Abstract
A machine vision sensor was developed for predicting deviations from the optimum amount of pitch in anode formulations using paste texture analysis. It could help operators mitigate the impact of the increasing variability of anode raw materials (coke and pitch). Paste samples were formulated in the laboratory using dry aggregate mixes obtained using two cokes having different properties and various amounts of pitch. These were imaged, formed into small cylindrical anodes, and baked to measure their density. A combination of image texture methods was used for extracting relevant paste textural features. The latter were then used as inputs of partial least squares regression models to predict deviations from the maximum baked density. Good prediction results were obtained. Furthermore, the sensor was able to detect when the paste was at the optimal amount of pitch for both cokes and to measure deviations from it.
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