Predicting Particle Size of Copper Ore Grinding With Stochastic Configuration Networks

This article presents a case study on predicting the particle size of copper ore grinding with stochastic configuration networks (SCNs). A set of temporal data was collected from the copper ore grinding process for building the predictive model. The well-known least absolute shrinkage and selection operator (LASSO) is employed for temporal feature selection. Then, an SCN model is constructed to estimate the residual between the output of the LASSO model and the product particle size. The weights of the linear model and the output weights of the SCN model are optimized simultaneously using the elastic-net regularization method to improve the prediction performance. An analysis on the selected input variables is given, which is helpful to better understanding the output results. Comparative experiments are carried out to demonstrate the effectiveness and merits of the proposed modeling techniques in terms of the model's accuracy, reliability, and interpretability.