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The verification efficiency and precision of copper ore grade has a great influence on copper ore mining. At present, the common method for the exploration of reserves often uses chemical analysis and identification, which have high costs, long cycles, and pollution risks but cannot realize the in situ determination of the copper grade. The existing scalar spectrometric techniques generally have limited accuracy. As a vector spectrum, polarization state information is sensitive to mineral particle distribution and composition, which is conducive to high-precision detection. Taking the visible-near infrared parallel polarization reflectance spectrum data and grade data of a copper mine in Xiaoyuan village, Huaining County, Anhui Province, China, as an example, the characteristics of the parallel polarization spectra of the copper mine were analyzed. The spectra were pretreated by first-order derivative transform and wavelet denoising, and the dimensions of wavelet denoising spectra, parallel polarization spectra, and first-order derivative spectra were also reduced by principal component analysis (PCA). Three, four, and eight principal components of the three types of spectra were selected as variables. Four machine learning models, the radial basis function (RBF), support vector machine (SVM), generalized regression neural network (GRNN), and partial least squares regression (PLSR), were selected to establish the PCA parallel polarization reflectance spectrum and copper grade prediction model. The accuracy of the model was evaluated by the determination coefficient (R2) and root mean square error (RMSE). The results show that, for parallel polarization spectra, first-order derivative spectra, and wavelet denoising spectra, the PCA-SVM model has better results, with R2 values of 0.911, 0.942, and 0.953 and RMSE values of 0.022, 0.019, and 0.017, respectively. This method can effectively reduce the redundancy of polarized hyperspectral data, has better model prediction ability, and provides a useful exploration for the grade analysis of hydrothermal copper deposits at meso-low temperatures.

In order to rationally develop and utilize a Ta–Nb–Li–Rb rare metal deposit in Jiangxi Province, the mineralogical characteristics of the ore, such as chemical composition, mineral composition, modes of occurrence of major elements, and dissemination characteristics of major minerals, were investigated in detail based on optical microscopy analysis, chemical analysis, X-ray diffraction analysis, artificial panning, mineral liberation analysis, and electron probe microanalysis. The results reveal that the main useful elements in the ore are tantalum, niobium, lithium, and rubidium. Niobium and tantalum are mainly found in the mineral form of columbite. Columbite has particle sizes ranging from 0.5 mm to 0.012 mm, with the most common sizes being 0.3 to 0.044 mm. Intergranular dispersion accounts for 73.92% of the embedding in columbite, whereas inclusions account for 26.08%. Lithium is found mostly in zinnwaldite, while rubidium is found primarily in feldspar and zinnwaldite, both in a homogenous distribution. The beneficiation evaluation of this ore was conducted based on the mineralogical characteristic, and it indicates that the tantalum-niobium–lithium–rubidium rare metal resources, as well as the feldspar and quartz non-metallic resources in the ore, can be effectively and comprehensively recovered using gravity, magnetic, and flotation separation methods. A staged grinding and separating process was adopted which could produce tantalum–niobium mineral concentrates (18.34% Ta2O5 at a recovery of 47.65% and 41.33% Nb2O5 at a recovery of 69.96%), zinnwaldite concentrate (2.41% Li2O and 0.80% Rb2O at a recovery of 81.82%) and other concentrates such as cassiterite, topaz, galena, sphalerite, and feldspar. This study provides suggestions for the rational development and utilization of the deposit and provides a reasonable level of recovery prediction.

Using ferric chloride as an oxidant, here, we investigated the leaching effect of low-nickel matte in a flow field produced by mechanical agitation. The factors affecting a leaching reaction, such as stirring speed, leaching time, low-nickel matte particle size, and inert abrasive quartz sand, were studied. X-ray diffraction (XRD), X-ray fluorescence spectroscopy (XRF), a laser particle size analyzer, optical microscopy (OM), a scanning electron microscopy (SEM) with an energy dispersive X-ray detector (EDS), and a Raman spectrometer were used to characterize the materials before and after the leaching reaction. The contents of the main metal ions such as Ni, Cu, and Co in the leaching solution were analyzed by inductively coupled plasma atomic emission spectroscopy (ICP-AES). Using the control variable method, the optimal experimental conditions were as follows: 2 mol/L FeCl3—0.5 mol/L HCl-H2O system with low-nickel matte and quartz sand (mass ratio is 1:5) and leaching at 90 °C for 8 h. The results showed that the blocking effect of the solid product sulfur layer was effectively removed and continuous leaching was realized. The leaching efficiencies of Ni, Cu, and Co were 98.9%, 99.3%, and 98.1%, respectively.