Prediction of technological process parameters in aluminum production based on artificial neural networks


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

Конференция: International Scientific Conference on Applied Physics, Information Technologies and Engineering, APITECH 2020

Год издания: 2020

Идентификатор DOI: 10.1088/1742-6596/1679/3/032092

Аннотация: The paper presents the theoretical aspects to construct a decision support system for assessing the technical condition and efficiency of the technological process in aluminum production. The key parameters that characterize the technological state of the electrolysis cells include the cell voltage, amperage of anodes and "noise". Показать полностьюThis study is aimed at methods of machine learning as it relates to forecasting these parameters. Forecasting relies on recurrent neural networks. The method of maximum accuracy was used to elicit the neural network architecture. The results obtained in the tests run on the suggested model of forecasting are deemed acceptable in terms of predicting the technological process indicators. The introduction of the decision support system into the production management process will allow us to timely detect the technological disruptions and take measures to prevent their occurrence, which in turn will increase the efficiency of aluminum production. © Published under licence by IOP Publishing Ltd.

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Журнал: Journal of Physics: Conference Series

Выпуск журнала: Vol. 1679, Is. 3

Номера страниц: 32092

ISSN журнала: 17426588

Издатель: IOP Publishing Ltd


  • Mikhalev A.S. (Siberian Federal University, 79 Svobodny pr., Krasnoyarsk, 660041, Russian Federation)
  • Lugovaya N.M. (Siberian Federal University, 79 Svobodny pr., Krasnoyarsk, 660041, Russian Federation)
  • Penkova T.G. (Institute of Computational Modelling SB RAS, 50/44 Akademgorodok, Krasnoyarsk, 660036, Russian Federation)

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