Improving milling operation using uncertainty and global sensitivity analyses

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In general, semi-autogenous grinding (SAG) mills are difficult to operate due to several factors, including the uncertainty of the process. The uncertainty can be epistemic or stochastic, the first is related to the geological factors and the second with operating factors. Changes in these factors could generate an increase in the power consumption, which must be avoided because the energy consumption in comminution represents between 56% and 70% of the costs in the mining industry. Usually, the simulation is used to help adjust the operating conditions of the SAG mill, however, in the literature, there are few publications that propose how to consider these uncertainties in milling operations. This work shows that the uncertainty analysis (UA) and the global sensitivity analysis (GSA) can provide useful tools for the identification of operational conditions for mill systems under uncertainties, considering a SAG as case study UA allows the study of the effect of the distribution and the magnitude of the uncertainties of the input variables in the responses of the grinding process. The GSA method of Sobol'-Jansen (Jansen, 1999; Saltelli et al., 2010) is utilized to identify influential input variables. Then, the regionalization of the input variables is applied to identify the operational regions. It is shown that the control of the uncertainty of the significant input variables allows the control of the uncertainty in the output variables. Therefore, the systematic method proposed in this work allows to evaluate the uncertainties in the processes as a valuable tool for any practitioner in the field of mineral processing. The novelty of this method is that allows to quantify results that have been exposed in the literature only from an experimental approach.