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A Machine Vision Sensor for Quality Control of Green Anode Paste Material

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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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Acknowledgements

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.

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Authors and Affiliations

  1. Aluminium Research Centre-REGAL, Chemical Engineering Department, Université Laval, Quebec, QC, G1V 0A6, Canada

    Julien Lauzon-Gauthier & Carl Duchesne

  2. Alcoa Aluminum Center of Excellence, Deschambault, QC, G0A 1S0, Canada

    Julien Lauzon-Gauthier & Jayson Tessier

Authors
  1. Julien Lauzon-Gauthier
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  2. Carl Duchesne
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  3. Jayson Tessier
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Correspondence to Carl Duchesne.

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Lauzon-Gauthier, J., Duchesne, C. & Tessier, J. A Machine Vision Sensor for Quality Control of Green Anode Paste Material. JOM 72, 287–295 (2020). https://doi.org/10.1007/s11837-019-03893-y

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  • DOI: https://doi.org/10.1007/s11837-019-03893-y

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