Development of a protocol to determine the sorting potential of particulate ore material

The objective of this research was to develop a protocol/ methodology to determine the potential for an ore to be sorted using sensor-based sorting. The research builds upon previous methodologies in literature to determine ore sortability. The first attempt to create a standard methodology to assess the amenability of an ore to sorting at a pilot-scale was developed by Fitzpatrick (2008). Tong (2012) developed a methodology to assess the amenability of an ore to sensor-based sorting on an ideal laboratory-scale. These methodologies focus on determining the upgrading potential of an ore based on ore sorting amenability tests. In order to gain further acceptance of sorting technology in the mining industry, Lessard et al. (2015) developed a method to determine the impact of ore sorting on an operation from an economic perspective. The protocol, developed during the current research, is used to determine the potential ore sortability based, firstly, on intrinsic particle properties and, secondly, based on laboratory-scale sensor sortability tests using ideal and industrial sensor measurement parameters. The intrinsic sortability results represent the ideal/ best- case sortability if a perfect separator existed and are calculated based on particle-by-particle ore characterisation. Ore that is intrinsically sortable is further assessed based on ideal laboratory-scale sensor sort ability tests using selected sensors. Ore sorting sensors that show potential based on the ideal sensor tests are further assessed by determining the sort ability of the ore using sensor measurement parameters similar to those used on industrial-scale ore sorting machines

Grantor dc:publisher.institution

Department of Chemical Engineering

Year dc:date.issued

2016

Author dc:creator

• Duncan, Michael Graeme

Advisor dc:contributor.advisor

• Deglon, David A

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/11427/20327

OAI identifier oai:identifier

oai:open.uct.ac.za:11427/20327