Abstract
Anode spike crises have a deleterious effect on current efficiency to the point of jeopardizing smelter operation. All the time spent with a spike under an anode is a period where current is lost, and the longer the spike remains present, the more serious are the mid-term consequences for the reduction process. It is therefore most important to detect these spiky anodes as early as possible and remove them from the pots. A new tool based on anode current measurements, combined with machine learning, has been developed and tested. It is an effective way of detecting many of these spikes, usually a few days before they become obvious. This article describes the development of the tool and the first results obtained on industrial cells.
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Jeffrey Keniry and Eugene Shaidulin, Anode signal analysis – The next generation in reduction cell control, Light Metals 2008, TMS 2008
Walter Zucchini and Iain L. MacDonald, Hidden Markov Models for time series: an introduction using R, Chapman and Hall/CRC (2009), ISBN 9781420010893
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© 2018 The Minerals, Metals & Materials Society
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Martel, A. (2018). Spike Detection Using Advanced Analytics and Data Analysis. In: Martin, O. (eds) Light Metals 2018. TMS 2018. The Minerals, Metals & Materials Series. Springer, Cham. https://doi.org/10.1007/978-3-319-72284-9_64
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DOI: https://doi.org/10.1007/978-3-319-72284-9_64
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Publisher Name: Springer, Cham
Print ISBN: 978-3-319-72283-2
Online ISBN: 978-3-319-72284-9
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