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Towards Application of Machine Learning Methods in Pyrometallurgy: A Case Study of an Exploratory Data Analysis for Ferromanganese Production

  • 4IR in Extractive Metallurgy
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Abstract

The management of ferromanganese production requires the monitoring and analysis of a large amount of data. This case study explores how much information and value can be extracted from the process monitoring data using data-driven methods. The study describes data preparation, cleaning and the application of unsupervised learning methods to eventually summarize over 140 monitored variables in just two statistically independent time series. Although this analysis helped to condense the information to humanly consumable form, to further extract the value from this information requires the input of a specialist with the domain-specific knowledge and experience. As a result, it is suggested to use machine learning methods outputs as virtual advisors to aid decision-making done by domain experts.

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Notes

  1. Sadly, there is no published paper that describes the algorithm.

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Acknowledgements

The financial assistance of the National Research Foundation (NRF) towards this research is hereby acknowledged. Opinions expressed and conclusions arrived at are those of the author and are not necessarily to be attributed to the NRF.

This paper is published by permission of Mintek and Transalloys.

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  1. MINTEK, Johannesburg, 2125, South Africa

    A. V. Cherkaev, Q. G. Reynolds & J. D. Steenkamp

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  1. A. V. Cherkaev
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  2. Q. G. Reynolds
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Correspondence to A. V. Cherkaev.

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Cherkaev, A.V., Reynolds, Q.G. & Steenkamp, J.D. Towards Application of Machine Learning Methods in Pyrometallurgy: A Case Study of an Exploratory Data Analysis for Ferromanganese Production. JOM 74, 47–52 (2022). https://doi.org/10.1007/s11837-021-05023-z

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