Sensor-based sorting techniques have the potential to improve ore grades while reducing waste material processing. Previous research has shown that by discarding waste prior to further processing, sensor-based sorting can reduce energy, water, and reagent consumption, as well as fine waste production. Recent studies of sensor-based sorting and the fundamental mechanisms of the main sorting techniques are evaluated in this literature review to inform optimal sensor selection. Furthermore, the fusion of data from multiple sensing techniques is being investigated in order to improve characterization of the sensed material and thus sorting capability. The key to effective sensor-based sorting implementation was discovered to be the selection of a sensing technique capable of sensing a characteristic capable of separating ore from waste with a sampling distribution sufficient for the considered sorting method. Classifications of possible sensor fusion sorting applications in mineral processing are proposed and illustrated with case studies. It was also discovered that the main impediment to implementing sensor fusion is a lack of correlative data on the response of multiple sensing techniques to the same ore sample. To provide data for the evaluation and development of sensor fusion techniques, a combined approach of experimental testing supplemented by simulations is proposed.
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Journal of Computer Science & Systems Biology received 2279 citations as per Google Scholar report
