Artificial Intelligence as Analysis Tool of the Circuit Behavior of Mineral Processing Plants

Abstract

Production key figures of mineral processing plants, often designed as circuits with recirculation of material, are subject to a high number of influencing factors. In order to set up plant operation in an optimal way, identifying factors with high significance is important. In this study, an artificial neural network is employed as an additional tool for such processing plant audits by means of feature importance analysis. The presented method is applicable independently of the specific plant design, wherever sufficient process data is available. Furthermore, specific outcomes of the analysis of an exemplary potash compaction circuit are discussed.