A Methodology to Determine the Potential for Particulate Ore Sorting Based on Intrinsic Particle Properties
| dc.contributor.author | Duncan, Michael | |
| dc.contributor.author | Deglon, David | |
| dc.date.accessioned | 2022-07-21T11:52:31Z | |
| dc.date.available | 2022-07-21T11:52:31Z | |
| dc.date.issued | 2022-05-16 | |
| dc.date.updated | 2022-05-27T13:36:59Z | |
| dc.description.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 determining 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. | en_US |
| dc.identifier | doi: 10.3390/min12050630 | |
| dc.identifier.apacitation | Duncan, M., & Deglon, D. (2022). A Methodology to Determine the Potential for Particulate Ore Sorting Based on Intrinsic Particle Properties. <i>Minerals</i>, 12(5), 630. http://hdl.handle.net/11427/36656 | en_ZA |
| dc.identifier.chicagocitation | Duncan, Michael, and David Deglon "A Methodology to Determine the Potential for Particulate Ore Sorting Based on Intrinsic Particle Properties." <i>Minerals</i> 12, 5. (2022): 630. http://hdl.handle.net/11427/36656 | en_ZA |
| dc.identifier.citation | Duncan, M. & Deglon, D. 2022. A Methodology to Determine the Potential for Particulate Ore Sorting Based on Intrinsic Particle Properties. <i>Minerals.</i> 12(5):630. http://hdl.handle.net/11427/36656 | en_ZA |
| dc.identifier.ris | TY - Journal Article AU - Duncan, Michael AU - Deglon, David AB - 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 determining 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. DA - 2022-05-16 DB - OpenUCT DP - University of Cape Town IS - 5 J1 - Minerals LK - https://open.uct.ac.za PY - 2022 T1 - A Methodology to Determine the Potential for Particulate Ore Sorting Based on Intrinsic Particle Properties TI - A Methodology to Determine the Potential for Particulate Ore Sorting Based on Intrinsic Particle Properties UR - http://hdl.handle.net/11427/36656 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/36656 | |
| dc.identifier.vancouvercitation | Duncan M, Deglon D. A Methodology to Determine the Potential for Particulate Ore Sorting Based on Intrinsic Particle Properties. Minerals. 2022;12(5):630. http://hdl.handle.net/11427/36656. | en_ZA |
| dc.language.iso | en | en_US |
| dc.publisher.department | Department of Chemical Engineering | en_US |
| dc.publisher.faculty | Faculty of Engineering and the Built Environment | en_US |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en_US |
| dc.source | Minerals | en_US |
| dc.source.journalissue | 5 | en_US |
| dc.source.journalvolume | 12 | en_US |
| dc.source.pagination | 630 | en_US |
| dc.source.uri | https://www.mdpi.com/journal/minerals | |
| dc.title | A Methodology to Determine the Potential for Particulate Ore Sorting Based on Intrinsic Particle Properties | en_US |
| dc.type | Journal Article | en_US |