Abstract
Anodic incidents occur when an aluminum electrolysis cell short circuits at a specific anode position as a result of a spike or other deformation forming on the bottom surface of the anode. These undesirable process events reduce the current efficiency of the affected cell, which makes early detection critical for minimizing losses. This paper describes the development of an anodic incident detection system based on monitoring individual anode current signals with a multivariate Principal Component Analysis (PCA) model. The model and monitoring system were developed and tested using 2422 individual anode current signals collected from an aluminum smelter. The results indicate that anode current signals may be useful for detecting anodic incidents in advance relative to regular detection methods based on typical process signals. Anodic incidents appear to alter the correlation structure of individual anode current signals, which could make detection possible by monitoring the Squared Prediction Error (SPE) statistic.
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LaJambe, D., Poulin, É., Duchesne, C., Tessier, J. (2020). Anodic Incident Detection through Multivariate Analysis of Individual Anode Current Signals. In: Tomsett, A. (eds) Light Metals 2020. The Minerals, Metals & Materials Series. Springer, Cham. https://doi.org/10.1007/978-3-030-36408-3_74
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DOI: https://doi.org/10.1007/978-3-030-36408-3_74
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