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The study is aimed at particle sorting at the Copper Mountain Mine using XRF. Possible applications include the rejection of barren material from mill feed, the rejection of pebbles in the SABC circuit or the recovery of valuable material from low grade stockpiles. It is recognized that XRF is a surface measurement that can detect copper but depending on several operational conditions, such as the orientation of the particle or mineral texture, the sensor spot may not see the copper. However, XRF also provides information about the concentrations of a range of elements in minerals that are associated with copper mineralization which can improve sorting. The study described herein is aimed at improving XRF sensor-based sorting by the introduction of logistics regression (LR)- and support vector machine (SVM)-based machine learning approaches. To solve the collinearity and dimensionality issues in the input variables, the authors propose a combined approach of principal component analysis (PCA) and stepwise regression to extract the significant features. The combined PCA and stepwise regression approach is novel and has shown to be very effective for dimensionality reduction of the XRF spectrum data. By applying the ROC and AUC), the LR and SVM models are compared. Results showed that the LR model with the AUC of 0.847 outperforms the SVM with kernel functions with respect to classification accuracy; especially for data sets with a small number of features. The improved classification accuracy should benefit the economic performance of the particle sorting system.
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