Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций
Конференция: 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 ofПоказать полностьюmaximum 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.
Журнал: CEUR Workshop Proceedings
Выпуск журнала: Vol. 2727
Номера страниц: 99-107
ISSN журнала: 16130073
Издатель: CEUR-WS