Minerals Engineering 58 (2014) 100-103 9 Contents lists available at ScienceDirect - - - - M INERALS [NGINEf.RING ·---· -·----- Minerals Engineering - ·--- -- ---- ELSEVIER journal homepage: www.elsevier.com/locate/ mineng ......... The value of automated mineralogy 4] Cross Mark Ying Gu Robert P'. Schouwstra Chris Rule a. b.*, c j/(MRC, The University of Queens land, 40 Isles Road, Indooroopilly, QLD 4068, Australia a Anglo Technical Solutions Research, PO Box 106, Crawn Mines 2025, South Aftica b A11glo Atnerica11 Platinu1n, 55 Marshall Street, Marshalltown, ] oha11nesburg 2001, South Aftica c ARTICLE INFO ABSTRACT Article history: Automated mineralogy methods and tools, such as the Mineral Liberation Analyser ( MLA) and the Received 27 May 2013 QEMSCAN, are now widely used for ore characterization, process design and process optimization. Accepted 17 jar1uary 2014 Several case studies published recently demonstrate that large gains can be obtained throL1gh grinding Available online 13 February 2014 and flotation optimization guided by automated mineralogy data. However, since automated mineralogy can only provide the information pointing to where the process gains can b,e made; it does not directly l(eywords: impact the prodLtction gain. Thus the question is often asked: how to value the contribution of automated Autornated 1ni11eralogy mineralogy to process improvement at a particular plant. This appears to be a difficult question to Value of ir1for1natio11 answer. On close examination however, it is found that this is essentially a qL1estion of the valLme of infor Cor1centrator opti1nisation mation and this is reasonably well docL1mented in various other industries. Hubbard, 2010, in chapter 7 "Measuring the Value of Information'', dealt with exactly this type of problem. The value of information is the redLmced risk of an investment and opportunity loss. The methods HLmbbard developed can be applied to estimate the value of automated mineralogy, as well as metallLmrgical test work, both producing information that reduces the risk of investment.This paper first introduces HL1bbard's theory on the valLte of information and how to measure it. It then applies his methods to estimate the value of automated mineralogy, using Anglo Platinum's fine grinding project as an example. In the end, a general model is developed to allow the simulation of the valLte of automated mineralogy in different mining operations constrained by different parameters. © 2014 Elsevier Ltd. All rights reserved. 1. Introduction It must be emphasized that a utomated mineralogy can only provide information pointing to where the process gains can be Automated mineralogy methods and tools, such as the Mineral m.ade and the extent of the benefit. To realise the gain, the p rocess l iberation Analyser (MIA) and the QEMSCAN, are now widely used ing plant needs to be adjusted and, in most cases, modified signif in the mining indust ry worldwide, with close to two hundred of icantly by expert process engineers. Consequently, the linl< these systems installed in research and company central labs over between automated mineralogy data and final p roduction gain is the last ten years. By measuring samples of ore and processing plant indirect. As such, the question is often asl

The value of automated mineralogy

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