Neural Networks and Support Vector Machine Models Applied to Energy Consumption Optimization in Semiautogeneous Grinding

Abstract

Semiautogenous (SAG) mills for ore grinding are large energy consumption equipments. The SAG energy consumption is strongly related to the fill level of the mill. Hence, on-line information of the mill fill level is a relevant state variable to monitor and drive in SAG operations. Unfortunately, due to the prevailing conditions in a SAG mill, it is difficult to measure and represent from first principle model the state of the mill fill level. Alternative approaches to tackle this problem consist in designing appropriate datadriven models, such as Neural Networks (NN) and Support Vector Machine (SVM). In this paper, NN and a SVM (specifically a Least Square-SVM) are used as Nonlinear autoregressive with exogenous inputs (NARX) and Nonlinear autoregressive moving average with exogenous inputs (NARMAX) models for on-line estimation of the filling level of a SAG mill. Good performances of the developed models could allow implementation in SAG operation/control hence optimizing its energy consumption.