On-stream mineral identification of tailing slurries of tungsten via NIR and XRF data fusion measurement techniques

Abstract

Mineral flotation processes are controlled by monitoring the grade of the present minerals. The economy of the flotation process can be significantly improved by on-line analysis of minerals in a slurry. However, online and quantitative mineral identification of slurries is challenging. Industrial developers are demanding novel ideas enabling differentiation between minerals with similar elemental contents, such as scheelite and fluorite or gangue minerals, since they have different flotation properties. The primary focus of this research is the measurement of mineral contents from the elemental concentrations acquired by an on-stream slurry analyser based on X-ray fluorescence (XRF) and near-infrared spectroscopy (NIR). In this work, the samples in the test were obtained from a tungsten dressing plant. It is vital to master the mineral grade for controlling the flotation plant. The XRF parameters were optimised by Monte Carlo simulation, and the XRF and NIR data fusion was discussed. A multivariate statistical method called the least squares support vector machine (LS-SVM) was employed to perform the element-to-mineral conversion. The results show that such data integrations enable on-stream and quantitative identification of slurry mineral contents, especially for scheelite, wolframite, fluorite and calcite, which are essential minerals in tungsten ore beneficiation. This technique can lead to many benefits, such as rapid control of concentrate quality, enhanced recovery and savings in money, time, energy and workforce.