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Sulfide copper mineral, typically Chalcopyrite (CuFeS), is one of the most common minerals for producing metallic copper via the pyrometallurgical process. Generally, flotation tailings are produced as a byproduct of flotation and still consist of un‒recovered copper. In addition, it is expected that more tailings will be produced in the coming years due to the increased exploration of low‒grade copper ores. Therefore, this research aims to develop a copper recovery process from flotation tailings using high‒pressure leaching (HPL) followed by solvent extraction. Over 94.4% copper was dissolved from the sample (CuFeS as main copper mineral) by HPL in a HO media in the presence of pyrite, whereas the iron was co‒dissolved with copper according to an equation given as C = 38.40 × C. To avoid co‒dissolved iron giving a negative effect on the subsequent process of electrowinning, solvent extraction was conducted on the pregnant leach solution for improving copper concentration. The result showed that 91.3% copper was recovered in a stripped solution and 98.6% iron was removed under the optimal extraction conditions. As a result, 86.2% of copper was recovered from the concentrate of flotation tailings by a proposed HPL‒solvent extraction process.

Generally, trace precious metals remaining in wastewaters generated from the refining process of precious metals are not recovered, due to a relatively high processing cost as well as various technical problems. Recovery of precious metals from wastewaters is very important for the conservation of resources and the protection of environment. However, wastewaters containing a large amount of ammonium ion (NH 4 þ ) cannot be treated by general neutralization operation, due to formation of metal ammine complexes with increasing pH. In this study, the possibility of recovering precious metals and other valuable metals from wastewaters by various traditional metallurgical processes such as cementation, neutralization and reduction, were investigated. A recovery of 99% Copper (Cu), 96% Palladium (Pd), and 85% Gold (Au) by cementation using Iron (Fe) powder, and 99.6% Cu, 99.5% Pd by cementation using Aluminum (Al) powder was achieved. However, complete recovery of all valuable metals by a one-step cementation process was not possible. On the other hand, precious metals and other valuable metals including Copper and Indium, etc., were precipitated by combining neutralization, deammoniation and reduction processes. Results showed that the recovery of Platinum (Pt) in the reduction process was improved by adding deammoniation step. Finally, precious metals are concentrated in the crude copper metal by fusion process. The recovery of Au, Ag, Pd was more than 91%, and that of Pt was about 71%.

In mining operations, an ore is separated into its constituents through mineral processing methods, such as flotation. Identifying the type of minerals contained in the ore in advance aids greatly in performing faster and more efficient mineral processing. The human eye can recognize visual information in three wavelength regions: red, green, and blue. With hyperspectral imaging, high resolution spectral data that contains information from the visible light wavelength region to the near infrared region can be obtained. Using deep learning, the features of the hyperspectral data can be extracted and learned, and the spectral pattern that is unique to each mineral can be identified and analyzed. In this paper, we propose an automatic mineral identification system that can identify mineral types before the mineral processing stage by combining hyperspectral imaging and deep learning. By using this technique, it is possible to quickly identify the types of minerals contained in rocks using a non-destructive method. As a result of experimentation, the identification accuracy of the minerals that underwent deep learning on the red, green, and blue (RGB) image of the mineral was approximately 30%, while the result of the hyperspectral data analysis using deep learning identified the mineral species with a high accuracy of over 90%.