Abstract
The MONSOON project is a European H2020 innovation project dedicated to optimize process industry through resources and energy efficiency . The consortium is composed of 11 partners from 7 European countries. Rio Tinto and Aluminium Dunkerque (AD) are among the industrial partners, while ProbaYes acts as Data Science experts. The MONSOON project has built a two-components platform dedicated to both development and deployment of data analytics functions, employed for AD’s Paste Plant process optimization . The carbon anodes are a key component to the electrolysis reaction. The quality of the anodes (density, composition…) directly impacts the quantity and quality of the produced aluminum . A method, based on machine learning techniques, has been developed for monitoring the quality of the produced anodes and understanding the root causes of non-quality, using real-time Paste Plant data. This article presents the approach proposed in this context, the designed tools, and the first results obtained so far.
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© 2019 The Minerals, Metals & Materials Society
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Azennoud, B., Bernard, A., Bonnivard, V., Pedroli, H. (2019). Anode Quality Monitoring Using Advanced Data Analytics. In: Chesonis, C. (eds) Light Metals 2019. The Minerals, Metals & Materials Series. Springer, Cham. https://doi.org/10.1007/978-3-030-05864-7_152
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DOI: https://doi.org/10.1007/978-3-030-05864-7_152
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