Automated Microlithotype Analysis on Particulate ... - ACS Publications

1198

Energy & Fuels 2003, 17, 1198-1209

Automated Microlithotype Analysis on Particulate Coal E. Lester,* D. Watts, M. Cloke, and D. Clift School of Chemical, Environmental and Mining Engineering, University of Nottingham, Nottingham NG7 2RD, United Kingdom Received October 15, 2002. Revised Manuscript Received May 13, 2003

This paper describes an image analysis technique that allows petrographic analysis of coal to be performed on particulate coal. The technique is able to separate coal particles from the background resin and then gather information about the petrographic composition of the particle, as well as morphological information. This process requires two separate images of the surface captured at different camera exposure times. At the lower exposure, the liptinite has a grayscale value that is similar to that of the surrounding resin; however, at a higher exposure time, the resin remains black whereas the liptinite grayscale increases significantly enough to distinguish it from the resin. After the coal particles have been separated from the background resin, image processing is used to separate touching particles. The histogram and morphological data for each particle are stored individually in a data file. Detailed information about the petrographic composition of the sample can be collected, including maceral and microlithotype analysis.

Introduction Maceral analysis is useful when characterizing coals for use as a fuel. For example, the association between maceral composition and combustion has been well described in the literature.1-3 Understanding how each maceral type can influence burnout is important when assessing a coal’s potential for power generation. For this reason, many power generators find petrographic characterization to be a useful tool. Maceral analysis is normally conducted using manual techniques. As a technique, the criteria for acceptable errors are wellknown and the agreement between the results of different workers, certainly from within the same laboratory, is generally quite reasonable. However, limitations do exist over the number of samples that can be sensibly analyzed in a day, because the work is relatively time-consuming. In utilization fields, such as combustion, microlithotype analysis can be more useful than maceral analysis, because this classification system identifies not just the maceral composition but also the associations of the different maceral groups within each particle. The results give more information, but microlithotype analysis is even more time-consuming and difficult to perform, because such an analysis involves identification of all the macerals that lie beneath a 20-point Ko¨tter graticule. Particles can be sorted into microlithotype classes without using the 50 µm × 50 µm Ko¨tter graticule, based on manual estimation of the amount of each maceral in each particle.4 * Author to whom correspondence should be addressed. E-mail: [email protected]. (1) Lee, G. K.; Whaley, H. J. Inst. Energy 1983, 56, 190. (2) Cloke, M.; Lester, E. Fuel 1994, 73, 315-321. (3) Shibaoka, M.; Thomas, C. G.; Gawronski, E.; Young, B. C. Proc. Int. Conf. Coal Sci. 1989, 1123. (4) Bailey, J. G.; O’Brien, G.; Esterle, J.; Sexty, G.; Beath, H.; Chalmers, G. Advances in the Study of the Sydney Basin, Proceedings of the 30th Newcastle Symposium; The University of Newcastle: Newcastle, New South Wales, Australia, 1996; p 135.

Figure 1. Coal block made with red resin, showing blackened particle boundaries.

This estimation system is reasonably accurate, because it relies on making basic decisions such as (i) does a particle contain more than 5% of each maceral?, (ii) is the vitrinite content in a particle greater than the inertinite content?, and (iii) does the particle contain more than 95% of any one maceral? Although automated analysis is one possible alternative to manual analysis, it is not without its difficulties. These have been discussed in a recent paper.5 Color image analysis has been used, incorporating colored resins to discriminate between liptinite and the background resin;6 however, this technique also is not without its difficulties, especially in regard to particle boundaries. Figure 1 shows how particles in a colored resin can have a black boundary, which is caused by (5) Lester, E.; Watts, D.; Cloke, M. Fuel 2002, 81, 2209-2217. (6) Verhelst, F.; David, P.; Fermont, W.; Jegers, L.; Vervoort, A. Int. J. Coal Geol. 1996, 29, 1.

10.1021/ef020246k CCC: $25.00 © 2003 American Chemical Society Published on Web 07/19/2003

Microlithotype Analysis on Particulate Coal

Energy & Fuels, Vol. 17, No. 5, 2003 1199

in a logic filter. The maceral data is created from the histogram by thresholding either side of the vitrinite peak(s). Figure 2 shows how the microlithotype classification system is distributed between the three major maceral groups. Figure 3 shows the logic tree that explains how the maceral composition dictates the microlithotype classification. This paper demonstrates how complex petrographic characterization can be performed on individual coal particles with automated image analysis techniques, using black and white images. Experimental Section

Figure 2. Trigonal diagram showing the microlithotype classification system across the three maceral groups.

subsurface reflection. Colored resin coal blocks are not as practical as black resin blocks for manual analysis. A technique recently published by the authors5 demonstrates how entire coal particles (including liptinite) can be correctly identified in black and white, using normal noncolored resin. This technique can be used to measure the histogram profile of each particle, along with size and shape data. These data can then be processed in the spreadsheet to create the microlithotype data, by sorting the maceral composition data from each particle

The equipment used in this study included a personal computer-based (PC-based) image analysis system that was operated on a 850 MHz Pentium III computer loaded with KS400 v3.1 software (supplied by Imaging Associates, Ltd.) and a Zeiss Axiocam color digital camera that used a PCI interface card with thin fiber-optic cable for data and control lines at a rate of 200 Mbit/s. The camera captures images with a resolution of 1300 pixels × 1030 pixels. Coal blocks were prepared in Struers liquid resin. Five coals were selected for the study, on the basis of their petrographic composition, and sieved into 53-75 µm and 106-125 µm fractions. Table 1 shows the origin and petrographic information of the five coals. The mean random vitrinite reflectance of the coals ranged from 0.5% to 1.2%. The vitrinite content was in the range of 50%95%, and the inertinite content was 2%-50%. One coals Drummondswas also sieved into a greater range of size fractions, to evaluate the sizing function of the system and to determine how microlithotype results varied with particle size.

Figure 3. Logic tree for the assignment of microlithotype classifications.

1200 Energy & Fuels, Vol. 17, No. 5, 2003

Lester et al.

Table 1. Maceral Analysis Results from Automated and Manual Methods Content (%) 38-53 micron

53-75 µm

75-106 µm

106-125 µm

125-150 µm

+150 µm

component manual automated manual automated manual automated manual automated manual automated manual automated liptinite vitrinite inertinite

4.1 88.1 7.8

Bentinck (UK deep mine), Rran ) 0.68% 7.6 6.8 6.5 85.4 83.2 84.3 7.0 10.0 9.1

liptinite vitrinite inertinite

4.4 42.8 52.8

Koon Fontein (South Africa), Rran ) 0.62% 2.9 3.2 3.5 45.8 45.2 43.0 51.3 51.6 53.6


2.0 95.2 2.8

Kaltim Prima (Indonesia), Rran ) 0.54% 2.1 2.2 1.8 94.3 94.8 93.6 3.7 3.0 4.7


0.8 65.2 34.0

Stanley Moss (UK open cast), Rran ) 1.30% 1.2 1.6 1.2 67.0 59.8 65.7 31.8 38.6 33.1

4.8 82.4 12.7

5.4 84.0 10.6


5.6 77.6 16.8

4.9 81.9 13.2

Drummond (USA), Rran ) 0.78% 6.0 6.2 9.2 81.2 86.0 79.0 12.8 7.7 11.8

The Image Analysis Method This paper extends the work recently described by some of the current authors5 and describes an image analysis system that can be used to analyze individual coal particles, rather than just to measure the maceral composition of entire coal samples. The technique is based on the difference in absorbtivity of the liptinite and the background resin. Figure 4a shows a particle that is composed of vitrinite (gray phase), inertinite (white phase), and liptinite (dark phase); the dark background is the resin. This image is captured at an exposure of 350 ms, where the greatest contrast between the three maceral phases exists. However, if the exposure time of the camera is increased to 750 ms, Figure 4b is produced. The vitrinite and inertinite peaks both increase in grayscale reflectance and the liptinite now reflects more significantly than the background and can therefore be separated. If a mask is created from this new image, composed of the three macerals and overlayed on the initial lower exposure image (Figure 4a), then the correct reflectance of the macerals can be measured and useful morphological data can also be acquired, such as particle width, circularity, perimeter, and area. To measure histogram and morphology data on a particle-by-particle basis, it is necessary to separate all touching particles. However, this process is complicated by the nonuniformity of coal particles on the block surface. If every coal particle was essentially “circular”, then the method of separation would be relatively simple and would rely on basic image processing functions such as erosion and dilation. A more reliable system was developed using a technique called binary grain reconstruction. The entire imaging process can be observed in Figure 4a-j and is described as follows. The first step is the creation of a mosaic image. This mosaic normally is composed of a 3 × 3 matrix of images, with each image having a resolution of 1300 pixels × 1030 pixels, which creates a final mosaic of 3090 pixels × 3900 pixels. The

6.2 84.0 9.8

6.6 77.0 16.4

8.7 78.0 13.3

12.0 70.0 18.0

10.4 75.9 13.7

larger mosaic image is used in the program to allow as many whole particles to fall inside the edges of the mosaic. For simplicity, Figure 4a is a single image with a resolution of 1030 pixels × 1300 pixels. This step is followed by creation of the overexposed mosaic image (Figure 4b), delineation of the image to create the sharpest edges for thresholding and reduce halo effects (Figure 4c), and then thresholding of the delineated overexposed image to produce the binary mask (Figure 4d). The next step of the process involves the removal of particles smaller than 5 µm in size (Figure 4e). This is an arbitrary threshold because, below this size, the histogram profiles are poorly resolved, because of the small number of measurements that are possible at the 1300 × 1030 resolution of the image. Analysis time is related to the number of particles analyzed rather than the size of the particles; hence, the presence of small (and less significant) particles increases analysis time. The “exclusion” process to remove these small particles can be overridden if the system was used to analyze pulverized coal samples. The next step involves the removal of all particles touching the edge of the image, because these particles cannot be sized properly (Figure 4f). Minimization of the image size to the region around the particle(s) of interest occurs next, which saves on processing time (Figure 4g). Any touching particles then are separated using a function called binary grain reconstruction. A distance transform function is performed on the binary image, which is then smoothed to reduce the detail in the particle outline; this procedure allows the separation to be based on the larger features in the image. A watershed operation is then performed on the image, which identifies the boundaries between the separate areas in the particle. The detected boundaries are combined with the binary particle image, resulting in an image with boundary lines superimposed on the image particles (Figure 4h). The masks from the separate particles are then overlaid back onto each particle in Figure 4a in the initial gray scale image (examples in Figure 4i and j). Finally, the autostage is moved to capture a new mosaic and the process is




Lester et al.

Figure 4. Stepwise photographic depiction of the binary grain reconstruction imaging process. (a) Initial image captured at 350 ms. (b) Same image at Figure 4a but captured at 750 ms. (c) Delineated version of Figure 4b to give the best edge detection of the particles. (d) Binary image showing the particles separated from the resin. (e) Sub-5-µm particles are removed, to reduce the processing time. (f) Particles touching the edges of the photograph are excluded, to eliminate sizing errors. (g) Field of view is reduced to the region of interest, to reduce processing time. (h) Binary grain reconstruction is used to split the particles. (i) Mask (the binary overlay) for each particle is then identified in turn. (j) Mask in Figure 4i is overlayed on Figure 4a; all the pixels under this mask are measured and then added to the database.

repeated from the first step until 500 particles have been analyzed. The analysis program requires ∼45 min to analyze 500 particles, although the exact time is dependent on the number of particles in each image mosaic. The greater the number of whole particles in the mosaic image, the quicker the analysis time. Elimination of Subsurface Reflections. After the coal block is polished, a small number of subsurface particles can be seen on the surface. These will also be captured and analyzed during the analysis. An example can be observed in Figure 5a. These subsurface particles are out of focus and, as a result, normally have a very uniform grayscale distribution. Figure 5b shows the grayscale histogram for the particle in Figure 5a. The standard deviation of the profile in this figure is 3.65. The average standard deviation for all the particles from this particular sample was >30. It was found that histograms with a standard deviation of 95%. Clearly, the agreement between the results is much better overall, with only one notable outlier. Particle Size Results. Table 5 shows the results for the Feret Max size analysis of all 15 samples. Clearly, there is good agreement between the 53-75 µm samples and the 106-125 µm samples. The variation across the Drummond size fractions is more unusual, but all appear to show a peak close to the 30-60 µm size range and a second peak close to the respective sieved size range. Panels a-f in Figure 12 show the size distributions for each of the Drummond samples. Interpretation of this effect would be simpler if the coal particles were spherical, because the nature of 2D surfaces slices from three-dimensional (3D) objects can be inferred using statistical methods.11 When irregular shapes are involved, this type of deconvolution is difficult and is made more difficult by larger-sized arrays, as observed in Figure 12f. Overall, the average Feret Max value increases as the sieve size increases, and the maximum Feret Max value increases accordingly; however, the

Figure 9. Correlation of microlithotype results for the four repeat analyses of Koon Fontein coal, 53-75 µm. The figure shows the dispersion of the data across the microlithotype grid.


Lester et al.

Table 4. Microlithotype Results for All 15 Coal Samples size fraction method vitrite inertite vitrinertite-V vitrinertite-I trimacerite-V trimacerite-I trimacerite-L liptite durite clarite Bentinck manual IMAS manual 106-125 µm IMAS

58 62 65 56

6 0 7 0

7 12 4 18

manual 53-75 µm IMAS manual 106-125 µm IMAS

19 14 20 16

34 0 12 0

10 23 15 33

manual IMAS manual 106-125 µm IMAS

86 84 78 81

1 0 3 0

manual IMAS manual 106-125 µm IMAS

35 39 52 48

manual 38-53 µm IMAS manual 53-75 µm IMAS manual 75-106 µm IMAS manual 106-125 µm IMAS manual 125-150 µm IMAS manual +150 µm IMAS

46 47 65 58 46 49 51 46 42 40 43 38

53-75 µm

53-75 µm

53-75 µm

6 6 2 3

4 3 3 3

2 0 0 0

1 0 0 0

0 0 2 0

1 0 3 0

16 17 13 18

Koon Fontein 18 50 32 47

4 3 9 0

3 1 7 0

0 0 0 0

1 0 1 0

5 0 4 0

5 7 1 4

4 6 4 7

Kaltim Prima 2 1 0 1

0 0 4 0

0 0 1 0

0 0 0 0

0 0 0 0

0 0 1 0

8 9 8 10

19 0 10 0

15 30 13 23

Stanley Moss 24 28 20 22

2 0 2 0

0 0 1 0

0 0 0 0

1 0 0 0

2 0 1 0

1 3 1 6

9 0 7 0 4 0 7 0 4 0 4 0

15 21 3 14 6 15 4 13 7 14 4 21

Drummond 11 10 4 4 2 5 1 5 3 5 6 5

5 6 2 6 13 10 10 9 16 18 15 10

4 1 0 0 2 1 1 1 2 0 3 2

0 0 0 0 2 0 1 0 3 0 3 0

0 0 1 1 1 0 2 8 2 0 2 0

1 0 1 0 3 0 6 0 1 0 0 0

8 15 18 16 21 19 17 18 20 23 20 24

Table 5. Size Analysis Data of All the Coal Samples Particle Size (µm) 38-53 53-75 75-106 106-125 125-150 +150 µm µm µm µm µm µm Bentinck average minimum maximum standard deviation

Figure 10. Plot of the total deviation in microlithotype results across 2000 particles.

average minimum maximum standard deviation average minimum maximum standard deviation average minimum maximum standard deviation

Figure 11. Correlation between the automated and manual inertite content.

average Feret Max value is clearly affected by the presence of the smaller peak at ∼30 µm. This could be a result of preparation techniques, with larger particles

average 49.3 minimum 13.2 maximum 116.3 standard 19.1 deviation

61.5 13.0 161.1 25.7

92.1 14.4 214.4 48.1

Koon Fontein 63.0 93.0 15.1 14.0 131.6 215.6 24.4 46.9 Kaltim Prima 62.4 98.9 16.5 13.4 136.3 218.5 22.2 45.1 Stanley Moss 58.2 87.6 12.8 13.1 141.4 217.6 26.2 47.4 Drummond 61.2 86.2 13.5 14.7 161.2 260.6 25.2 45.1

97.1 13.4 218.5 45.7

100.6 13.7 223.4 54.8

93.0 14.1 324.5 72.4

present under the surface only being partially exposed during polishing, in the same way that the volume of



Figure 12. Variation across the Drummond coals of different size fractions ((a) 38-53 µm, (b) 53-75 µm, (c) 75-106 µm, (d) 106-125 µm, (e) 125-150 µm, and (f) +150 µm).

an iceberg is much greater below the surface. This “iceberg theory”, in relation to polishing and preparation techniques, will be investigated further by the authors. Plotting Microlithotype Data. The classification system for microlithotypes requires a developed understanding of maceral classes and how they are segregated. A more useful means of displaying the 500 data points is a 3D bar chart plot.12,13 Panels a-j in Figure 13 show the results for the 53-75 and 106-125 µm samples of each coal, with the frequency (Z-axis) weighted for each individual particle size. There is a clear difference in the plots for Kaltim Prima (Figure 13g and h), which is a low-rank, highvolatile subbituminous coal, and Koon Fontein (Figure

13e and f), which is a medium-volatile bituminous coal from South Africa. The plots themselves form a useful visual “fingerprint”. These 3D plots are more useful than the 2D plots, because overlapping data can make 2D plots, such as Figure 9, misleading. There is reasonable similarity between the Bentinck coal (Figures 13c and d) and the Drummond coal (Figures 13a and b), although the former is from the United Kingdom and the latter is from the United States. The Stanley Moss plots in Figures 13i and j seem to fall midway between the Kaltim Prima- and Koon Fontein-type distributions.

(11) Langston, P. A.; Burbidge, A. S.; Jones, T. F.; Simmons, M. J. H. Powder Technol. 2001, 116, (1), 33. (12) Lester, E.; Allen, M.; Cloke, M.; Atkin, B. Coalbed Methane and Coal Geology; Gayer, R. A., Harris, I., Eds.; Geological Society Special Publication 109; Geological Society: London, 1996; p 237. (13) Wigley, F.; Williamson, J.; Jones, A. R. Fuel Process. Technol. 1990, 24, 383.

(1) The image analysis technique described in this paper works using only black and white images, which is a major benefit for performing petrographic analysis, through the use of two different exposure times on a digital camera.

Conclusions


Lester et al.



Figure 13. Results for the coal samples with size fractions of 53-75 and 106-125 µm (frequency weighted for each individual particle size): (a) Drummond, 53-75 µm; (b) Drummond, 106-125 µm; (c) Bentinck, 53-75 µm; (d) Bentinck, 106-125 µm; (e) Koon Fontein, 53-75 µm; (f) Koon Fontein, 106-125 µm; (g) Kaltim Prima, 53-75 µm; (f) Kaltim Prima, 106-125 µm; (i) Stanley Moss, 53-75 µm; (j) Stanley Moss, 106-125 µm.

(2) The system can accurately characterize particulate coal and collect histogram and morphology information that can be processed into microlithotype data. (3) Subsurface particles can be excluded, using their characteristic histogram profiles and morphological features. (4) Three-dimensional (3D) plots of the data can aid in the visualization of the microlithotype information. (5) The boundaries around three trimacerite microlithotypes are large, whereas the criteria for all the others are dictated by a (5% boundary, as with inertite. This can be a problem for the image analysis system, because a particle may comprise 94.4% of a single maceral but would not be categorized as inertite, vitrite, nor liptite. Image analysis is arguably more accurate than manual analysis, because it uses more data points

to make a decision; however, “edge effects” can limit this advantage. These are potential sources of discrepancies that exist between manual and automated results. (6) Understandably, the interpretation of Feret Max data and their relationship with sieved particle size is not entirely straightforward, because the majority of a large particle may remain subsurface, with only a small area visible on the surface. (7) Repeatability tests indicate that at least 500 particles should be counted but 700 particles will generate more-accurate results. A sampling population of more than 700 particles per sample does not improve predictions significantly, although analyzing larger particle sizes could influence the sampling requirement. EF020246K

Automated Microlithotype Analysis on Particulate ... - ACS Publications

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