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%.

This paper deals with the recovery of gold from waste printed circuit boards (WPCBs) ash by high-pressure oxidative leaching (HPOL) pre-treatment and iodide leaching followed by reduction precipitation. Base metals present in WPCB ash were removed via HPOL using a diluted sulfuric acid solution at elevated temperatures. Effects of potassium iodide concentration, hydrogen peroxide concentration, sulfuric acid concentration, leaching temperature, and leaching time on gold extraction from pure gold chips with KI–H₂O₂–H₂SO₄ were investigated. The applicability of the optimized iodide leaching process for the extraction of gold from the leach residue obtained after HPOL were examined at different pulp densities ranging from 50 g/t to 200 g/t. Results show that the removal efficiency was 99% for Cu, 95.7% for Zn, 91% for Ni, 87.3% for Al, 82% for Co, and 70% for Fe under defined conditions. Under the optimal conditions, the percentage of gold extraction from the gold chips and the residue of WPCBs was 99% and 95%, respectively. About 99% of the gold was selectively precipitated from the pregnant leach solution by sequential precipitation with sodium hydroxide and L-ascorbic acid. Finally, more than 93% of gold recovery was achieved from WPCB ash by overall combined processes.

Japan's largest platform for academic e-journals: J-STAGE is a full text database for reviewed academic papers published by Japanese societies