second type of distribution was prepared showing the weight fraction of sample as a function of particle mineral content. Particles were classified into fractions having no apparent mineral matter, fractions containing less than 20% mineral matter (by weight), or fractions consisting of 20-40%, 40-60%, 60-80%, or 80-100% mineral matter. Since the mineral content of a particle can be used to calculate the particle density, such a format is helpful for predicting the response of a coal to density-based cleaning. A third type of presentation was prepared showing a distribution of the coal sample according to the fraction of the particle surface covered by mineral matter. The fraction of surface covered was calculated from the recorded data on the amount of particle perimeter in contact with the three possible phases, Le., coal, mineral, or mounting media. Data for the individual features were combined to determine the cumulative amount of each phase apparent on the particle surface. The resulting distribution helps to provide a n indication of the response of a coal sample to surface-based cleaning, given an understanding of the dependence on particle behavior on surface composition. The precision of image analysis results is governed by the rules of counting statistics. Variability decreases with the square root of the number of particles counted within a class. Thus, for the analyses in this paper, precision in the results is about f0.08% for values around 0.1%, &0.25% for values around 1 % , and &0.790/0 for values around 10%. (14) Huggins, F. E.; Huffman, G . P.; Lee, R. J. In Coal and Coal Products: Analytical Characterization Techniques; Fuller, E. L., Ed.: American Chemical Society: Washington, DC, 1982; pp 239-258.
Table 111. Weight Distribution (in %) by Phase and Particle Size for Argonne Premium Coal Samples UDoer FreeDort Coal area-equivalent diameter, Fm ~
pyrite kaolinite illite quartz calcite other total
phase pyrite kaolinite illite quartz calcite other total
phase pyrite kaolinite illite quartz calcite other total
0.49 0.28 0.48 0.13 0.03 0.40 1.81
