The Value Of Automated Mineralogy

Automated mineralogy methods and tools, such as the Mineral Liberation Analyzer (MLA) and the QEMSCAN, are now widely used for ore characterization, process design and optimization. Several case studies published recently demonstrate that large gains can be obtained through grinding and flotation optimization guided by automated mineralogy data. However, since automated mineralogy can only provide the information pointing to where the process gains can be made, it does not directly impact the production gain. Thus, a question is often asked: how to value the contribution of automated mineralogy to process improvement of a particular plant. This appears to be a difficult question. On close examination however, it is found that this is essentially a question of the value of information and it is reasonably well documented 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 reduced risk of an investment and opportunity loss. The methods Hubbard developed can be applied to estimate the value of automated mineralogy, as well as metallurgical test work, which also produces information that reduces the risk of investment. This paper first introduces Hubbard?s theory on the value 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 this case, the value of automated mineralogy includes the reduction in investment risk worth US$ 62.5million, in project rejection worth US$197 million and in opportunity loss worth US$320 million. In the end, a general model is developed to allow the simulation of the value of automated mineralogy in different mining operations constrained by different parameters.