Application of artificial neural networks to forecast technological process parameters in aluminum production | Научно-инновационный портал СФУ

Application of artificial neural networks to forecast technological process parameters in aluminum production

Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций

Конференция: 1st Siberian Scientific Workshop on Data Analysis Technologies with Applications, SibDATA 2020

Год издания: 2020

Ключевые слова: aluminum production, forecasting, neural network, process disruptions, technological process parameters, voltage

Аннотация: The study is aimed at methods of machine learning as it relates to forecasting technological process parameters. The forecasting tools are developed in two main stages: analysis and preprocessing of input data, elaboration of a math model and validation of the solution. Forecasting relies on recurrent neural networks. The method of maximum accuracy was used to elicit the neural network architecture, and calculate the metrics of MSE, MAPE, the coefficient of determination and Theil coefficient. The results obtained in the tests run on the suggested model of forecasting the cell voltage are deemed acceptable in terms of predicting the technological process indicators. The identified errors will ensure that preventive measures are taken in a timely manner to avoid process disruptions and increase overall efficiency of aluminum production. Copyright © 2020 for this paper by its authors.

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Издание

Журнал: CEUR Workshop Proceedings

Выпуск журнала: Vol. 2727

Номера страниц: 99-107

ISSN журнала: 16130073

Издатель: CEUR-WS

Авторы

  • Mikhalev A. (Siberian Federal University, 26, Kirenskogo str., Krasnoyarsk, 660074, Russian Federation)
  • Lugovaya N. (Siberian Federal University, 26, Kirenskogo str., Krasnoyarsk, 660074, Russian Federation)
  • Penkova T. (Institute of Computational Modelling of the Siberian Branch, Russian Academy of Sciences, 50/44 Akademgorodok, Krasnoyarsk, 660036, Russian Federation)

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