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Advanced Automated Char Image Analysis Techniques Tao Wu, Edward Lester,* and Michael Cloke School of Chemical, EnVironmental and Mining Engineering, UniVersity of Nottingham, Nottingham NG7 2RD, U.K. ReceiVed NoVember 7, 2005
Char morphology is an important characteristic when attempting to understand coal behavior and coal burnout. In this study, an augmented algorithm has been proposed to identify char types using image analysis. On the basis of a series of image processing steps, a char image is singled out from the whole image, which then allows the important major features of the char particle to be measured, including size, porosity, and wall thickness. The techniques for automated char image analysis have been tested against char images taken from ICCP Char Atlas as well as actual char particles derived from pyrolyzed char samples. Thirty different chars were prepared in a drop tube furnace operating at 1300 °C, 1% oxygen, and 100 ms from 15 different world coals sieved into two size fractions (53-75 and 106-125 µm). The results from this automated technique are comparable with those from manual analysis, and the additional detail from the automated sytem has potential use in applications such as combustion modeling systems. Obtaining highly detailed char information with automated methods has traditionally been hampered by the difficulty of automatic recognition of individual char particles.
Introduction The pulverized fuel process has been documented and is generally well-understood.1-4 During pulverized fuel combustion the coal particles are rapidly pyrolyzed2,3 (10-100 ms) to yield char particles which then burnout4 (50-2000 ms). The importance of char morphology on burnout is also well-documented,5-8 hence, the ability to describe chars in a meaningful way to those investigating char combustion behavior is very useful.5,9 Oil immersion microscopy is a means of studying char morphology by observing sectioned char particles. Scanning electron microscopy is another method of imaging char particles but is less useful when attempting to quantify char types, although not impossible.10,11 * To whom correspondence should be addressed. Telephone: 00 44 (0) 115 951 4974. Fax: 00 44 (0) 115 951 4115. E-mail: Edward.Lester@ nottingham.ac.uk. (1) Smoot, L. D. Fundamentals of coal combustion: For clean and efficient use; Elsevier: Amsterdam, 1993. (2) Pyatenko, A.; Bukhman, S.; Lebedenskii, N.; Nasarov, V.; Tolmachev, I. Experimental investigation of single coal particle devolatilization by laser heating. Fuel 1992, 71, 701-704. (3) Tsai, C. Y.; Scaroni, A. W. Energy Fuels 1987, 1, 263-269. (4) Bend, S. L.; Edwards, I. A. S.; Marsh, H. Petrographic characterisation of coals to relate to combustion efficiency. Proceedings of International Conference Coal Science, Tokyo; 1989; pp 437-439. (5) Shibaoka, M. Microscopic investigation of unburnt char fly ash. Fuel 1985, 64, 263-269. (6) Cloke, M.; Lester, E.; Gibb, W. Characterization of coal with respect to carbon burnout in p.f-fired boilers. Fuel 1997, 76, 1257-1267. (7) Bailey, J. G.; Tate, A.; Diessel, C. F. K.; Wall, T. F. A Char Morphology System with Application to Coal Combustion. Fuel 1990, 69, 225-239. (8) Cloke, M.; Wu, T.; Barranco, R..; Lester, E. Char characterisation and its application in a coal burnout model. Fuel 2003, 82, 1989-2000. (9) Atlas of char occurrence; Lester, E., Alvarez, D., Eds.; The Combustion Working Group-ICCP Char Atlas: 2000; [CD-ROM]. www.iccop.org. (10) Wu, H.; Bryant, G.; Benfell, K.; Wall, T. An experimental study on the effect of system pressure on char structure. Energy Fuels 2000, 14, 287-290. (11) Jones, R. B.; Morley, C.; McCourt, C. B. Maceral effects on the morphology and combustion of coal char. Proceedingss1985 International Conference on Coal Science; 1985; pp 669-672.
Of the many papers discussing the attributes of char morphology,5-7,9-14 most are based on manual or semiautomated identification techniques,5,6,13,14 with far fewer arising from automated image analysis methods. There is a clear reason for this. Char analysis for manual operators requires the identification of morphological features such as wall thickness and porosity, and these can be described in clear terms.7,9 However, these ‘rules’ cannot be directly transferred to a computer based system. There are two particular problems that hinder automated image analysis based characterization: the fragility of the char, which results in breakages in the char wall structure; and the separation of individual chars that are touching each other. Char walls are often incomplete, either as a result of porosity or wall breakage during preparation. Figure 1 shows a single char particle with breaks in the char wall. A manual operator with some experience can easily identify this as a tenuisphere by making a set of decisions about the particle, including the decision that this is a single char, not three thin-walled fragments originating from three separate chars. These decisions are straightforward and made, in part, subconsciously by the operator, but a computer system needs implicit rules that enable the computer to make the same decision. With the automated system, preprogrammed rules about repairing and reconnecting chars would be necessary. A manual operator analyzing char will make decisions about what is in view, not just based on what is visible, but also on what is likely. Figure 2 is an example of this process. An experienced manual operator will observe two particles that are touching and describe each particle separately. Defining how (12) Lester, E.; Cloke, M.; Allen, M. Char characterization using image analysis techniques. Energy Fuels 1996, 10, 696-703. (13) Alvarez, D.; Borrego, A. G.; Mene´ndez, R. Unbiased methods for the morphological description of char structures. Fuel 1997, 76, 12411248. (14) Barranco, R. The characterization and combustion of South American coals. Ph.D. Thesis, University of Nottingham, U.K., 2001.
10.1021/ef050360d CCC: $33.50 © 2006 American Chemical Society Published on Web 04/27/2006
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Important Morphological Features of Char
Figure 1. Char with broken walls.
Figure 2. View of two touching char particles.
the manual operator decides that there are two particles touching and where the boundary between the particles occurs is made subconsciously, based partly on experience. A computer system would initially only identify one particle and would require extra instructions about how to separate the two particles, as well the understanding that any given object might actually be a group of touching objects. These two examples demonstrate why automated systems are difficult to develop. Any increase in the number of rules inevitably leads to an increase in the amount of image processing that is required, which, in turn, leads to increases in analysis time. Computer processing speeds have therefore dictated what is achievable with image analysis. Processing speeds for PCs have increased more than 30 times in the past 10 years.15 The advent of the 3+ GHz computer has meant that the processing time to analyze a char sample is more acceptable. (15) Fact Index. Timeline of computing 1990-forward [Online], 2004. Available at: http://www.fact-index.com/t/ti/ timeline_of_computing_1990_ forward.html> [Accessed on Aug. 26, 2004].
Much effort has been made to describe the different structural types of char particles and assign them into specific catergories.5-8,13,14,16,17 Figure 3 shows a series of chars and the logic tree used to classify them.9 While there are many different types described in the literature, there are essentially three fundamental characteristics that are used to differentiate between them, namely, porosity, pore structure, and wallthickness distribution. The driver for using automated systems lies, in part, with the desire to collect more information than is currently possible with manual analysis. Alongside these morphological features, some additional geometric characteristics of char can also be measured while performing automated char image analysis. Geometric attributes of individual char particles such as particle size, perimeter length, and the relationship between primary and secondary pores can also be quantified. These characteristics are much easier to collect using image analysis methods and could be useful in combustion studies or burnout modeling. Figure 4 identifies some of the useful and important features in char particles. Char Porosity. Two kinds of porosity can be identified from char image analysis, primary porosity, which counts the central void(s), and secondary porosity, which takes into account voids located on the char boundaries. While the manual operator can qualitatively assess this feature, quantifying it would be difficult. Char Wall Thickness. The classic distinction between thinwalled (tenui) and thick-walled (crassi) chars is based on the majority of the walls being above or below 3 µm, respectively.9 This distinction is based on the experience of the ICCP combustion working group and has been adopted as a suitable threshold between the types. Secondary pores are generally ignored when making an assessment of the thickness of char walls; i.e., they are counted as char material. Particle Shape. Char particles do not tend to form into perfect spheres, so a single width measurement is not the best means of describing char shape. Therefore, some parameters are needed to represent char size and its shape. The minimum and maximum dimensions (feretmin and feretmax) of the char particle are employed to give some indications of its shape. An equivalent char radius is also adopted to characterize char size based on its area. The char size distribution is then associated with the particle size measurement. Char Voidage. To distinguish spherical particles (tenuisphere and crassisphere) from network particles (tenuinetwork, crassinetwork, mixed porous, and mixed dense), information about the size and distribution of voids within the char particle is needed. In this study, every major void of individual particles is detected and measured. For particles have more than two voids, the first three largest voids are used to set up an index that is used to distinguish spherical particles from network particles. The present paper describes the development of a new technique for automated image analysis system (ChIAS), which was devised as part of a coal burnout modeling project. The new technique has been tested against manual char image analysis data and uses the features described above to identify the char type. (16) Lightman, P.; Street, P. M. Microscopical examination of heat treated pulverized coal particles. Fuel 1968, 47, 7-28. (17) Street, P. J.; Weight, R. P.; Lightman, P. Further investigation of structural changes occurring in pulverized coal particles during rapid heating. Fuel 1969, 48, 343-365.
Char Image Analysis Techniques
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Figure 3. Char classification used by the Combustion Working Group of the ICCP.9
Figure 5. (a, b) Reconstructing char walls by using the close function. Figure 4. Some important char characteristics.
Experimental Section Image Analysis System. Polished char blocks were put under a microscope (Zeiss Leitz Ortholux II POL-BK, with a ×32 magnification oil-immersion objective and a ×10 magnification eyepiece), which has a two-dimensional automatic stage controlled either by a computer or a manual control joystick. A very flexible high-performance color digital camera, Zeiss AxioCam, is attached to the microscope and is connected to a PC by an optic fiber cable. The computer has a 3 GHz processor with 1Gb of RAM and operates with the KS400 version 3.1 image analysis system (provided by Imaging Associates Ltd., United Kingdom). The camera is capable of capturing images in 1300 × 1030 pixels, which can be then transferred to the computer via the optic fiber cable at a rate of 200 Mbits/s for processing and analysis. A computercontrolled stage allows a sequence of images to be captured along a systematic grid (normally 5 × 50), covering a long, narrow area of the polished surface. Coal Selection and Char Sample Preparation. Prepared in blocks using a liquid resin (Estratil 2195, provided by Cray Valley Ltd. Spain) mixed with methyl ethyl ketone (50% (w/w)), 30 char samples were polished carefully for microscopic observation. Pyrolyzed chars of all 15 coals in two size fractions (53-75 and 106-125 µm) were collected from a drop tube furnace operating at 1300 °C, with 1% O2 and 100 ms residence time.
Automated Analysis Method Char Reconstruction. A large proportion of thin-walled chars after mounting and polishing are in fragments. It is essential to group those broken boundaries and fragments into one integral char image. The integrity of the char walls is vital when measuring porosity and wall thickness since the system must fill the internal pores, which cannot be achieved with holes in the external walls. Char walls can be manually reconstructed by drawing lines to fill the gap(s).7,12 Image analysis functions such as dilation and erosion have shown some success in reconnecting char walls,12,13 although this can lead to a distortion of the original image, which consequently leads to errors in char area measurements. It was suggested in earlier publications that the use of “close” function is a better choice to repair broken char walls.12,13 The close function is a function in some commercial image analysis software packages that can be used in char-wall reconstruction. Though the close function is a compound procedure of dilation followed by erosion, it has the additional ability to preserve the original size of the object(s) analyzed. The subtlety of the function can be controlled by defining the “shape factor”. This factor specifies the direction in which the char pixels can be eroded and dilated. Figure 5a shows the difference between
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Figure 6. Schematic of separation of touching particles.
Figure 7. Sequence of char wall-thickness measurement.
Figure 8. Logic tree for automated char image analysis.
the different shape factors, and Figure 5b shows how close function works with a char using shape factors 6 and 7. From the comparison of results, structural shape 6 provides a better approach to the original image, preserving the original size and the secondary porosity seen in the char walls. An algorithm to rebuild an individual char image from a group of fragmented char boundaries has been developed using close function. The first step of the algorithm is to apply the close function on the improved binary image to combine possible parts/fragments and find the major voids for all those possible chars in the image. Then those voids are used to remove irrelevant fragments from the integral char particle on the basis of a series of measurements and judgments. The char image found so far is still in need of further investigation to see whether it consists of touching particles.
Separating Touching Particles. As explained in the Introduction, the image analysis system must separate touching particles in an image. The separation of touching particles is a well-established process in image processing and is wellunderstood in the literature. However, the particles discussed in the literature tend to be relatively predictable and often spherical.18 Chars are much more difficult to separate because their structures are highly variable and are generally nonspherical (18) Russ J. C. The image processing handbook, 4th ed.; Boca Raton, FL, 2002; p 732. (19) Cloke, M.; Lester, E.; Allen, M.; Miles, N. J. Repeatability of maceral analysis using image analysis systems. Fuel 1995, 74, 654658. (20) Petrographic analysis of bituminous coal and anthracitesPart 3: Method of determining maceral group composition of bituminous coal and anthracite. British Standard BS 6127-3, 1995.
Char Image Analysis Techniques
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(despite the use of the terms tenuisphere and crassisphere). There are two image analysis functions that can be realistically used to separate touching char particles. The first approach is an erosion-dilation method until the ultimate borderline between the objects is detected, called the “separate” function. Another approach is using a combination of distmapeuclid, normalize, and binexoskeleton functions to deagglomerate objects. The distmapeuclid function generates a Euclidean distance-transformed image from a binary image, in which bright pixels indicate an increasing distance from the background. The normalize function creates an 8 bit (0-255) artificial gray scale spreading a range of gray values over the input image to generate a new output image. The resultant image will have an enhanced contrast between the char walls and the background. The binexoskeleton function examines the background in the input binary image, determines the skeleton of the zone of influence, and then stores the skeleton of individual objects as a binary
image in the output image. Consequently, the touching particles are separated. Figure 6 shows the effect of the separating operation using the three functions. Char Wall Thickness. The center of gravity of the char particle is used as the reference point for wall-thickness measurement. After this has been established, wall thickness is measured by drawing a series of chords from the center of gravity. The overlap of the chords with the char walls identifies the locations of the char walls and enables a series of thickness measurements to be made. In this study, 72 chords were drawn for each particle. While detailed measurements are possible, there will also be a direct increase in processing time. Parts a-c of Figure 7 show how char wall thickness is measured using this chord method. Figure 7a is the original color image for a crassisphere char particle, and Figure 7b is the enhanced corresponding binary image, while Figure 7c is an image after filling all voids inside the char walls, which is used in de-
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Figure 9. (a) Frequency of crassi-char against particle count. (b) Frequency of tenui-char types against particle count.
termining whether this char is a tenui- or crassi-particle. Secondary pores are excluded during char wall thickness measurements. In this study, the area of the third largest void will be used as a threshold to fill voids on the char boundary. All voids with area less than that for a spherical particle and less than half of that for a network particle were filled before any further measurements were taken. The distinction between crassi- (or thick-walled) or tenuiwalled (or thin-walled) chars was taken as 3 µm;9 i.e., if 50% of the chords were found to be greater than 3 µm, then the char was designated to be a crassi-walled char. Char Porosity and Char Type. Char porosity is determined by dividing the char void area by the whole char area. The char void area is the difference between the char area filled (i.e., char walls and all voids) and the char area unfilled (i.e., char walls only). Each char type, based on Figure 3, has a porosity range. The porosity values for any given char can therefore act as an initial means of classification. The porosity of each char type is as follows:9
tenuispheres, >80%; crassispheres, >40%; tenuinetworks, >70%; crassinetworks, >40%; mixed porous, >60%; mixed dense, 40-60%; inertoids, 5-40%; solids/dense chars,
