University of Ottawa

Published purpose

In this project, we will apply optical spectroscopy and data analytics technique to address problems in gold mining process. Gold processing by cyanidation can be limited by the presence of specific gangue materials in the ore. The aurocyanide complex, Au(CN)2-, can be partially removed from solution by carbonaceous materials (CM) in the ore, thus reducing yields and profits. We propose to combine the hyperspectral imaging tools of coherent Raman microscopy, Surface Enhanced Raman Scattering (SERS) plasmonic sensors, and advanced machine learning for the rapid screening of CMs with respect to their preg-robbing ability. This novel platform technology will eliminate the need for extensive sample preparation and metallurgical testing, offering Canada’s gold mining sector a rapid, automated, in situ proxy for slow and expensive industry-standard lab-based screening approaches. This potentially disruptive tool for the real-time assessment, operation, and optimization of gold processing will help increase efficiency while reducing environmental impacts.