Application of imaging spectroscopy to multivariate ore sorting technologies

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We propose to improve on present-day ore sorting technologies using hyperspectral imaging spectroscopy in the visible to shortwave infrared regions of light. It has been estimated that that more than 40% of the total energy consumption at a mine site is used in crushing and milling of rock, which in a heterogeneous deposit includes both desirable ore and undesirable waste rock. The ability to quickly and objectively discern changes in both grade and rock mineralogy throughout the mining process would allow for greater optimization of mine operations by, for example, separating oxide and sulphide ores into different processing streams. MineSense Technologies Ltd. is a B.C.-based mining technology company specializing in multivariate telemetry of ore at all stages of mining. Their clients are mining companies that want to optimize their operations through pre-concentration and sorting of mined ore rocks as well as the rejection of waste rock. Hyperspectral imaging spectroscopy is a technique that would complement and integrate well with the company's existing sensors. However, due to the complexity of hyperspectral data and the harsh environmental conditions, decision-making at a speed relevant to a mining operation is particularly challenging. Accordingly, the application of hyperspectral remote sensing in the mining and milling environment is at the cutting edge of science and engineering and would benefit greatly from academic-industry collaboration. The main objectives of the project are: (1) to take a specific ore deposit case example and explore which spectral regions are most applicable to enhancing ore sorting capabilities, and (2) to develop the scientific and technical basis for integrating imaging spectroscopy into real time multivariate ore sorting. Results of the study could provide significant advantages for the Canadian mining industry.