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Char Characterization Using Image Analysis Techniques Edward Lester, Michael Cloke,* and Martin Allen Coal Technology Research Group, Department of Chemical Engineering, University of Nottingham, University Park, Nottingham, NG7 2RD, U.K. Received September 6, 1995X
Three different image analysis techniques for the characterization and analysis of the morphology of chars are presented and discussed in this paper. Each technique attempts to provide objective and repeatable results for the various morphological characteristics of chars produced in a drop-tube furnace or during pulverized fuel combustion. Each technique is described fully, together with the results of several experiments carried out to assess each method for its potential use on a regular basis. The repeatability of the distance transform method is tested and found to be within acceptable limits when 50 images are analyzed. The benefit of automated image-analysis systems is demonstrated by the distance transform method, which produces large quantities of information regarding the whole char sample as opposed to the assessment of single char particles.
Introduction Most workers identify two distinct stages in the combustion of pulverized fuel.1 The first is pyrolysis and is over quickly with times of 30-100 ms reported.2,3 The second stage is char combustion which may require about 1 s.4 The performance of a particular coal in the furnace will, therefore, depend on how it reacts in each of the stages of pyrolysis and char combustion. Important during pyrolysis is the release of volatiles to provide a stable flame. However, changes occurring during pyrolysis also determine the morphology of the char formed and the morphology of the char types present will affect the overall combustion efficiency.5 Discussion of the behavior of char during combustion is complicated by the number of char classifications proposed6 and the fact that manual char analysis can be both timeconsuming and subjective. For example, the repeatability and reproducibility of maceral analysis have been well-documented,7,8 and, in this case, only three or four main phases may be classified. In manual char analysis, based on point-counting, several more groups need to be classified (a minimum of six are described below) and, clearly, this may lead to greater errors than with maceral analysis. One potential way forward is the use of image-analysis. Image analysis systems have been developed at Nottingham for the analysis of major maceral groups in coal.9,10 This work has been extended to the analysis of chars, and in this paper three different Abstract published in Advance ACS Abstracts, March 15, 1996. (1) Sadakata, M.; Saito, M.; Soutome, T.; Murata, H.; Ohno, Y. Extended Abstr. Joint Int. Conf. Aus/NZ/Jap. Combust. Inst. 1989, 100. (2) Pyatenko, A.; Bukhman, S.; Lebedenskii, N.; Nasarov, V.; Tolmachev, I. Fuel 1992, 71, 701 (3) Tsai, C. Y.; Scaroni, A. W. Energy Fuels 1987, 1, 263. (4) Bend, S. L.; Edwards, I. A. S.; Marsh, H. Int. Conf. Coal Sci. Tokyo 1989, 437. (5) Oka, N.; Murayama, T.; Matsuoka, H.; Yamada, S.; Yamada, T.; Shinozaki, S.; Shibaoka, M.; Thomas, C. Fuel Process Technol. 1987, 15, 213. (6) Cloke, M.; Lester, E. Fuel 1994, 73, 319. (7) British Standard 6127:1981. (8) Cloke, M.; Lester, E.; Allen, M.; Miles, N. J. Fuel 1995, 74, 654. (9) Lester, E.; Allen, M.; Cloke, M.; Miles, N. J. Fuel 1994, 73, 1729. (10) Cloke, M.; Lester, E.; Allen, M.; Miles, N. J. Fuel 1995, 74, 659. X
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techniques are described which can be used for the characterisation of char particles using image analysis. Experimental Section Equipment. The microscope used is a Leitz Ortholux II POL-BK. A Hamamatsu C2400 black and white high-resolution video camera is mounted on top of the microscope and is used to downput the image under the microscope to an IBAS 2000 Image Processing System. The image system is essentially a PC hosted, hardwired image processor manufactured by the Kontron Image Analyser Division. All experiments used a 32× magnification oil-immersion lens and nonfluorescing oil from Leitz. The eyepiece of the microscope includes a 10× magnification lens, and each image downloaded to the image-analysis system has a screen width of 0.261 mm. Block Preparation. A similar method is used to that previously described for mounting powdered coal.9 Chars, especially those with thin walls, are more difficult to mount than coal. This inevitably leads to some damage to char particles during the mounting process. In order to minimize this, several tests were carried out on both the block pressing and polishing processes. The final method used is outlined below and the effect of the fracturing and fragmentation of the chars in relation to the image-analysis techniques investigated is discussed later. A powdered dental resin called Simplex Rapid is mixed with the char in the approximate proportions 1:1 respectively, by volume. The mix is then “wetted” with a small amount of methyl methacrylate. Using a Presi Mecapress C, the mixture is pressed at 120 °C and 200 kPa pressure for 10-12 min followed by a short cooling time. Each block is then polished using a Struers Pedemat Rotapol polisher. The polishing takes about 3 min.
Image Analysis Methods Method 1. Char Voids Percentage. Figure 1 gives a representation of six basic char types used in this laboratory for manual char analysis. These twodimensional representations are typical of what would be observed when chars are mounted in a resin block and observed under the microscope. The black areas represent char material and the white areas voids or © 1996 American Chemical Society
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Table 1. Voids Percentage for Six Char Types Produced from 11 Different Coals char type coal
tenuisphere voids (%)
tenuinetwork voids (%)
crassisphere voids (%)
crassinetwork voids (%)
fusinoid voids (%)
solid voids (%)
Kaltim Prima Bentinck McQuarrie Pinang Kellingley El Cerrejon China SSM Island Creek Drummond Kromdrai Tower average std dev
61.0 53.1 61.7 57.7 48.1 56.2 54.8 54.2 57.8 54.8 51.6 55.5 4.0
59.6 49.9 48.5 50.1 52.6 47.5 49.9 51.1 47.9 45.0 42.3 49.5 4.4
37.6 42.3 44.7 39.7 41.5 42.0 41.3 37.4 33.5 34.9 33.5 38.9 3.8
34.4 40.8 37.5 38.4 33.6 39.6 32.7 35.4 28.2 33.5 22.2 34.2 5.4
12.9 na 11.1 na na 10.2 11.3 16.3 11.4 9.3 5.9 11.1 3.0
2.9 4.2 1.4 2.4 6.2 1.5 2.5 2.9 2.4 1.2 6.3 3.1 1.8
Figure 2. Method for calculating the char voids % of an individual idealized char.
Figure 1. Char types used in manual identification experiments.
holes. The char voids percentage represents the percentage white area to the total area of white plus black. From Figure 1 it can be observed that each char type will have a characteristic voids percentage. In order to test this, 11 char samples produced from different coals under standard conditions in a drop-tube furnace (1300 °C, 100 ms, 2% O2), were examined. In each case, 100 particles were examined and identified manually as a particular char type. Each char image was then captured individually using a video camera attached to the image analyzer. The image was then converted to a binary image, with the char material shown in white, and the whole image was then filled. The voids percentage was then calculated as shown in Figure 2. The results are shown in Table 1 which demonstrates that each char type has a characteristic range for voids percentage, although three specific types can be identified in terms of thin-walled (tenui), thick-walled (crassi), and fusinoids and solids. The technique was then developed to give an auto-
mated method capable of calculating voids percentage for a whole field of chars and then for a series of images. The final result is an average voids percentage, which would give an indication of the type of chars found in a particular sample. A low voids percentage number for a char sample would indicate that the sample contained a significant number of solid or fusinoid chars. Conversely, for a char sample to produce a high voids percentage, it would require high levels of open chars. As mentioned in the Experimental Section, one of the main problems is maintaining the integrity of the individual char particles during the mounting process. A process was necessary to recreate the solid walls of a char; otherwise the filling process, necessary to calculate voids percentage, would not be possible. Figure 3 shows how the walls of a fractured char can be rejoined to form a solid char wall without altering the overall wall thickness. A program was developed which allows the manipulation of each screen image so that char particles are closed to form perfectly whole char. This method uses the tool called close on the morphology software on the IBAS 2000. An alternative to this is the use of the erosion and dilation functions, but this has significant disadvantages. Dilating a broken char, followed by erosion, would leave a closed object, but not with the same boundary edges. The retention of the same boundary edges is essential for the final area measurement needed to calculate voids percentage. The dilation and erosion tools literally inflate and deflate white pixels in eight directions. The close function retains the details of the initial image and hence the final product is similar to the initial broken char but with the char walls joined together. When the erosion stage of the close function occurs, no walls are broken, whereas three dilations and three erosions would simply produce a binary image similar to the initial image but without any of the initial definition. Figure 4 simulates the
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Figure 3. Closing sequence used to repair fractured char walls.
Figure 4. Difficulty of maintaining initial wall definition when using the dilation and erosion functions.
effect of the dilation-erosion process on the wall definition of a char. Once the close function has been used to join the char walls, the char particles are then filled to produce solid objects. A simple ratio of image areas then allows char voids percentage to be calculated. This voids percentage can then be used to indicate the openness of the particles. In order to test the process, a number of char blocks, which had also been manually characterized, were analysed. Fifty consecutive images, each containing approximately 10 chars, were examined. The time required for analysis was about 14-16 min depending
Figure 5. Correlation between manual and automated voids %.
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on the need for autofocusing. The char voids percentage from the manual results was calculated using the individual voids percentage from Table 1. Applying this method was not without an accepted degree of inaccuracy since, in reality, all char types do not have the same voids percentage. The correlation between manual and automated results is shown in Figure 5. Using the anticipated errors for each char type, an error bar has been added for each separate point on the graph. The results in Figure 5 indicate that there is a correlation between manual and automated char voids percentage, but only with a certain degree of interpretation. The reason for the distribution of results is probably due to the close function not working efficiently. Most chars appeared close together in the block, which is probably a function of the amount of char material used to make each block. It may also be a function of the powder resin causing localized agglomerations. These factors meant that the close function joined separate char particles together before filling in the voids, as well as not always joining the walls of some larger chars. The severity of the close function can be increased to allow the larger particles to be “repaired”, but this would have resulted in more of the smaller particles being joined to each other. Realistically, it did not appear possible to use this method without incurring an unacceptable amount of scatter to the results. Method 2. Semiautomated Analysis. Because of the problems with the voids percentage program, a semiautomated program was written to enable the manual repair of broken chars as well as the manual identification of the selected char type. The first stage of the operation required a single particle to be identified, which was then rebuilt using a mouse. The degree of subjectivity involved depended on the physical condition of the char. Once the particle had been restored, and identified for its char type, a series of measurements were made using the different functions of the image-analysis software. These included maximum and minimum diameter, average wall thickness, and char voids percentage. All data were then appended to a designated file, which could be easily imported to a spread sheet package like Microsoft Excel. The char voids percentage figures for individual char types obtained using this method are those shown in Table 1. As well as this data, information regarding
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Figure 6. Correlation between maximum and minimum diameters for the chars from different coal types.
maximum and minimum diameters for chars from different coals are shown in Figure 6. The chars used in these experiments were the products of sized coal samples, sieved into the 106-125 micron range, and therefore it might be expected that this kind of size banding would be seen. One notable feature in the figure is the pattern between the maximum and minimum size of each sample type. A high maximum diameter corresponded well to a high minimum size, and this could well be a feature of the swelling of each coal used in the experiment during pyrolysis. The benefit of the system is that it provides a huge amount of information about each particle. All the data generated could be of potential value when considering utilization processes such as char combustion. The drawback of the system is the time required for the analysis; 100 particles require around 1 h from the operator. Hence this technique is labor intensive and time-consuming. However, it does have an advantage over simple manual identification techniques in that quantitative information is also obtained which has benefits in identifying and ascribing char types. Method 3. Distance Transform. To reduce analysis time further, an automated process was investigated which used a distance transform function to characterize char particles. The basis of the method is to assume that the thickness or general bulk of char material is generally unique to a particular char type. Apart from this characteristic measurement, it is important to quantify char thickness for utilization purposes such as coal combustion. During combustion, a thin-walled cenosphere of char may burn at a similar rate as a solid sphere of the same diameter, but it would require far less time to burn out due to the much lower quantity of char material present. Therefore, a technique which quantifies the thickness of the chars present would be useful in the investigation of char combustion processes. The method used to quantify the thickness of the char walls is shown in Figure 7. The char particles are first singled out into binary images. The edges of any
Figure 7. Distance transform mapping process.
particle (shown by a 0-255 gray scale gradient) are given the gray scale number 1. Any neighboring white pixels are then called [2]. The row of pixels after this is then [3]. Hence, a char particle can be contoured in this way until all the white pixels present after thresholding have been designated a contour number. The process can be applied to single char particles or a series of images each containing several char particles, whereby percentages of char within the first, second, and third etc. contours, represent the cumulative percentage for the whole scan. This means that tenuispheres would be fully mapped out within a few contours, whereas a solid will require many more contours. In this way the relative thickness of char particles can be measured and, hence, a more realistic representation of the whole char sample presented. To test the distance transform method, a series of experiments were carried out using the program. This was done to test its validity and to determine the capabilities of the program. Three sets of tests were carried out. The first involved idealized chars which were drawn and used to show characteristic results for different char types and the effect of char size. The second tested the method on real chars and the third examined the repeatability of the technique. The results are described in the next section. Results and Discussion Idealized Chars. A series of idealized chars were drawn up, which represent the main types of char seen
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Lester et al. Table 2. Distance Transform Parameter Results for 10 Ideal Char Types
Figure 8. Idealized char types used to test the distance transform mapping process.
under the microscope. Figure 8 shows these char types which are similar to those shown in Figure 1 but contain additional mixed chars and char fragments. Each char type was captured using the Hamamatsu video camera attached to the image analyzer and then the whole
char type
[3] (%)
[5] (%)
[95%]
tenuisphere tenuinetwork tenuifragment crassisphere crassinetwork crassifragment fusinoid mixed porous mixed solid solid
43.8 36.3 39.8 12.8 12.0 19.4 21.9 15.9 7.7 3.8
71.3 60.2 62.9 21.4 19.5 31.1 37.3 26.3 12.5 6.2
7 9 9 32 32 19 13 39 88 97
screen was inverted to create white chars on a black background (as would be seen with real chars in oil immersion). Each char was then mapped using the distance transform technique. Figure 9 shows the cumulative histogram profiles for each of the different char types. Each char type has its own distinctive trace. The number of contours required to represent the amount of char material in thin-walled char types can be seen to be much less than for thick-walled and solid material. Indeed, for solids only about 50% of material was mapped after 30 contours. The crassi- and tenuifragments were included in the test to evaluate how broken-walled chars would affect the process. It appeared that there was little difference between the tenuifragment, tenuisphere, and the tenuinetwork. Likewise, the crassifragment showed a similar trace to its sphere and network counterparts. This result is useful because it allows the analysis of char blocks to be performed without serious concern about the structural integrity of the char samples. Table 2 gives the cumulative percentages of char covered at 3 contours (called [3]), 5 contours (called [5]), and the contour number corresponding to a 95% covering of the chars (called [95%]). Thus, the char type is easily characterized using one of these parameters. In order to examine the effect of char size, the char diagram shown in Figure 8 was enlarged (141%) and reduced (65%) using a photocopier. The new images were captured and analyzed in the same way as described above. The results for each type were then compared. Table 3 gives the new [3], [5], and [95%]
Figure 9. Distance transform profiles for the idealized char types.
Char Characterization Using Image Analysis Techniques Table 3. Distance Transform Parameter Results for the Three Sizes of Ideal Chars
char type tenuisphere tenuinetwork tenuifragment crassisphere crassinetwork crassifragment fusinoid mixed porous mixed solid solid
[3] [5] [95%] image size value ratio value ratio ratio (%) (%) (%) (%) (%) value (%) 65 100 141 65 100 141 65 100 141 65 100 141 65 100 141 65 100 141 65 100 141 65 100 141 65 100 141 65 100 141
71.5 43.8 33.1 59.3 36.3 27.6 60.8 39.8 30.7 20.3 12.8 9.9 18.7 12.0 8.4 30.6 19.4 14.7 36.5 21.9 17.6 25.5 15.9 12.2 12.1 7.7 5.6 8.1 3.8 5.3
61.3 100 132 61.2 100 132 65.5 100 130 63.1 100 129 64.2 100 143 63.4 100 132 60.0 100 124 62.3 100 130 63.6 100 139 46.9 100 139
96.8 71.3 54.1 91.8 60.2 45.7 92.2 62.9 48.8 33.6 21.4 16.0 30.7 19.5 13.8 48.4 31.1 23.8 62.9 37.3 29.4 41.0 26.3 19.9 19.2 12.5 9.1 13.0 6.2 8.6
73.7 100 132 65.6 100 132 68.2 100 129 63.7 100 134 63.5 100 141 64.3 100 131 59.3 100 127 64.1 100 132 65.1 100 137 47.7 100 139
4 7 10 5 9 12 5 9 12 21 32 43 21 32 45 12 19 26 9 13 19 25 39 56 57 88 124 63 97 136
57.1 100 143 55.6 100 133 55.6 100 133 65.6 100 134 65.6 100 141 63.1 100 137 69.2 100 146 64.1 100 146 64.8 100 141 64.9 100 140
results for the new chars and shows that the distance transform method could detect changes in size, even when the char shape was exactly the same. There are some variations between the ratio of the parameter and the actual ratio of the area for each char type. In most cases this is observed where the parameter is a low number and hence more subject to absolute error. During char burnout the size of particles is a very important factor in the rate of burnout and, therefore, the advantage of this technique is that it gives results which are indicative of both char type and size.
Energy & Fuels, Vol. 10, No. 3, 1996 701 Table 4. Distance Transform Parameter Results for the Three Images Shown in Figure 9 brief description image 1 image 2 image 3
mixture of thin-walled networks with a fusinoid and an open solid (from sclerotinite) thin-walled spheres and networks thick and thin-walled chars with a large fusinoid
[3] (%)
[5] (%)
[95%]
63
76
19
91 77
99 89
4 9
Manual char analysis, like maceral analysis, is based on 500 individually identified points hence, on a statistical basis, any one point represents 0.2% of the total analysis. However, the value of 0.2% is irrespective of mass and volume and so a difficulty will arise in the interpretation of manual analyses when correlating carbon-in-ash values to specific char particles. A solid is a thick structure which contains significantly more carbon than a tenuisphere, and in, for example, a fly ash sample which contains 500 char particles, 499 of which are tenuispheres with only one solid present, in terms of mass and volume, the solid could represent .0.2% of the total mass of carbon in the sample. Since the distance transform method is able to recognize size variations, even within the same char type, the relative effect of one char type over another can be investigated. Figure 10 shows the effect of altering the ratio of thinwalled particles to a single, thick-walled particle. It is clear that the shape and position of the curve depend on the nature of the single “thick” particle as well as the number of thin-walled particles added. Real Chars. In order to demonstrate the program, three images were captured containing several real chars. The images were also photographed and these are shown in Figure 11. Table 4 shows the [3], [5], and [95%] results for each image. The image containing the majority of solids and fusinoids produced the smallest [3] and [5] values, and the largest [95%] value, which, on morphological grounds, would indicate potential burnout problems during combustion. Conversely, thinwalled chars are shown to give much higher values of
Figure 10. Effect of an increasing ratio of thin-walled chars to a single thick-walled char.
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Figure 11. Photographs of multiple chars used to test the distance transform method.
Figure 12. Standard deviation of ACA percentages against contour number.
the [3] and [5] parameters and a much lower value for the [95%] parameter. Repeatability of Scanning Patterns. The distance transform process was made into a fully automated program where a series of images were captured and analysed and the results produced in the form of a cumulative array. As with all analysis processes, it is necessary to test the repeatability of the system. In order to do this, 10 sets of 50 images were scanned at different positions on a single char block and the standard deviation over the 10 sets compared with the cumulative percent over each contour. The data obtained are plotted in Figure 12 and show that the greatest standard deviation is given over the 3, 4, and 5 contours. This would be expected since at this contour level most char types will be contributing to the data. As the contour number increases thinner-walled chars will be excluded from the analysis and greater similarity
between the results from the different sets of data would be expected. Figure 12 also demonstrates that with 50 images the maximum standard deviation recorded is still within an acceptable level for experimental error. A similar-shaped curve is shown in Figure 13 where the results from the 10 sets of scans were compared with results obtained from a different char, which contained characteristically thicker-walled chars. The percentage difference represents the cumulative percentage for a given contour number for the thinner-walled chars minus the cumulative percentage for the thicker-walled chars. For example, referring back to Figure 9, the difference between cumulative plots for thinner- and thicker-walled chars can be seen. The greatest variation between the results shown in Figure 13 is again the 3-6 contour region where the influence of the thinner-walled chars is the greatest. The processing time for 50 images is about 12-15 min
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Figure 13. Percentage difference between a char sample containing thin-walled chars and a sample which contains mainly thick-walled chars.
using the current equipment. This time can be improved using a faster processor and dedicated software. Thus, the distance transform technique provides a quantitative and repeatable method which can be carried out reasonably quickly in order to characterize chars. In these examples the chars have been produced on a drop-tube furnace but chars produced during actual combustion can also be classified in this way. Work has been carried out at Nottingham to use this technique to investigate chars produced from coals over the world and these results will form the basis of future publications.
the semiautomated program produces a great deal of useful information, and could, perhaps, be adjusted to include the distance mapping function as well. The main benefit of the distance transform method is the capability of assigning each char type with a meaningful profile and to give a quantitative measure to the overall char type produced from different coals. The program can also distinguish between identical shapes of different sizes. For utilization processes, such as combustion, the technique can provide valuable information in interpreting the behavior of different coals.
Conclusions Three different char characterization methods using image analysis have been devised and investigated. The distance transform technique holds the most promise in terms of a rapid characterization technique, although
Acknowledgment. The support of the Science and Engineering Council, UK, and PowerGen plc is acknowledged for the funding of this work. EF9501713
