| Summary: | With sensor-based ore sorting attracting more attention among the industry leaders, and in an effort to show the potential for sensor-based ore sorting technology, this research takes a particle sorting approach and looks at sorting low-grade and waste rock stockpiles to concentrate the misplaced mineralized rocks and generate value.
The results from the optical sensor showed that where there was a visual distinction between the mineralized and gangue material, this sensor managed to identify each group well.
Despite using a multivariate linear regression (MLR) analysis, the electromagnetic sensor did not predict the grades effectively.
The X-Ray Transmission (XRT) sensor performed quite well for both base metal and gold samples. One recurring problem was the presence of iron minerals such as pyrite that, due to their relatively high atomic density, tarnished the sorting results.
With elemental distinguishing capabilities, the X-Ray Fluorescence (XRF) sensor boasts great potential for ore sorting. Both single and multivariate linear regression analysis were used to analyse the results from the XRF sensor. Although, while overall satisfactory results were obtained from the XRF sensor, sensor capabilities in actual dynamic sorting cases need to be assessed.
Recommendations for future work can be on different aspects of this work. One would be to try to improve the static, bench-top testing facilities so they represent dynamic sorting scenarios better, such as use of a conveyor-type platform where rocks can pass under a sensor. If a similar study is to be performed, it is highly suggested to focus the efforts on one mine, one size fraction (preferably -50 mm +37.5 mm) with a larger number of particles.
In terms of continuation of this work, it would be best to take these tests to the next level and perform bulk sorting tests to determine how these bench-scale tests correlate with bulk dynamic sorting results. Also, a detailed economic analysis based on these results would yield valuable results. === Applied Science, Faculty of === Mining Engineering, Keevil Institute of === Graduate
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