DETERMINATION OF DEGREE OF CARBONIZATION IN COKES BY IMAGE ANALYSIS. Abstract. Background

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1 DETERMINATION OF DEGREE OF CARBONIZATION IN COKES BY IMAGE ANALYSIS Stein Rørvik, SINTEF Materials and Chemistry, Sem Sælands vei 12, N-7465 Trondheim, Norway Arne Petter Ratvik, SINTEF Materials and Chemistry, Sem Sælands vei 12, N-7465 Trondheim, Norway Trygve Foosnæs, Norwegian University of Science and Technology, Department of Chemistry, N-7491 Trondheim, Norway Abstract A method to determine the degree of carbonization of coke has been developed. The method is based on automatized optical microscopy and image analysis. The degree of carbonization is determined by examining the interference colors in reflected cross polarized light. Optically anisotropic areas have different colors than the isotropic areas. The degree of carbonization is given as the fraction of optical anisotropic area to the total coke area, excluding porosity. The microscope is equipped with automatic stage movement and auto-focus. Several hundred images at random positions are automatically acquired and analyzed. The results are compared to reactivity data for different cokes and their XRD properties. Background Traditionally, cokes for metallurgical use are characterized using reflectance ranking. The reflectance (percent of light at a specified wavelength returning from a polished sample surface) is measured on the vitrinite component of the coke. This reflectance rank increases with increasing maturation / coalification degree of the coke. Other related properties are volatile content (decreasing with increasing rank), carbon content (increasing with increasing rank), calorific value (increasing with increasing rank) and crystallite size (increasing with increasing rank). In the scope of this paper, the cokes characterized were for manganese and silicon production. Due to our research institute's long experience with using optical microscopy and computerized image analysis, it was desirable to apply the developed techniques on metallurgical cokes. Eilertsen et.al. [1] published a method for characterizing cokes by image analysis. This method was developed further to include automatized analysis of a high number of coke grains by Rørvik et.al. [3]. These techniques were developed for petroleum cokes. Petroleum cokes are fully carbonized (by volume), and the published techniques characterized the cokes by measuring the size and texture of the optical domains. When applying this technique on metallurgical cokes, the method was not able to rank the different cokes well. All cokes came out with a similar value representing the size of the (quite small) optical domains in metallurgical cokes. Metallurgical cokes usually have a fraction of the volume without any particular optical texture. The amount of volume with optical texture in the coke can range from almost nothing to 100 %. It was evident that a better way of classifying the metallurgical cokes would be to quantify the relative amount of optically active volumes rather than the size of the optical domains. Method Optical Principles In a metallurgical microscope, the light from a halogen lamp is first plane polarized, and sent through the optics in the microscope onto the coke sample surface. On the coke surface, the light is reflected and the polarization is changed according to the graphite layers' angle at the reflecting point. The reflected light passes a second polarizing filter, which is crossed at 90º relative to the first filter. A half-wave retarder-plate shifts the phase of the light one half wavelength. This causes interference in the visible wavelength range. The interference color depends on the angle of the graphite layers of the coke texture. Basically, the layers having a east-west direction are magenta, SW-NE are yellow and SE-NW are cyan. The layers that are perpendicular to the observation direction will appear with a purple color. Optically isotropic areas will also appear with a purple color. Observations have shown that the purple parts of the developed texture areas have a higher brightness of purple than the optically isotropic areas have. Therefore, the brightness as well as the color should be used for characterizing the images. Figure 1 shows a typical image of a petroleum coke. The coke grain is embedded in epoxy (green). The entire coke grain is crystallized. The carbon layers in the purple areas in the upper right part of the grain are crystalline, but perpendicular to the viewing direction (parallel to the sample surface) and therefore not causing any optical interference. Figure 2 shows a typical image of a metallurgical coke photographed under the same conditions. The purple color here represents carbon with low crystallinity. The upper left part of the grain is fully crystallized, while the lower right part is only crystallized along the edges of the pores.

The stage movement and focus is controlled directly by the computer image analysis software.

2 Figure 1. Petroleum coke in polarized light. Figure 2. Metallurgic coke in polarized light. Figure 3. Microscope hardware, schematic. Microscopy Hardware The samples are examined using a standard inverted reflected light metallurgical microscope (Leica MeF3A), equipped with a motorized XY- stage and focus controller. The stage movement and focus is controlled directly by the computer image analysis software. Digital images are acquired using an electronic 3-chip CCD 1 video camera (Sony DCX 950P) with analog RGB output and a frame-grabber card. Newer 1-chip digital cameras provide higher resolution than this camera, but they do not provide the same color accuracy as the 3-chip camera. Color accuracy is very important in the present technique since the degree of carbonization of the coke is determined by the colors appearing in polarized light. Figure 3 illustrates schematically the current microscope setup. Microscopy Software The computer software used is a modified version of the image analysis program NIH Image version The Macintosh version of NIH Image software is in the public domain, and was written by Wayne Rasband at the U.S. National Institutes of Health. The basic program with source code is available electronically via Internet from The source code was modified to support the specific hardware used, and various additional image operations were added. The NIH image software has a Pascal-like macro language, which was used to control the analysis. A proper macro programming language is essential for this kind of work. 1 CCD is abbreviation for Charge Coupled Device. These cameras use a chip with an array of sensors that accumulates an electrical charge proportional to the amount of light exposed onto them.

3 Image Analysis, Determination of Carbonized Area The main difference between the present work and the ones published earlier [1, 3] is the way the information is extracted from the images. The sample preparation, image acquisition and statistical post-processing procedures are similar to the procedures described earlier [3]. The steps in the image analysis are as follows: 1. A color image of the coke grain is captured (Figure 4). 2. The blue channel is extracted from the color image (Figure 5). This is the basis for determination of the epoxy / pore areas. The contrast between the epoxy and carbon is highest in this channel. 3. The green channel is extracted from the color image (Figure 6). This is the basis for determination of the carbonized areas. The contrast between the carbonized areas and the isotropic areas is highest in this channel. 4. The pores are thresholded from the blue channel image according to a pre-determined limit (Figure 7). 5. The carbonized areas are thresholded from the green channel image according to a pre-determined limit (Figure 8). 6. A composite image is created for visualization of the result, where the pores are green, the crystallized carbon is red and the isotropic carbon is white (Figure 9). 7. The carbonized fraction for each image is determined by the ratio crystallized area / (crystallized area + isotropic area). Image Analysis, Workflow of Analysis for a Coke Sample The workflow in the analysis is identical to the automatized method published earlier [3]. It is repeated here for easy reference: 1. Coke grains screened to a narrow fraction size of mm are embedded in epoxy under vacuum. This fraction size was chosen because one microscope image at 250x will fit conveniently inside each grain. After the epoxy has cured, the sample is cut, ground and polished. 2. A microscope overview image at low magnification (20x) is captured by merging 5 x 6 adjacent images. Each image covers a physical area of 4.6 x 3.4 mm, giving a total overview sample area of 23 x 20 mm. Acquiring this overview image is automatized and takes just a few minutes. 3. The overview image (which typically contains intersected grains) is processed to find the size and position of each grain. A given number of grains, meeting certain size criteria, are randomly selected by the computer. For practical reasons, a square number of grains is used. An appropriate number has shown to be 256 grains, and was used for the present analysis. About 100 grains is the minimum to get a satisfactory reproducibility. 4. The operator changes the microscope magnification to 250x, and starts the automatic collection of image data. The computer moves the sample to each physical grain position (as calculated from the relative positions in the overview image), requests an auto-focus, and captures a color image to disk. Each image covers a 380 x 260 µm physical area. 5. The images are read by the computer and analyzed in batch. From each color image, the carbonized fraction is extracted as described earlier in this paper. Each image (768 x 576 pixels) is divided into 6 x 4 = 24 sub-frames of 128 x 128 pixels each that are measured. A mosaic index and a fiber index is calculated for each of these 24 frames. 5. The results are post-processed in Microsoft Excel. The statistical processing is straightforward, and done by pasting the results into a template worksheet. First, average values of the 24 measurement tiles in each image are calculated. Then, average values are calculated for all images. Distribution plots are made of these values. The reproducibility is shown by a plot of how the average values converge as a function of number of frames analyzed. Results Figure 10 through Figure 15 shows example result images from some analyzed cokes. Figure 10 shows a color mosaic image of all frames analyzed from a blend coke. The composite image in Figure 11 shows that this coke has a large variation in degree of crystallization from image to image (mix of red and white areas); which is expected as this is a blend of different cokes from different sources. Figure 12 shows a color mosaic image of all frames analyzed from a anisotropic single source coke. The composite image in Figure 13 shows that the degree of crystallization is high for this coke (mostly red areas). Figure 14 shows a color mosaic image of all frames analyzed from an isotropic single source coke. The composite image in Figure 15 shows that the degree of crystallization is low for this coke (mostly white areas). A lower number of images were analyzed for the blend coke, but this is not relevant for the discussion here.

4 Figure 4. Coke, RGB colors. Figure 7. Coke, thresholded pores (black). Figure 5. Coke, blue color channel. Figure 8. Coke, thresholded crystallized carbon (black). Figure 6. Coke, green color channel. Figure 9. Coke, composite image with pores (green), crystallized carbon (red) and isotropic carbon (white).

5 Figure 10. Coke, BBC (blend), color image. Figure 11. Coke, BBC (blend), carbonized area (red). Figure 12. Coke, PD (single source), color image. Figure 13. Coke, PD (single src.), carbonized area (red). Figure 14. Coke, BG (single source), color image. Figure 15. Coke, BG (single src.), carbonized area (red).

$Figure 16. Average carbonized fraction for all cokes. Figure 17. Lc value for all cokes. Figure 18. Reactivity vs. carbonized fraction for all cokes. Figure 19. Reactivity vs. alkali index for all cokes.$ $Figure 16 shows the average carbonized fraction for 6 different cokes. Figure 17 shows the L C value (crystallite height, determined by X-ray diffraction) for all cokes.$

6 Figure 16. Average carbonized fraction for all cokes. Figure 17. Lc value for all cokes. Figure 18. Reactivity vs. carbonized fraction for all cokes. Figure 19. Reactivity vs. alkali index for all cokes. Figure 16 through Figure 19 are all taken from Kaczorowski [2]. These are the same cokes as the ones used for the figures above, and is named using the same codes. Figure 16 shows the average carbonized fraction for 6 different cokes. Figure 17 shows the L C value (crystallite height, determined by X-ray diffraction) for all cokes. The ranking is similar by both methods. For the single source cokes, coke "PD" is more crystalline than "ST" and "BG". For the blend cokes, "RBC" is less crystalline than "PBC" and "BBC". It is interesting to observe that the ranking is similar for the two different methods, as the image analysis method operates on a micrometer scale while the X-ray method operates at a nanometer scale. Figure 18 shows the Boudouard reactivity versus carbonized fraction for all cokes. The reactivity is measured as degree of conversion pr time unit (dx/dt). The results do not correlate well. However, Kaczorowski found in his work that the alkali content was more important for reactivity than the coke structure (also as determined by other methods) for these cokes. Figure 19 shows the Boudouard reactivity versus alkali index for all cokes. The alkali index is determined by the ratio of the basic to other oxides in the coke ash. There is a good correlation between alkali index and reactivity. The lack of correlation to the image analysis results does not necessarily mean that the image analysis method is useless, just that other factors may be more important for reactivity. References [1] Jan L. Eilertsen, Stein Rørvik, Trygve Foosnæs, Harald A. Øye: "An Automatic Image Analysis of Coke Texture", Carbon, Volume 34, Issue 3, 1996, p [2] Jakub Kaczorowski: "The Boudouard Reaction in Manganese Production", Doctoral thesis 2006:224, Norwegian University of Technology and Science, ISBN [3] Stein Rørvik, Marianne Aanvik, Morten Sørlie, Harald A. Øye: "Characterization of Optical Texture in Cokes by Image Analysis", The Minerals, Metals and Materials Society (TMS), Light Metals Proceedings 2000.

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