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The use of automated coal petrography in determining maceral group composition and the reflectance of vitrinite
International Journal of Coal Geology, 9 (1988) 385-395 Elsevier Science Publishers B.V., Amsterdam - Printed in The Netherlands
385
The use of automated coal p e t r o g r a p h y in d e t e r m i n i n g maceral group composition and the reflectance of vitrinite JIN KUILI, XIA JIAN and HAO DUOHU Graduate school, China Institute of Mining and Technology, Beijing, China
(Received October 23, 1985; revised and accepted August 13, 1987)
ABSTRACT
Jin Kuili, Xia Jian and Hao Duohu, 1988.The use of automated coal petrography in determining coal maceral group composition and the reflectanceof vitrinite. Int. J. Coal Geol., 9: 385-395. Using photometric and image analysis methods, a reasonable technique and procedure have been developedfor the determination of maceral group composition and the reflectanceof vitrinite. This procedure is introduced by an optimum method for seeking the interval values of each maceral group, i.e., from the "fingerprint" reflectancehistogram, it seeks reasonable interval values and distribution values for vitrinite and other maceral groups. After checkingand correction, these values can be put into a program and computerized. This method had advantages such as increased speed of analysis and reproducibilityof the results.
INTRODUCTION Quantification of coal maceral groups and det erm i nat i on of the reflectance of vitrinite are considered as two key methods in coal petrography in China. Indeed, both these two par a m et er s must first be determined in theoretical studies of the genesis of coal or in the application of coal petrography to geological investigation, as well as in coal-processing and conversion techniques. In recent years, researchers have pursued the more convenient and highspeed au to matio n as a substitute for manual systems, in order to strive to make use of operators' experience in coal petrography. T h e authors are interested in this problem and took pa r t in the Working Group on A ut om at ed Coal Petrology during the 35th ICCP Meeting in Porto, Portugal, 1982. This paper is written on the basis of the report on the collaborative exercise of the Working Group and on some work on Chinese coals. T h e authors have noted the complexity of the variation in the physical properties of coal macerals (Kojima et al., 1979; P i t t and Dawson, 1979). We consider for example, t hat factors affecting reflectance, fluorescence intensity, etc.,
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387 are complex. In addition to coal rank, the source materials and the conditions for their transformation during sedimentation are very important. Even in one coal sample, an overlap of reflectance values of macerals and differences in the reflectance of the same maceral can be found. Moreover, in recent years, attention has been paid to the transition of macerals into each other, and many other complex cases may also be found. This is just the essential cause of the availability of operators' experience in existing automation either by microsscope photometry or by image analysis systems. After an investigation of the presently existing techniques for automated coal petrology (Harris et al., 1977; Davis and Vastola, 1977; Kojima et al., 1979; Golden and Hunn, 1980; Hampson et al., 1980; Davis et al., 1983 ), the authors suggest that a method available is that the varying values, called interval values, for vitrinite or other maceral groups on "fingerprint" reflectance histrograms could be examined, corrected, and put into a program to determine the desired data in question. This method is verified by the authors using microscope photometers and image-analysis systems. MICROSCOPE PHOTOMETRY FOR AUTOMATEDMEASUREMENT The devices used for the determination were Opton and Leitz MPV3 microscope photometers, both equipped with scanning stages, as well as PDP-11 and HP-85 computers (Davis and Vastola, 1977; Piller, 1977).
Automated measurement of vitrinite reflectance by microscopephotometry According to the above-mentioned procedure, we first construct a reflectance histogram by automatic point-count-lattice scanning on a whole polished pellet, i.e., the "fingerprint" is obtained. Then we find out the distributive interval values for vitrinite. We leave the peak, but take the uncorrected interval values; this is a more reasonable selection because, for one thing, there may be different kinds of vitrinites for measurement such as the total vitrinite or the wide monomaceralic bands (telocollinite of the ICCP), for another, a variation is present in reflectances even for the same maceral. Because of a great complexity in the variation of physical properties of coal macerals, the expected interval values cannot be processed by simple mathematical methods; therefore, the uncorrected interval values must be checked and corrected again manually, using several viewed grains within the pellet on the device. Then these corrected values are put into a program to determine the real results. The "fingerprint" is shown in Fig. 1 and the data obtained are listed in Table 2. The instructions for automated measurements from the Working Group of ICCP are also listed in Table 1 (ICCP, 1981/1982, 1982/1983). From the "fingerprint", we take account of rejected measurements falling on the boundary between vitrinite and the mounting medium or other macer-
388 TABLE 1 The instructions of the automated measurement from the Working Group on Automated Coal Petrologyof ICCP Single coals
Binary blends
1. Manual determined random reflectance on 100 points, R,,~..~ S.D.
Manual determined random reflectance on 500 points.
2. Automated determined random reflectance on at least 4000 points, Ro~r,n) S.D.
Automated determined random reflectance on at least 4000 points.
3. Manual V (vitrinite), E (exinite) and I (inertinite) composition.
Composition of blend: (a) as analyzed basis; (b) taking into acount the vitrinite content of each coal.
als, as in the cases pointed out by Pitt and Dawson ( 1979 ), and we obtain the uncorrected interval value of 0.55-1.25%. After correcting, we obtain 0.6-1.02% for total vitrinite, and 0.6-0.9% for telocollinite. The interval values of 0.60.9% will be taken as the input data for determining the reflectance.
Automated measurement in determining maceral group composition by microscope photometry We adopt the above-mentioned principle for quantification of three maceral groups. For selection of the interval values, it is necessary to reject part of the interval values (this differs from image analysis as described below) on account of the above-mentioned factor of the heterogeneity of macerals. As input data we take V (vitrinite)=0.6-1.02%, E ( e x i n i t e ) = 0 . 3 3 - 0 . 5 7 % , I (inertinite) = 1.4-3.2% (see Fig. 1 ); the obtained maceral group composition is listed in Table 2. The reflectance values of vitrinite and maceral group (V, I, E ) compositions obtained can be compared with results of measurements from an image-analysis system or from a manual routine operation on either device (see Table 2 ). Automated measurement on a photometer provides limited useful parameters, and its function is only within a certain range, that is to say, in optimal properties or maceral measurements (Piller, 1977). IIt is not suitable for determining porosity, grain size, fractions, etc. For this purpose, another device is needed - the image-analysis system, which is an advanced automated device. This paper emphasizes this aspect.
IMAGE-ANALYSIS SYSTEM FOR A U T O M A T E D M E A S U R E M E N T Automated measurement with an image-analysis system is far better than that using a microscope photometer, beause the former will be provided with
V.I.E.
Ro(random)
t
Imageanalysis system
Microscope photometer
analysis system
Image-
Microscope photometer
Data type
Maceral group composition of each coal in binary blend
Automated
Manual
Automated
Manual
Automated
Manual Automated Manual
V I E V I E
V I E V I E
S.D.
S.D.
92.00 rare 8.00 93.00 rare 7.00
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1.40
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0.72
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73.93 9.59 14.48
0.90
0.91
0.88 0.89
1.15
0.97
4
3
8
1
2
Binary blends
Singel coals
Vitrinite reflectance and maceral group composition data obtained by manual and automatic systems
TABLE 2
0.90
1.07
93.00 5.00 2.00 87.00 4.20 8.80
79.48 8.94 11.58
0.85
5
0.91
0.93
0.88
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83.20 13.00 3.80
6
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78.60 7.20 14.20
0.85
0.92
0.93
7
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390
Fig. 2. A comparison of undetected and detected images (a,b) on the monitor screen. The (bt photo shows a case of detected inertinite within a single coal, sample 2.
a digital image-processing function. Based upon the optical characters of coal grains, related forms, size and many other textural features, signals from samples, photos and polished or thin sections can be digitized; moreover, the system can be equipped with auto-focus control and a scanning stage for automation. Meanwhile, its operation is not confined in the way that the abovementioned point-count-lattice scanning is, and it can measure the desired coalmaceral fragments within each frame (Fig. 2). In our work, the image-analysis systems used are Leitz T.A.S. and Leitz T.A.S. Plus; both use Tasic as language on a PDP-11 computer (Vollath, 1979 ). The block diagram of the Leitz T.A.S. and T.A.S. Plus is shown in Fig. 3. The samples used are two single coals (Nos. 1-2) and five binary blends (Nos. 3-7) employed for the collaborative exercise of the Working Group on Automated Coal Petrology of ICCP. The instructions from the Working Group are listed in Table 1. The samples we used also include Chinese single coals (Nos. 8-9 ). A Leitz Orthoplan microscope and a stepping, motor-driven scanning stage are used, the object image is obtained under oil immersion, and reflectances are measured by means of a 549 nm filter (lack of 546 nm filter). A set of
Fig. 3. Block diagram of image-analysis sytem.
391 50-
o >
0.31 0.51
1.0
1,23 Retlectonce
Fig. 4. Diagram showing the relationship between grey values and reflectances.
American glass standards has reflectances of 0.30 %, 0.51%, 0.94 %, 1.02 %, 1.38 % and 1.67% in oil, respectively, and a standard of Leitz 1.23%. Pellets used are 2 cm in diameter, the mounting medium is shellac. The oil-immersion objective is 32 X. As we have mentioned, the problem of the automated determination of reflectances on vitrinite and quantification of coal-maceral groups, deducting the influence of high mineral content, especially of pyrite, can be solved directly only by using the grey-reflectance relationship (Harris et al., 1977; Zeiss, 1979; Oosthuyzen, 1980; Voort and Golden, 1981 ). Conversely, if other parameters and features, such as forms, are considered, then a certain mathematical model must be suggested (England et al., 1979). This is impossible for the abovementioned problem. Therefore, a relationship between grey values (100 levels in Leitz T.A.S. ) and the random reflectance of the above-mentioned standards is established by TRA (transmittance; it may be transformed into reflectance) instructions, which can transform light density into different electrical levels through a built-in densitometer, and it is not necessary to correct the readings of the standards because they are isotropic. This proved the linearity to be good, and then no more calibration is applied except only to check with a standard of 1.23% Ro later in each experiment. This relationship is plotted in Fig. 4. For automated measurements, we use the "fingerprint" also, and adopt a procedure in a similar way to that of microscope photometry. Results of the measurements are listed in Table 2.
Manual and automated random reflectance measurements by Leitz T.A.S. The manual or automated random reflectance measurement is made in accordance with the instructions and uses telocollinite as mentioned above. With manual random reflectance measurements, the "fingerprint" is not necessary. 500 point readings (ICCP, 1981/1982, 1982/1983) are made directly by means ofa lightpen - an apparatus for man-machine communication. In automated random reflectance measurement, we can also work with the aid of the "fingerprint" reflectance histogram, otained from scanning images of 100 frames for a pellet. The area of each frame of 32594 ltm 2 (0.03 m m 2) is
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393 representative. Thus, it represents the distribution of grey-reflectance of all the various components, including that of the mounting medium. For a single-coal "fingerprint", selection of uncorrected interval values of vitrinite distribution is easier than in the case of a binary blend, in which the reflectance of different ran]~s of coals may overlap. Nevertheless, they may be checked and corrected again manually with several viewed grains of a pellet on the device; as described above, the real interval values of vitrinite or others are not suitable for processing with the mathematical method. As is seen in Fig. 5a, single coal No. 1 shows the uncorrected interval values of V (vitrinite) = 0.76-1.53%, E (exinite) = 0.29-0.73% and I (inertinite) = 1.53-3.23%. After preferred checking, we get the real data V = 0.97-1.49%, E = 0.29-0.76%, I--1.53-3.23% and 1.10-1.39% for telocollinite; the latter is used as a parameter for automation. The binary blend in Fig. 5b, corrected after examination, shows V = 0.69-1.26%, E = 0.33-0.66%, I = 1.29-3.29% as total distribution of maceral groups, and V1=0.69-0.99%, E1=0.33-0.46%, I1=1.29-1.79%, V2 = 1.03-1.26%, E2 = 0.49-0.66%, I2 = 1.82-3.29% as distributions of maceral groups for two different rank coals, respectively. Since it is a binary blend, V = 0.69-1.26%, it must not be examined by an optimum seeking method, and should be used only as input data. In either case, single coal or binary blend, the boundaries between maceral groups are more reasonable than with microscope photometry, though they contain assumed factors. These effects, such as the mounting medium and rejected measurements falling on boundaries between macerals, may be removed, since in image analysis the determination is made on coal grains. This is the main difference between image analysis and miroscope photometry. We also paid attention to studying the number of measuring points. The amount of 4000 points required is too large either for single coals or for a blend. A considerable number of points is determined repeatedly. The accuracy at 4000 points is no better than that at 2000 points. For example, pellet No. 1 at 4000 points, shows that Ro is 1.268%, S.D. is 0.061, but at 2000 points, they are 1.270% and 0.058% respectively. We suggest that the suitale number of points without sacrificing S.D. is 2000.
Automated determination of maceral group composition by Leitz T.A.S. The quantification of V.I.E. groups in single coals and blends is obtained by getting the interval values with the above-mentioned method and putting them into the program to computerize them. The results are listed in Table 2. Two samples, No. 5 and No. 6, were analyzed by this procedure, but these results are not ideal, because there is something wrong with the classification o f the transitional macerals.
394 SOME REMARKS ON THE AUTOMATED MEASUREMENT WORK
Automated measurement of reflectance or maceral group composition on the microscope photometer is an available method, and searching new ways of automation in other fields may also be necessary and cannot be neglected, though some researchers have their reservations. It is possible that many new methods such as fluorescence intensity or transmittance measurements could be carried out by automation following developments in the techniques. For the automated determination of reflectance, the microscope photometer gives readings more rapidly than an image-analysis system, but the accuracy of the obtained data is lower. With regard to image analysis, it is the more advantageous method, but it requires a higher quality of sample preparation, so it must be considered in combination with a method for the enhancement of image contrast. Many useful data could be developed by automated procedures. For example, automating microlithotype analysis is a more difficult task, but through automated measurements on different macerals in turn, it is also possible to determine the various kinds of microlithotypes. For automation, an in-line procedure of a microscope photometer combined with an image-analysis system must also be considered, such as using the former for reflectance or other optical parameter study, and the latter for macerals and those features including optical parameter studies. CONCLUSION
In this paper, we discuss two kinds of methods, one is microscope photometry and the other is the image-analysis method. Both use the scanning stage for scanning on a whole polished pellet. The "fingerprint" is used in selecting the optimum interval values for vitrinite and other maceral groups. For microscope photometry, it is necessary to reject part of the interval values on account of rejected measurements falling on the boundary between macerals and the mounting medium or other macerals, whereas in image analysis the rejected measurements may be removed, since the determination is made on coal grains. After checking and correction, the real interval values can be put into a program and computerized, and the final results for reflectance or maceral-group composition can be obtained. From our point of view, 2000 points would be suitable for such measurements. ACKNOWLEDGEMENT
We are grateful to Huang Yunqing, Zhao Jinan and Chen Ting for helping us with programming in the measurements.
395 REFERENCES Chao, E.C.T., Minkin, J.A. and Thompson, C.L., 1982. Application of automated image analysis to coal petrology. Int. J. Coal Geol., 2: 113-150. Davis, A. and Vastola, F.J., 1977. Development in automated microscopy of coal. J. Microsc., 109: 3-12. Davis, A., Kuehn, K.W., Maylotte, D.E. and Peters, R.L.St., 1983. Mapping of polished coal surfaces by automated reflectance microscopy. J. Microsc., 132 (3): 297-302. England, B.K., Mikka, K.A. and Bagnall, E.J., 1979. Petrographic characterization of coal using automatic image analysis. J. Microsc., 116 (3): 329-336. Golden, J.F. and Hunn, W., 1980. Microstructural analysis by new image analysis techniques. Microsc. Sci., 8: 305-309. Hampson, A.J., Becken, W.H., Schat, R.R. and Whyte, P.A., 1980. Coke quality control by online coal quality monitoring at DOFASCO. 39th A.I.M.E. Iron-making Conference. Washington, D.C. Harris, L.A., Rose, T., Derose, L. and Greene, J., 1977. Quantitative analysis of pyrite in coal by optical image techniques. Econ. Geol., 72: 695-697. ICCP. The collaborative exercise 1981/1982 and 1982/1983 of the Working Group on Automated Coal Petrology. Kojima, K., 1976. Automatic system for evaluating coking coals. Iron Steel Int., 49: 435-436. Kojima, K., Sugai, T. and Hara, H., 1979. Automatic system for evaluating coking coal and its application in Nippon Steel Corporation (abstr.). Ninth Int. Congr. Carboniferous Stratigraphy and Geology, Urbana, III., Abstr., p. 109. Oosthuyzen, E.J., 1980. An elementary introduction to image analysis - A new field of interest at the National Institute for Metallurgy, National Institute for Metallurgy. South Africa. Piller, H., 1977. Microscope Photometry. Springer-Verlag, Berlin. Heidelberg, 253 pp. Pitt, G.J. and Dawson, K.M., 1979. Some considerations involved in the automation of reflectance measurements on coal. J. Microsc., 116(3): 321-328. Vollath, D., 1979. Use of the TAS with process computer. Leitz Sci. Tech. Inf., VII(5): 129-135. Voort, G.F.V. and Golden, J.F., 1981. Automation: the JK inclusion analysis. 14th Annual convention of the Int. Metallo. Soc., San Francisco. Zeiss, H.S., 1979. Automating coal petrology at Bethlehem Steel. Ninth Int. Congr. Carboniferous Stratigraphy and Geology, Urbana, III, Abstr., p. 242.
385
The use of automated coal p e t r o g r a p h y in d e t e r m i n i n g maceral group composition and the reflectance of vitrinite JIN KUILI, XIA JIAN and HAO DUOHU Graduate school, China Institute of Mining and Technology, Beijing, China
(Received October 23, 1985; revised and accepted August 13, 1987)
ABSTRACT
Jin Kuili, Xia Jian and Hao Duohu, 1988.The use of automated coal petrography in determining coal maceral group composition and the reflectanceof vitrinite. Int. J. Coal Geol., 9: 385-395. Using photometric and image analysis methods, a reasonable technique and procedure have been developedfor the determination of maceral group composition and the reflectanceof vitrinite. This procedure is introduced by an optimum method for seeking the interval values of each maceral group, i.e., from the "fingerprint" reflectancehistogram, it seeks reasonable interval values and distribution values for vitrinite and other maceral groups. After checkingand correction, these values can be put into a program and computerized. This method had advantages such as increased speed of analysis and reproducibilityof the results.
INTRODUCTION Quantification of coal maceral groups and det erm i nat i on of the reflectance of vitrinite are considered as two key methods in coal petrography in China. Indeed, both these two par a m et er s must first be determined in theoretical studies of the genesis of coal or in the application of coal petrography to geological investigation, as well as in coal-processing and conversion techniques. In recent years, researchers have pursued the more convenient and highspeed au to matio n as a substitute for manual systems, in order to strive to make use of operators' experience in coal petrography. T h e authors are interested in this problem and took pa r t in the Working Group on A ut om at ed Coal Petrology during the 35th ICCP Meeting in Porto, Portugal, 1982. This paper is written on the basis of the report on the collaborative exercise of the Working Group and on some work on Chinese coals. T h e authors have noted the complexity of the variation in the physical properties of coal macerals (Kojima et al., 1979; P i t t and Dawson, 1979). We consider for example, t hat factors affecting reflectance, fluorescence intensity, etc.,
0166-5162/88/$03.50
© 1988 Elsevier Science Publishers B.V.
~3 294
o
C
E
.= (m
b0
0~
E
,~,~;~ ~:=~,m~ , l ~ , ~ , ~ , ~ . , ~ , ~ , ~ , , ~ , ~ = , ~ , , i ¢ ~ * v ~
~
~
~,~
~
~~
• ~=~ ~,*,
0. 005 0.03 0. 055 0.08 0. 105 0.13 0. 155 0.18 0. 205 0.23 0. 255 0.28 0. 305 0.33 0. 355 0.48 0. 505 0.53 0. 555 0.58 0, 605 0.63 0. 655 0.68 0. 705 0.73 0. 755 0 . 78 0. 805 0.83 0 . 855 0.88 0. 905 0.93 0. 955 0.98 1. 0 0 5 I . 03 1. 055 1.08 1. 1 0 5 1.13 I . 155 1.18 I. 2 0 5 1.23 I • 255
1.28 1. 305 1.33
1. 355 I . 38 1. 405 1.~3 I. 4 5 5
29
*~:***
II
*~: *'~: *~ **:
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1. 505 1.53 1. 555 1.58 I. 6 0 5 1.63
1. 655 1.68 1. 7 0 5
1.73 I. 755 I . 78 I . 805
12 1• 10
1.83 i. 855
c)
(.
387 are complex. In addition to coal rank, the source materials and the conditions for their transformation during sedimentation are very important. Even in one coal sample, an overlap of reflectance values of macerals and differences in the reflectance of the same maceral can be found. Moreover, in recent years, attention has been paid to the transition of macerals into each other, and many other complex cases may also be found. This is just the essential cause of the availability of operators' experience in existing automation either by microsscope photometry or by image analysis systems. After an investigation of the presently existing techniques for automated coal petrology (Harris et al., 1977; Davis and Vastola, 1977; Kojima et al., 1979; Golden and Hunn, 1980; Hampson et al., 1980; Davis et al., 1983 ), the authors suggest that a method available is that the varying values, called interval values, for vitrinite or other maceral groups on "fingerprint" reflectance histrograms could be examined, corrected, and put into a program to determine the desired data in question. This method is verified by the authors using microscope photometers and image-analysis systems. MICROSCOPE PHOTOMETRY FOR AUTOMATEDMEASUREMENT The devices used for the determination were Opton and Leitz MPV3 microscope photometers, both equipped with scanning stages, as well as PDP-11 and HP-85 computers (Davis and Vastola, 1977; Piller, 1977).
Automated measurement of vitrinite reflectance by microscopephotometry According to the above-mentioned procedure, we first construct a reflectance histogram by automatic point-count-lattice scanning on a whole polished pellet, i.e., the "fingerprint" is obtained. Then we find out the distributive interval values for vitrinite. We leave the peak, but take the uncorrected interval values; this is a more reasonable selection because, for one thing, there may be different kinds of vitrinites for measurement such as the total vitrinite or the wide monomaceralic bands (telocollinite of the ICCP), for another, a variation is present in reflectances even for the same maceral. Because of a great complexity in the variation of physical properties of coal macerals, the expected interval values cannot be processed by simple mathematical methods; therefore, the uncorrected interval values must be checked and corrected again manually, using several viewed grains within the pellet on the device. Then these corrected values are put into a program to determine the real results. The "fingerprint" is shown in Fig. 1 and the data obtained are listed in Table 2. The instructions for automated measurements from the Working Group of ICCP are also listed in Table 1 (ICCP, 1981/1982, 1982/1983). From the "fingerprint", we take account of rejected measurements falling on the boundary between vitrinite and the mounting medium or other macer-
388 TABLE 1 The instructions of the automated measurement from the Working Group on Automated Coal Petrologyof ICCP Single coals
Binary blends
1. Manual determined random reflectance on 100 points, R,,~..~ S.D.
Manual determined random reflectance on 500 points.
2. Automated determined random reflectance on at least 4000 points, Ro~r,n) S.D.
Automated determined random reflectance on at least 4000 points.
3. Manual V (vitrinite), E (exinite) and I (inertinite) composition.
Composition of blend: (a) as analyzed basis; (b) taking into acount the vitrinite content of each coal.
als, as in the cases pointed out by Pitt and Dawson ( 1979 ), and we obtain the uncorrected interval value of 0.55-1.25%. After correcting, we obtain 0.6-1.02% for total vitrinite, and 0.6-0.9% for telocollinite. The interval values of 0.60.9% will be taken as the input data for determining the reflectance.
Automated measurement in determining maceral group composition by microscope photometry We adopt the above-mentioned principle for quantification of three maceral groups. For selection of the interval values, it is necessary to reject part of the interval values (this differs from image analysis as described below) on account of the above-mentioned factor of the heterogeneity of macerals. As input data we take V (vitrinite)=0.6-1.02%, E ( e x i n i t e ) = 0 . 3 3 - 0 . 5 7 % , I (inertinite) = 1.4-3.2% (see Fig. 1 ); the obtained maceral group composition is listed in Table 2. The reflectance values of vitrinite and maceral group (V, I, E ) compositions obtained can be compared with results of measurements from an image-analysis system or from a manual routine operation on either device (see Table 2 ). Automated measurement on a photometer provides limited useful parameters, and its function is only within a certain range, that is to say, in optimal properties or maceral measurements (Piller, 1977). IIt is not suitable for determining porosity, grain size, fractions, etc. For this purpose, another device is needed - the image-analysis system, which is an advanced automated device. This paper emphasizes this aspect.
IMAGE-ANALYSIS SYSTEM FOR A U T O M A T E D M E A S U R E M E N T Automated measurement with an image-analysis system is far better than that using a microscope photometer, beause the former will be provided with
V.I.E.
Ro(random)
t
Imageanalysis system
Microscope photometer
analysis system
Image-
Microscope photometer
Data type
Maceral group composition of each coal in binary blend
Automated
Manual
Automated
Manual
Automated
Manual Automated Manual
V I E V I E
V I E V I E
S.D.
S.D.
92.00 rare 8.00 93.00 rare 7.00
82.37 1.70 15.93
1.28 0,084 1.27 0,061
1.40
90.00 6.00 4.00 85.00 11.00 6.00
85.72 8.57 5.71 81.00 11.00 8.00
0.83 0.80 0.76 0,120 0.89 0.73 1,075
1.15
0,785
0.72
9
Vl
E2
v~
I1
E1
89.00 3.80 7.20
88,55 2.49 8,96
90.00 1.70 8.30
73.93 9.59 14.48
0.90
0.91
0.88 0.89
1.15
0.97
4
3
8
1
2
Binary blends
Singel coals
Vitrinite reflectance and maceral group composition data obtained by manual and automatic systems
TABLE 2
0.90
1.07
93.00 5.00 2.00 87.00 4.20 8.80
79.48 8.94 11.58
0.85
5
0.91
0.93
0.88
83.00 11.00 6.00 75.00 17.00 8.00 58.00 6.00 36.00 27.00 1.00 72.00
83.20 13.00 3.80
6
89.00 4.00 7.00 94.00 4.00 2.00 82.00 8.00 10.00
78.60 7.20 14.20
0.85
0.92
0.93
7
Oo ¢D
390
Fig. 2. A comparison of undetected and detected images (a,b) on the monitor screen. The (bt photo shows a case of detected inertinite within a single coal, sample 2.
a digital image-processing function. Based upon the optical characters of coal grains, related forms, size and many other textural features, signals from samples, photos and polished or thin sections can be digitized; moreover, the system can be equipped with auto-focus control and a scanning stage for automation. Meanwhile, its operation is not confined in the way that the abovementioned point-count-lattice scanning is, and it can measure the desired coalmaceral fragments within each frame (Fig. 2). In our work, the image-analysis systems used are Leitz T.A.S. and Leitz T.A.S. Plus; both use Tasic as language on a PDP-11 computer (Vollath, 1979 ). The block diagram of the Leitz T.A.S. and T.A.S. Plus is shown in Fig. 3. The samples used are two single coals (Nos. 1-2) and five binary blends (Nos. 3-7) employed for the collaborative exercise of the Working Group on Automated Coal Petrology of ICCP. The instructions from the Working Group are listed in Table 1. The samples we used also include Chinese single coals (Nos. 8-9 ). A Leitz Orthoplan microscope and a stepping, motor-driven scanning stage are used, the object image is obtained under oil immersion, and reflectances are measured by means of a 549 nm filter (lack of 546 nm filter). A set of
Fig. 3. Block diagram of image-analysis sytem.
391 50-
o >
0.31 0.51
1.0
1,23 Retlectonce
Fig. 4. Diagram showing the relationship between grey values and reflectances.
American glass standards has reflectances of 0.30 %, 0.51%, 0.94 %, 1.02 %, 1.38 % and 1.67% in oil, respectively, and a standard of Leitz 1.23%. Pellets used are 2 cm in diameter, the mounting medium is shellac. The oil-immersion objective is 32 X. As we have mentioned, the problem of the automated determination of reflectances on vitrinite and quantification of coal-maceral groups, deducting the influence of high mineral content, especially of pyrite, can be solved directly only by using the grey-reflectance relationship (Harris et al., 1977; Zeiss, 1979; Oosthuyzen, 1980; Voort and Golden, 1981 ). Conversely, if other parameters and features, such as forms, are considered, then a certain mathematical model must be suggested (England et al., 1979). This is impossible for the abovementioned problem. Therefore, a relationship between grey values (100 levels in Leitz T.A.S. ) and the random reflectance of the above-mentioned standards is established by TRA (transmittance; it may be transformed into reflectance) instructions, which can transform light density into different electrical levels through a built-in densitometer, and it is not necessary to correct the readings of the standards because they are isotropic. This proved the linearity to be good, and then no more calibration is applied except only to check with a standard of 1.23% Ro later in each experiment. This relationship is plotted in Fig. 4. For automated measurements, we use the "fingerprint" also, and adopt a procedure in a similar way to that of microscope photometry. Results of the measurements are listed in Table 2.
Manual and automated random reflectance measurements by Leitz T.A.S. The manual or automated random reflectance measurement is made in accordance with the instructions and uses telocollinite as mentioned above. With manual random reflectance measurements, the "fingerprint" is not necessary. 500 point readings (ICCP, 1981/1982, 1982/1983) are made directly by means ofa lightpen - an apparatus for man-machine communication. In automated random reflectance measurement, we can also work with the aid of the "fingerprint" reflectance histogram, otained from scanning images of 100 frames for a pellet. The area of each frame of 32594 ltm 2 (0.03 m m 2) is
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393 representative. Thus, it represents the distribution of grey-reflectance of all the various components, including that of the mounting medium. For a single-coal "fingerprint", selection of uncorrected interval values of vitrinite distribution is easier than in the case of a binary blend, in which the reflectance of different ran]~s of coals may overlap. Nevertheless, they may be checked and corrected again manually with several viewed grains of a pellet on the device; as described above, the real interval values of vitrinite or others are not suitable for processing with the mathematical method. As is seen in Fig. 5a, single coal No. 1 shows the uncorrected interval values of V (vitrinite) = 0.76-1.53%, E (exinite) = 0.29-0.73% and I (inertinite) = 1.53-3.23%. After preferred checking, we get the real data V = 0.97-1.49%, E = 0.29-0.76%, I--1.53-3.23% and 1.10-1.39% for telocollinite; the latter is used as a parameter for automation. The binary blend in Fig. 5b, corrected after examination, shows V = 0.69-1.26%, E = 0.33-0.66%, I = 1.29-3.29% as total distribution of maceral groups, and V1=0.69-0.99%, E1=0.33-0.46%, I1=1.29-1.79%, V2 = 1.03-1.26%, E2 = 0.49-0.66%, I2 = 1.82-3.29% as distributions of maceral groups for two different rank coals, respectively. Since it is a binary blend, V = 0.69-1.26%, it must not be examined by an optimum seeking method, and should be used only as input data. In either case, single coal or binary blend, the boundaries between maceral groups are more reasonable than with microscope photometry, though they contain assumed factors. These effects, such as the mounting medium and rejected measurements falling on boundaries between macerals, may be removed, since in image analysis the determination is made on coal grains. This is the main difference between image analysis and miroscope photometry. We also paid attention to studying the number of measuring points. The amount of 4000 points required is too large either for single coals or for a blend. A considerable number of points is determined repeatedly. The accuracy at 4000 points is no better than that at 2000 points. For example, pellet No. 1 at 4000 points, shows that Ro is 1.268%, S.D. is 0.061, but at 2000 points, they are 1.270% and 0.058% respectively. We suggest that the suitale number of points without sacrificing S.D. is 2000.
Automated determination of maceral group composition by Leitz T.A.S. The quantification of V.I.E. groups in single coals and blends is obtained by getting the interval values with the above-mentioned method and putting them into the program to computerize them. The results are listed in Table 2. Two samples, No. 5 and No. 6, were analyzed by this procedure, but these results are not ideal, because there is something wrong with the classification o f the transitional macerals.
394 SOME REMARKS ON THE AUTOMATED MEASUREMENT WORK
Automated measurement of reflectance or maceral group composition on the microscope photometer is an available method, and searching new ways of automation in other fields may also be necessary and cannot be neglected, though some researchers have their reservations. It is possible that many new methods such as fluorescence intensity or transmittance measurements could be carried out by automation following developments in the techniques. For the automated determination of reflectance, the microscope photometer gives readings more rapidly than an image-analysis system, but the accuracy of the obtained data is lower. With regard to image analysis, it is the more advantageous method, but it requires a higher quality of sample preparation, so it must be considered in combination with a method for the enhancement of image contrast. Many useful data could be developed by automated procedures. For example, automating microlithotype analysis is a more difficult task, but through automated measurements on different macerals in turn, it is also possible to determine the various kinds of microlithotypes. For automation, an in-line procedure of a microscope photometer combined with an image-analysis system must also be considered, such as using the former for reflectance or other optical parameter study, and the latter for macerals and those features including optical parameter studies. CONCLUSION
In this paper, we discuss two kinds of methods, one is microscope photometry and the other is the image-analysis method. Both use the scanning stage for scanning on a whole polished pellet. The "fingerprint" is used in selecting the optimum interval values for vitrinite and other maceral groups. For microscope photometry, it is necessary to reject part of the interval values on account of rejected measurements falling on the boundary between macerals and the mounting medium or other macerals, whereas in image analysis the rejected measurements may be removed, since the determination is made on coal grains. After checking and correction, the real interval values can be put into a program and computerized, and the final results for reflectance or maceral-group composition can be obtained. From our point of view, 2000 points would be suitable for such measurements. ACKNOWLEDGEMENT
We are grateful to Huang Yunqing, Zhao Jinan and Chen Ting for helping us with programming in the measurements.
395 REFERENCES Chao, E.C.T., Minkin, J.A. and Thompson, C.L., 1982. Application of automated image analysis to coal petrology. Int. J. Coal Geol., 2: 113-150. Davis, A. and Vastola, F.J., 1977. Development in automated microscopy of coal. J. Microsc., 109: 3-12. Davis, A., Kuehn, K.W., Maylotte, D.E. and Peters, R.L.St., 1983. Mapping of polished coal surfaces by automated reflectance microscopy. J. Microsc., 132 (3): 297-302. England, B.K., Mikka, K.A. and Bagnall, E.J., 1979. Petrographic characterization of coal using automatic image analysis. J. Microsc., 116 (3): 329-336. Golden, J.F. and Hunn, W., 1980. Microstructural analysis by new image analysis techniques. Microsc. Sci., 8: 305-309. Hampson, A.J., Becken, W.H., Schat, R.R. and Whyte, P.A., 1980. Coke quality control by online coal quality monitoring at DOFASCO. 39th A.I.M.E. Iron-making Conference. Washington, D.C. Harris, L.A., Rose, T., Derose, L. and Greene, J., 1977. Quantitative analysis of pyrite in coal by optical image techniques. Econ. Geol., 72: 695-697. ICCP. The collaborative exercise 1981/1982 and 1982/1983 of the Working Group on Automated Coal Petrology. Kojima, K., 1976. Automatic system for evaluating coking coals. Iron Steel Int., 49: 435-436. Kojima, K., Sugai, T. and Hara, H., 1979. Automatic system for evaluating coking coal and its application in Nippon Steel Corporation (abstr.). Ninth Int. Congr. Carboniferous Stratigraphy and Geology, Urbana, III., Abstr., p. 109. Oosthuyzen, E.J., 1980. An elementary introduction to image analysis - A new field of interest at the National Institute for Metallurgy, National Institute for Metallurgy. South Africa. Piller, H., 1977. Microscope Photometry. Springer-Verlag, Berlin. Heidelberg, 253 pp. Pitt, G.J. and Dawson, K.M., 1979. Some considerations involved in the automation of reflectance measurements on coal. J. Microsc., 116(3): 321-328. Vollath, D., 1979. Use of the TAS with process computer. Leitz Sci. Tech. Inf., VII(5): 129-135. Voort, G.F.V. and Golden, J.F., 1981. Automation: the JK inclusion analysis. 14th Annual convention of the Int. Metallo. Soc., San Francisco. Zeiss, H.S., 1979. Automating coal petrology at Bethlehem Steel. Ninth Int. Congr. Carboniferous Stratigraphy and Geology, Urbana, III, Abstr., p. 242.