A Machine Vision Sensor for Quality Control of Green Anode Paste Material
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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.
The authors acknowledge financial support of the Natural Sciences and Engineering Research Council of Canada (NSERC) [Grant Numbers RGPIN 261188-2013 and RDCPJ 417576-11], Fonds de Recherche du Québec - Nature et Technologies (FRQNT) through the Aluminium Research Centre – REGAL, and Alcoa Corporation.
Conflict of interest
The authors declare no conflict of interest.
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