Performance improvements during mineral processing using material fingerprints derived from machine learning—A conceptual framework

Abstract

Material attributes (e.g., chemical composition, mineralogy, texture) are identified as the causative source of variations in the behaviour of mineral processing. That makes them suitable to act as key characteristics to characterise and classify material. Therefore, vast quantities of collected data describing material attributes could help to forecast the behaviour of mineral processing. This paper proposes a conceptual framework that creates a data-driven link between ore and the processing behaviour through the creation of material “fingerprints”. A fingerprint is a machine learning-based classification of measured material attributes compared to the range of attributes found within the mine’s mineral reserves. The outcome of the classification acts as a label for a machine learning model and contains relevant information, which may identify the root cause of measured differences in processing behaviour. Therefore, this class label can forecast the associated behaviour of mineral processing. Furthermore, insight is given into the confidence of available data originating from different analytical techniques. Taken together, this enhances the understanding of how differences in geology impact metallurgical plant performance. Targeted measurements at low-confidence unit processes and for specific attributes would upgrade the confidence in fingerprints and capabilities to predict plant performance.