Authors: Nadolski, S; Klein, B; Hart, CJR; Moss, A; Elmo, D

Open access courtesy of:

Cite As:
Nadolski, S, Klein, B, Hart, CJR, Moss, A & Elmo, D 2018, 'An approach to evaluating block and panel cave projects for sensor-based sorting applications', in Y Potvin & J Jakubec (eds), Proceedings of the Fourth International Symposium on Block and Sublevel Caving, Australian Centre for Geomechanics, Perth, pp. 133-140.

Download citation as:   ris   bibtex   endnote   text   Zotero

Advances in sensor-based sorting technologies in mineral applications have resulted in increased interest in the implementation of sorting systems at mining operations. Ore sorting holds significant potential to improve the productivity at block and panel caving operations, where the lack of selectivity and potential for dilution entry associated with the cave mining methods results in many operations mining and processing material that is below cutoff grade at certain stages of production. A sensor-based ore-sorting study, incorporating bulk and particle sorting systems, was carried out using material from the New Afton block cave operation. Results from the study were used to develop a method for evaluating future lifts where sensor-based sorting systems are in place. A case study is presented for a conceptual cave where both bulk and particle sorting systems are implemented. The method provides a means to nominate a cave footprint and elevation that maximises project value in the case where sensor-based sorting systems are installed. Through use of production scheduling software, the proposed method can be used to determine the economic footprint, reduction in milling requirements and associated metal recovery for a cave. Keywords: block caving, sensor-based sorting, PCBC, cave-to-mill, cave evaluation


Coghill, P, Cutmore, N, Lehmann-Horn, J, Lovric, B, McEwen, A, Milinkovic, D, Miljak, D, Roberts, G & Yong, R 2018, ‘Demonstration of a magnetic resonance analyser for bulk copper sorting’, in T Pretz, H Wotruba & A Feil (eds), Proceedings of the Eighth Conference on Sensor-Based Sorting & Control, RWTH Aachen University, Aachen, pp. 152–160.
Dassault Systèmes 2018, GEOVIA PCBC, version 6.8.1, computer software, Dassault Systèmes, Paris,
Diering, T, Richter, O & Villa, D 2010, ‘Block cave production scheduling using PCBC’, Proceedings of the SME Annual Meeting, Society for Mining, Metallurgy & Exploration, Englewood.
Erdenebat, E 2017, Study of New Afton Ore Heterogeneity and its Amenability to Sensor Based Ore Sorting, MASc thesis, The University of British Columbia, Vancouver.
Klein, B & Bamber, AS 2018, chapter on mineral sorting, in R Dunne, C Young & SK Kawatra (eds), SME Mineral Processing and Extractive Metallurgy Handbook, Society for Mining, Metallurgy & Exploration, Englewood, in press.
Kobzev, A 2014, ‘History of sensor-based sorting in CIS’, in T Pretz, H Wotruba & A Feil (eds), Proceedings of the Sixth Conference on Sensor-based Sorting, RWTH Aachen University, Aachen, pp. 39–48.
Nadolski, S, Samuels, M, Klein, B & Hart, CJR 2018, ‘Evaluation of bulk and particle sensor-based sorting systems for the New Afton block caving operation’, Minerals Engineering, vol. 121, pp. 169–179.
Teck Resources 2018, Building a Smarter Shovel, Teck Resources, Vancouver, viewed 5 May 2018,

© Copyright 2019, Australian Centre for Geomechanics (ACG), The University of Western Australia. All rights reserved.
Please direct any queries to or error reports to