A Methodology to Determine the Potential for Particulate Ore Sorting Based on Intrinsic Particle Properties

Abstract

Sensor-based particulate ore sorting is a pre-concentration technique that sorts particles based on measurable physical properties, resulting in reduced energy consumption by removing waste prior to grinding. This study presents an integrated methodology to determine the potential for ore sorting based on intrinsic particle properties. The methodology first considers the intrinsic sortability based on perfect separation. Only intrinsically sortable ore is further assessed by determin-ing the sensor-based sortability. The methodology is demonstrated using a case study based on a typical copper porphyry comminution circuit. The sorting duty identified for the case study was the removal of low-grade waste material from the pebble crusher stream at a suitable Cu cut-off grade. It was found that the ore had the potential to be sorted based on the intrinsic and ideal laboratory sensor sortability results but showed no potential to be sorted using industrial-scale sensors. The ideal laboratory XRF sensor results showed that around 40% of mass could be rejected as waste at copper recoveries above 80%. An economic analysis of the sortability tests showed that, at optimum separation conditions, the intrinsic, ideal sensor and industrial sensor sortability would result in an additional annual profit of ~$30 million, $21 million and $−7 million (loss), respectively.