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Mineral Classification Revisited: Use of Quasiternary Diagrams in the Visualization of Compositional Distribution of Inorganic Material in Coal Heikki J. Ollila, Jouni H. A. Daavitsainen, Laura H. Nuutinen, Minna S. Tiainen, Mika E. Virtanen, and Risto S. Laitinen* Department of Chemistry, UniVersity of Oulu, P.O. Box 3000, FIN-90014 Oulu, Finland ReceiVed July 31, 2005. ReVised Manuscript ReceiVed NoVember 15, 2005
A comparative study to determine the elemental composition of individual inorganic particles in the Pittsburgh No. 8 coal sample has been carried out with two different magnifications by SEM-EDS. The classification of particles into mineral classes left 30-40% of the particles unclassified. It was deduced that the sample contained the following minerals: calcite, kaolinite, pyrite, quartz, apatite, muscovite, and montmorillonite. The information of the compositional distribution of inorganic material in the coal sample is enhanced by use of the quasiternary diagrams. Minerals, such as apatite, calcite, pyrite, and quartz, can clearly be identified from the quasiternary diagram. A suitable elemental definition of the three corners in the quasiternary diagram enables the discussion of the compositional distribution and identity of the inorganic material that remains unclassified in the mineral classification. By combining the information from mineral classification and quasiternary diagrams, the composition of the inorganic material of the coal sample can be understood. This information can be used in the prediction of ash-related problems regardless of the fuel type.
Introduction The prediction and prevention of ash-related problems upon combustion of coal requires information of the compositional distribution and the identity of inorganic material that is comprised of a complex mixture of solid, liquid, and gaseous phases and consists mainly of organometallic complexes, exchangeable ions, dissolved salts, and discrete crystalline minerals.1-13 Depending on the nature of the material, the inorganic components in coal undergo several physical and * Corresponding author. Tel: +358 8 553 1611. Fax: +358 8 553 1608. E-mail:
[email protected]. (1) Wall, T. F.; Lowe, A.; Wibberley, L. J.; Stewart, I. M. Mineral matter in coal and the thermal performance of large boilers. Prog. Energy Combust. Sci. 1979, 5, 1-29. (2) Reid, W. T. The relation of mineral composition to slagging, fouling and erosion during and after combustion. Prog. Energy Combust. Sci. 1984, 10, 159-175. (3) Bryers, R. W. Fireside slagging, fouling, and high-temperature corrosion of heat-transfer surface due to impurities in steam-raising fuels. Prog. Energy Combust. Sci. 1996, 22, 20-120. (4) Benson, S. A. Comparison of Inorganic Constituents in Three LowRank Coals. Ind. Eng. Chem. Process Des. DeV. 1985, 24, 145-149. (5) Zygarlicke, C. J.; Steadman, E. N.; Benson, S. A. Studies of transformations of inorganic constituents in a Texas lignite during combustion. Prog. Energy Combust. Sci. 1990, 195-204. (6) Miller, S. F.; Schobert, H. H. Effect of mineral matter particle size on ash particle size distribution during pilot-scale combustion of pulverized coal and coal-waster slurry fuels. Energy Fuels 1993, 7, 532-541. (7) Benson, S. A.; Hurley, J. P.; Zagyrlicke, C. J.; Steadman, E. N.; Erickson, T. A. Predicting ash behavior in utility boilers. Energy Fuels 1993, 7, 746-754. (8) Patterson, J. H.; Corcoran, J. F.; Kinealy, K. M. Chemistry and mineralogy of carbonates in Australian bituminous and subbituminous coals. Fuel 1994, 73, 1735-1745. (9) Creelman, R. A.; Ward, C. R. A scanning electron microscope method for automated, quantitative analysis of mineral matter in coal. Int. J. Coal Geol. 1996, 30, 249-269. (10) Gupta, R. P.; Wall, T. F.; Kajigaya, I.; Miyamae, S.; Tsumita, Y. Computer-controlled scanning electron microscopy of minerals in coals implications for ash deposition. Prog. Energy Combust. Sci. 1998, 24, 523543.
chemical transformations during combustion and can cause problems such as emissions into the atmosphere or slagging, fouling, agglomeration of bed material, and corrosion.1-2,5-8,10-12,14-28 (11) McLennan, A. R.; Bryant, G. W.; Stanmore, B. R.; Wall, T. F. Ash Formation Mechanisms during pf Combustion in Reducing Conditions. Energy Fuels 2000, 14, 150-159. (12) McLennan, A. R.; Bryant, G. W.; Bailey, C. W.; Stanmore, B. R.; Wall, T. F. An Experimental Comparison of the Ash Formed from Coals Containing Pyrite and Siderite Mineral in Oxidizing and Reducing conditions. Energy Fuels 2000, 14, 308-315. (13) Vassilev, S. V.; Tasco´n, J. M. D. Methods for Characterization of Inorganic and Mineral Matter in Coal: A Critical Overview. Energy Fuels 2003, 17, 271-281. (14) Huffman, G. P.; Huggins, F. H.; Dunmyre, G. R. Investigation of the high-temperature behaviour of coal ash in reducing and oxidizing atmospheres. Fuel 1981, 60, 585-597. (15) Allen, R. M.; Mitchell, R. E. Mineral matter transformations during the combustion of a pulverized fuel in a laminar flow reactor. Int. Conf. Coal Sci. 1985, 401-404. (16) Quann, R. J.; Sarofim, A. F. A scanning electron microscopy study of the transformations of organically bound metals during lignite combustion. Fuel 1986, 65, 40-45. (17) Helble, J. J.; Srinivasachar, S.; Katz, C. B.; Boni, A. A. Mineral transformations in selected coalsssize and composition of the ash. Am. Chem. Soc., DiV. Fuel Chem. 1989, 34, 383-390. (18) Huffman, G. P.; Huggins, F. E.; Shah, N.; Shah, A. Behaviour of basic elements during coal combustion. Prog. Energy Combust. Sci. 1990, 16, 243-251. (19) Straszheim, W. E.; Marukuszewski, R. Automated image analysis of minerals and their association with organic components in bituminous coals. Energy Fuels 1990, 4, 748-754. (20) Hurley, J. P.; Schobert, H. H. Ash formation during pulverized subbituminous coal combustion. 1. Characterization of coals, and inorganic transformations during early stages of burnout. Energy Fuels 1992, 6, 4758. (21) Huffman, G. P.; Shah, A. N.; Zhao, J.; Huggins, F. E. Investigation of ash by microscopic and spectroscopic techniques. In The Impact of Ash Deposition on Coal Fired Plant; Engineering Foundation; Solihull: Birmingham, UK, 1993; 22. (22) Bool, L. E.; Peterson, T. W.; Wendt, J. O. L. The partitioning of iron during combustion of pulverized coal. Combust. Flame 1995, 100, 262270.
10.1021/ef050241y CCC: $33.50 © 2006 American Chemical Society Published on Web 01/04/2006
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Scanning electron microscopy combined with energydispersive X-ray analyzer (SEM-EDS) is a common tool in the evaluation of the chemical composition of the inorganic components in coal and ash.5-14,18,21,23,25-27,29-35 The SEM-EDS results have commonly been used to distribute the inorganic material into different mineral classes.5-7,10-12,14,18-19,26-27,29,32,34 The mineral classification, however, is not capable of identifying all inorganic material, since it does not occur only as mineral particles. The unclassified material may have an essential role in the combustion-related problems.1,7,10-12,18,23,27 Conventional ternary diagrams have been used to visualize the compositional distribution of inorganic material in coal and coal ash.7-8,10-11,14,18,21,26-27,30,32 They are also used to illustrate the thermal behavior of materials as a function of composition, that is, phase diagrams.11,14,27,30 Quasiternary diagrams are the logical extension of the ternary diagrams in which each corner is defined in terms of the sum of the contents of several elements. It is therefore possible to visualize the complete analytical data from a given sample.36-39 In this contribution, we present an enhancement to mineral classification. We explore the compositional distribution of a (23) Gentzis, T.; Goodarzi, F.; Hickinbotham, A. Mineralogical Composition of the Highvale Mine Coals and Its Impact on Plant Performance. Energy Sources 1995, 17, 681-702. (24) ten Brink, H. M.; Eenkhoorn, S.; Hamburg, G. A fundamental investigation of the flame kinetics of coal pyrite. Fuel 1996, 75, 945-951. (25) Wigley, F.; Williamson, J.; Gibb, W. H. The distribution of mineral matter in pulverised coal particles in relation to burnout behaviour. Fuel 1997, 76, 1283-1288. (26) Wigley, F.; Williamson, J. Modelling fly ash generation for pulverized coal combustion. Prog. Energy Combust. Sci. 1998, 24, 337343. (27) Bailey, C. W.; Bryant, G. W.; Matthews, E. M.; Wall, T. F. Investigation of the high-temperature behavior of excluded siderite grains during pulverized fuel combustion. Energy Fuels 1998, 12, 464-469. (28) Reifenstein, A. P.; Kahraman, H.; Coin, C. D. A.; Calos, N. J.; Miller, G.; Uwins, P. Behaviour of selected minerals in an improved ash fusion test: quartz, potassium feldspar, sodium feldspar, kaolinite, illite, calcite, dolomite, siderite, pyrite and apatite. Fuel 1999, 78, 1449-1461. (29) Huggins, F. E.; Kosmack, G. P.; Huffman, G. P.; Lee, R. J. Coal Mineralogies by SEM Automatic Image Analysis. Scanning Electron Microsc. 1980, 531-540. (30) Wang, H.; West, J.; Harb, J. N. Microanalytical characterization of slagging deposits from a pilot-scale combustor. Energy Fuels 1999, 13, 570-578. (31) Huggins, F. E. Overview of analytical methods for inorganic constituents in coal. Int. J. Coal Geol. 2002, 50, 169-214. (32) Yan, L.; Gupta, R. P.; Wall, T. F. A mathematical model of ash formation during pulverized coal combustion. Fuel 2002, 81, 337-344. (33) Goodarzi, F. Mineralogy, elemental composition and modes of occurrence of elements in Canadian feed-coals. Fuel 2002, 81, 1199-1213. (34) Zhang, L.; Sato, A.; Ninomiya, Y. CCSEM analysis of ash from combustion of coal added with limestone. Fuel 2002, 81, 1499-1508. (35) Casuccio, G. S.; Schlaegle, S. F.; Lersch, T. L.; Huffman, G. P.; Chen, Y.; Shah, N. Measurement of fine particulate matter using electron microscopy techniques. Fuel Process. Technol. 2004, 85, 763-779. (36) Virtanen, M.; Heikkinen, R.; Patrikainen, T.; Laitinen, R.; Skrifvars, B.-J.; Hupa, M. A novel application of CCSEM for studying agglomeration in fluidized bed combustion. In Impact of mineral impurities in solid fuel combustion; Kluwer Academic Plenum Publisher: New York, 1999; pp 147-154. (37) Virtanen, M. E.; Tiainen, M. S.; Laitinen, R. S. SEM-EDS and image analysis in the characterisation of coatings and adhesive material in the quartz-bed. In 5th European Conference on Industrial Furnaces and Boilers, Porto, Portugal, 2000; pp 117-126. (38) Virtanen, M.; Tiainen, M.; Nuutinen, L.; Pudas, M.; Laitinen, R. A tool for visualization and statistical analysis of SEM-EDS data. In Effects of coal quality on power plant performance: Ash problems, Management and Solutions; United Engineering Foundation: Park City, UT, 2001; pp 117-130. (39) Virtanen, M. E.; Tiainen, M. S.; Pudas, M.; Laitinen, R. S. Visualization and analysis of SEM-EDS data of quartz-bed agglomerates. In Progress in Thermochemical Biomass ConVersion; Blackwell Science: Cornwall, 2001; pp 671-677.
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coal sample that has been used in a collaborative study.40 We show that the elemental and the mineral compositions of the coal sample can be deduced directly from the quasiternary diagrams without a need for a separate mineral classification. However, by combining the data from the mineral classification and the quasiternary diagrams, additional information of the inorganic material of the coal sample can be achieved. This information can be used for the prediction of ash-related problems, regardless of the fuel type. Experimental Details Sample. In this work we considered the Pittsburgh No. 8 highvolatile bituminous coal sample that has previously been analyzed as part of a collaborative study reported by Galbreath et al.40 They have described the sample preparation of the cross-sectioned coalepoxy pellet in detail.40 SEM-EDS. The compositional distribution of the particles in the sample was determined by use of a JEOL JSM 6400 scanning electron microscope equipped with a Link ISIS EDS analyzer. The acceleration voltage was 15 keV, and the current was 12 × 10-8 A. The sample distance was 15 mm. The magnifications used for the SEM-EDS analysis were ×70 and ×300. About 1000 particles were analyzed for sodium, magnesium, aluminum, silicon, sulfur, phosphorus, potassium, calcium, titanium, and iron contents as appropriate. The image processing was performed with the IMQuant software incorporated in Link ISIS. The SEM-EDS results were visualized by use of quasiternary diagrams. Quasiternary Diagrams. The SEM-EDS results were visualized by use of quasiternary diagrams involving a locally designed software package.36-39 The quasiternary diagrams are a logical extension of the conventional ternary diagrams in which each corner has been defined in terms of a content of a single element. In a quasiternary diagram, each corner can be defined in terms of combined contents of several elements. A general point in the quasiternary diagram contains compositional information about all elements defined in the three corners. In this work, all analyzed elements are included in the three corners of the quasiternary diagram that therefore represents the whole elemental composition of the inorganic material in the coal sample. Let a corner be defined in terms of the sum of the contents of, for instance, silicon, phosphorus, and iron. The analytical results appear in this corner, if the combined content of silicon, phosphorus, and iron is 100%. The diagram provides no information about the relative amounts of the elements.At a general point in a quasiternary diagram the sum of the contents of all elements defined in the three corners is 100%. The column heights represent the relative contents of the inorganic material with the elemental composition as controlled by the definitions in the corners. Quasiternary diagrams have been used in several studies of different types of fuels.41-48 (40) Galbreath, K.; Zygarlicke, C.; Casuccio, G.; Moore, T.; Gottlieb, P.; Agron-Olshina, N.; Huffman, G.; Shah, A.; Yang, N.; Vleeskens, J.; Hamburg, G. Collaborative Study of Quantitative Coal Mineral Analysis Using Computer-Controlled Scanning Electron Microscopy. Fuel 1996, 75, 424-430. (41) Nuutinen, L.; Ollila, H.; Tiainen, M.; Virtanen, M.; Laitinen, R. An improved bed material for the BFB-boilers. Case 1: Co-combustion of sawdust and plywood waste. In 5th European Conference on Industrial Furnaces and Boilers, Porto, Portugal, 2000; pp 101-110. (42) Nuutinen, L.; Tiainen, M.; Virtanen, M.; Laitinen, R. An improved bed material for the BFB-boilers. Case 2: Combustion of fuel with high sodium content. In 5th European Conference on Industrial Furnaces and Boilers, Porto, Portugal, 2000, pp 111-116. (43) Nuutinen, L.; Ollila, H.; Tiainen, M.; Virtanen, M.; Laitinen, R. Role of quartz sand in the agglomeration during the FB-combustion using fuel with high sodium content. In Effects of coal quality on power plant performance: Ash Problems, Management, Solutions; United Engineering Foundation: Park City, UT, 2001; pp 53-60. (44) Nuutinen, L. H.; Tiainen, M. S.; Virtanen, M. E.; Enestam, S. H.; Laitinen, R. S. Coating Layers on Bed Particles during Biomass Fuel Combustion in Fluidized-Bed Boilers. Energy Fuels 2004, 18, 127-139.
Mineral Classification ReVisited
Energy & Fuels, Vol. 20, No. 2, 2006 593 Table 1. Criteria Used for Mineral Classification
Na [wt %]
mineral calcite kaolinite pyrite quartz apatite muscovite montmorillonite
0-10
Mg [wt %]
Al [wt %]
Si [wt %]
0-5
0-5 40-60
0-5 40-60
0-10
calcite kaolinite pyrite quartz apatite muscovite montmorillonite unclassified
S [wt %]
K [wt %]
0-10
Ca [wt %]
Ti [wt %]
80-100
0-5
Fe [wt %]
0-10
20-60
0-10
0-10
0-10
10-70
20-40 0-5
0-5
10-30 0-5
60-80 0-5 0-5
0-5
0-10 0-5
80-100 0-5 0-5
0-5
20-50 22-40
30-60 48-72
Table 2. Mineral Classification Results mineral
P [wt %]
×70 [wt %]
×300 [wt %]
Galbreath [wt %]
3.0 1.4 42.2 1.5
1.6 8.3 30.4 6.8 1.2 1.6 13.6 36.5
3.9 14.7 43.0 15.8
0.2 24.1 27.6
22.5
Results and Discussion Mineral Classification. On the basis of SEM-EDS determination, the inorganic particles in Pittsburgh No. 8 coal have been distributed into mineral classes according to the limits presented in Table 1. The compositional limits are modified from those by Huggins et al.29 We note, however, that the classification limits of the minerals are not unambiguous, and no generalized quidelines for suitable limits can be given. The mineral weight proportions are estimated assuming that the inorganic particle area is proportional to the particle volume.49,50 It is known, however, that the exposed section of the inorganic particle in the sample does not always represent the true volume of the inorganic particle. This approximation is suitable for this study, since our aim is not the determination of the total mineral matter in the sample. The differences between our classification and that of the collaborative study (laboratory A) made by Galbreath et al.40 are shown in Table 2. The collaborative study included only four minerals in the classification: calcite, kaolinite, pyrite, and quartz. In addition, we have also considered apatite, montmorillonite, and muscovite. Since Galbreath et al.40 did not provide information of the ranges of elemental compositions used in their mineral classification, a direct comparison of their classification with ours cannot be made. The contents of calcite and pyrite are comparable in both studies, but there is a significant difference in kaolinite and quartz contents. This is to be expected, since (45) Daavitsainen, J.; Nuutinen, L.; Ollila, H.; Tiainen, M.; Laitinen, R. FB Combustion of bark and sawdust in silica sand bed with dolomite addition. A case study. In 16th International Conference on Fluidized Bed Combustion, Reno, NV; ASME: Reno, NV, 2001. (46) Daavitsainen, J. H. A.; Laitinen, R. S.; Nuutinen, L. H.; Ollila, H. J.; Tiainen, M. S.; Virtanen, M. E. Effect of GR GRANULE used as bed material to reduce agglomeration in BFB combustion of biomass with high alkali metal content. In Progress in Thermochemical Biomass ConVersion; Blackwell Science: Cornwall, 2001; pp 705-712. (47) Daavitsainen, J. H. A.; Nuutinen, L. H.; Tiainen, M. S.; Laitinen, R. S. Agglomeration and the BFB Combustion of Biomass with High Alkali Metal Content. In Progress in Thermochemical Biomass ConVersion; Blackwell Science: Cornwall, 2001; pp 799-811. (48) Tiainen, M. S.; Daavitsainen, J. H. A.; Laitinen, R. S. The Role of Amorphous Material in Ash on the Agglomeration Problems in FB Boilers. A Powder XRD and SEM-EDS Study. Energy Fuels 2002, 16, 871-877. (49) Chayes, F. Modal AnalysissAn Elementary Statistical Appraisal; Wiley: New York, 1956; p 113. (50) DeHoff, R. T.; Rhines, F. N. QuantitatiVe Microscopy; McGrawHill: New York, 1968; p 422.
the elemental composition of muscovite and montmorillonite are relatively close to that of kaolinite (see Table 1). It should also be noted that the SEM-EDS analyses in the collaborative study 40 were carried out using a digitally stepped raster pattern. Mineral grains were automatically detected by an increase in the BSE signal above a preset operator-selected video threshold. The physical dimensions were determined of the identified mineral particles and the contents of the mineralforming elements were recorded as a point analysis at the center of the particle. By contrast, the compositions of the mineral particles in our BSE images were determined over the whole particle areas. Furthermore, our analyses involved only two different magnifications, while the collaborative study40 was carried out by considering 2-5 magnifications, depending on the laboratory. Quasiternary Diagrams. The corner definitions in the quasiternary diagrams are selected to facilitate the individual identification of different minerals. The locations of the appearance of different minerals in the quasiternary diagram are based on the compositional criteria given in Table 1 and are shown in Figure 1. For instance, the silicon content in quartz is defined as 80-100% in Table 1. Therefore, the mineral quartz appears in the quasiternary diagram as a triangle near the corner that has been defined in terms of Si + P + Fe (see Figure 1). Since, in general, the corner definitions involve the sum of the contents of several elements in each corner, the area representing different minerals in the diagram depends on the combined criteria for these elements and its estimation is a more complicated process. The definition of the region for pyrite is considered in detail in Figure 2. According to the criteria in Table 1, Na + Mg + S + K content spans a range 20-90%
Figure 1. The regions indicating the mineral classes (see Table 1) in the quasiternary diagram.
594 Energy & Fuels, Vol. 20, No. 2, 2006
Figure 2. The region of the pyrite class in the quasiternary diagram. Red lines indicate the minimum and maximum Na + Mg + S + K content, green lines the minimum and maximum S + P + Fe content, and blue lines minimum and maximum Al + Ca + Ti content.
(red lines in Figure 2), the Si + P + Fe content spans a range 10-80% (green lines), and the Al + Ca + Ti content spans a range 0-20% (blue lines). The combination of these ranges enclose a red area in Figure 2 within which the analytical results from pyrite are expected to appear. The areas for other minerals are estimated similarly.
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The quasiternary diagram containing analytical SEM-EDS results for the round-robin sample of Pittsburgh No.8 coal are presented in Figure 3. The comparison of the diagrams in Figure 3 with the area definitions for different minerals shown in Figure 1 indicates that the coal sample contains at least calcite, pyrite, and quartz. No apatite was detected in the sample, when the magnification was ×70 (Figure 3a), but some apatite can be observed with the magnification is ×300 (Figure 3b). The mineral classification shown in Table 2 is consistent with this finding. Kaolinite, muscovite, and montmorillonite lie too close to each in the quasiternary diagram for explicit identifications to be made. The particles that according to the mineral classification (Table 1) belong to the pyrite, apatite, calcite, and quartz classes are shown in Figure 4, parts a (×70) and b (×300). It can be seen that the majority of the particles fall in the appropriate regions in the quasiternary diagram. In some cases, however, the analytical results indicate that the particles lie outside that of the mineral class in which they formally belong. This is probably due to the presence of small amounts of elements the content of which are not considered in the mineral classification (see Table 1), but which appear in the quasiternary diagram. When the particles that formally belong to the abovementioned four mineral classes (see Figure 4) are removed from the quasiternary diagram of Figure 3, the remaining particles can be seen to belong to the kaolinite, muscovite, and mont-
Figure 3. The quasiternary diagrams of the Pittsburgh No. 8 coal sample from SEM-EDS analytical data. The magnifications are (a) ×70, and (b) ×300.
Figure 4. The quasiternary diagram of the Pittsburgh No. 8 coal sample based on the particles that fall in the following mineral classes: apatite (A, blue), calcite (C, yellow), pyrite (P, red), and quartz (Q, green). The magnifications are (a) ×70, and (b) ×300.
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Energy & Fuels, Vol. 20, No. 2, 2006 595
Figure 5. The quasiternary diagrams of the Pittsburgh No. 8 coal sample after the particles belonging to the mineral classes shown in Figure 4 have been removed. The particles fall in the region of the kaolinite, muscovite, and montmorillonite class, as well as that for the unclassified material. The magnifications are (a) ×70, and (b) ×300.
Figure 6. The quasiternary diagrams of the Pittsburgh No. 8 coal sample after the particles belonging to the mineral classes shown in Figure 4 have been removed and the corners have been redefined as Na + K, Al + Si, and Mg + P + S + Ca + Ti + Fe. The magnifications are (a) ×70, and (b) ×300.
morillonite classes (see Figure 5). In addition, there are several particles that do not belong to any mineral classes. We note that these three mineral classes as well as the unclassified particles are virtually superimposed in the quasiternary diagram. Therefore, it is not possible to distinguish these minerals from each other or from the unclassified material. Upon redefining the corners in terms Na + K, Al + Si, and Mg + P + S + Ca + Ti + Fe, it is possible to make further conclusions about these four superimposed classes. They lie near the Al + Si corner and are therefore probably aluminosilicates (see Figure 6 , parts a and b). The broader compositional distribution observed with the ×300 magnification shown in Figure 6b is probably a consequence of the individual compositional determination of small discrete particles that upon ×70 magnification cannot be resolved (see Figure 6a) and the analytical results of which show only the average values of their individual compositions. Conclusions The elemental composition and the area of individual inorganic particles were determined in the round-robin sample of Pittsburgh No. 8 high-volatile bituminous coal by a scanning electron microscope with two different magnifications. The SEM-EDS analytical results enabled the particles to be distributed into mineral classes. The main mineral classes were pyrite, apatite, calcite, quartz kaolinite, muscovite, and montmorillonite. In addition, 30-40% of the analyzed material remained unclas-
sified. However, it is important to characterize this portion of inorganic material, since it may have a major influence on the behavior of the ash. The quasiternary diagrams provide a convenient tool to enhance the mineral classification and to visualize SEM-EDS data. The quasiternary diagrams also indicated the presence of mineral classes calcite, apatite, pyrite, and quartz. The rest of the classified mineralsskaolinite, montmorillonite and muscoviteare all aluminosilicates with small differences in their chemical composition and therefore are not distinguishable. By changing the corner definition in the quasiternary diagram, it is possible to identify the unclassified material as a mixture of aluminosilicates. Quasiternary diagrams are independent from the mineral classification and therefore provide information of the whole chemical composition of the sample. By changing the corner definitions, it is possible to provide more detailed information about the inorganic material in coal samples than what is possible by using mineral classification. Acknowledgment. We are indebted to Dr. Steven A. Benson, Senior Research Manager, Energy & Environmental Research Center, Grand Forks, ND, for providing us the Pittsburgh No. 8 sample that was used in the earlier round-robin test. Financial support from Academy of Finland and Technological Development Agency, Finland, is gratefully acknowledged. EF050241Y
