Forecasting silicon ore concentrate yield using machine learning methods

The article effectively applies machine learning methods to predict the production of silicon ore concentrate. The problem of silica content control is a problem for the mining industry, since the quality of the final product and its cost depend on it [9; 10]. During the study, the data obtained from the flotation plant after their preliminary processing were used to identify the most dynamically changing factors (flotation indicators). Random forest and recurrent convolutional neural network LSTM models were trained with different sets of input features. The quality of the models used was assessed using the mean square error (MSE), mean absolute error (MAE) and determination coefficient (R-squared) metrics. As a result of the experiments, it was found that instant flotation indicators have a lesser effect on improving the quality of the forecast, and unique variables taken with different lags lead to an increase in accuracy. The results of the study can be used at enterprises engaged in the processing of silicon ore for more complete automation and optimization of flotation control processes.