A Review of Sensor-Based Sorting in Mineral Processing: The Potential Benefits of Sensor Fusion

Abstract

Sensor-based sorting techniques offer the potential to improve ore grades and reduce the amount of waste material processed. Previous studies show that sensor-based sorting can reduce energy, water and reagent consumption and fine waste production by discarding waste prior to further processing. In this literature review, recent investigations of sensor-based sorting and the fundamental mechanisms of the main sorting techniques are evaluated to inform optimal sensor selection. Additionally, the fusing of data from multiple sensing techniques to improve characterization of the sensed material and hence sorting capability is investigated. It was found that the key to effective implementation of sensor-based sorting is the selection of a sensing technique which can sense a characteristic capable of separating ore from waste with a sampling distribution sufficient for the considered sorting method. Classes of potential sensor fusion sorting applications in mineral processing are proposed and illustrated with example cases. It was also determined that the main holdup for implementing sensor fusion is a lack of correlatable data on the response of multiple sensing techniques for the same ore sample. A combined approach of experimental testing supplemented by simulations is proposed to provide data to enable the evaluation and development of sensor fusion techniques.

Novel fluorescence analysis for real-time moon mineralogy

“…XRF, PGNAA, and LIBS all provide elemental information, while Raman scattering provides information about chemical bonds and hyperspectral imaging information about chemical bonds and mineral species. SWAP (Size, Weight, And Power consumption) characteristics and knowledge required for each terrestrial task or off-world mission need to be considered carefully to select the ideal combination of sensors [16][17][18] .…”