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

Abstract

Abstract 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”. This 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.