Online Analysis of Malachite Content in the Beneficiation Process Based on Visible-NIR Spectroscopy and GWO-SVM Algorithm

High-precision prediction of the target minerals’ content in the feed and concentrate products is vitally important for the efficient beneficiation of mineral resources. Visible and near-infrared (NIR) spectroscopy provides a cost-effective way for online measurement of the mineral content in an industrial process. In this investigation, simulated specimens consisting of five different types of minerals that are present in the copper oxide ore, malachite, quartz, calcite, hematite, and chlorite were prepared with a variety of malachite contents, and the mixed specimens were analyzed by a visible-NIR spectrometer in an integral sphere mode. The reflectance spectrum is used as input and the malachite content as output to build the prediction model. The obtained data was modeled by support vector machines (SVM), and a Grey Wolf Optimization (GWO) is proposed with the goal of improving the prediction accuracy. The GWO algorithm has been applied to adaptively search for the best combination of featured values. After cyclic comparison, the optimal penalty factors c and g can be quickly and accurately selected. The experimental results show that the SVM model established by the GWO algorithm has a better fitting effect and smaller prediction error, compared with other models.