Canada is one of the largest mining countries in the world, but to remain globally competitive, the mining industry in Canada must find ways to increase its productivity and profitability. Efficiency of metal recovery requires optimized strategies reflecting the complex mineral chemistry that is encountered in many mineral processing operations. Methods of recovery (eg. flotation and cyanidation) have operated for more than a century. However, a quantitative relationship between mineral chemistry, mineral surface chemistry and reaction to reagents is only modestly developed and strategic models for improved efficiency are lacking. Most problems are solved on a case-by-case basis with the application of high technology analytical tools often overlooked in favor of more traditional approaches.
The technical objectives of this project are to identify the chemical controls operative on sponsor-specific case studies and use these parameters to identify potential optimization strategies. To meet the objectives of the project, our approach will be to perform detailed scientific investigations on these case studies. Our approach will be the following: 1) using ASCT, in combination with traditional analytical techniques, identify mineral surface and pulp chemical parameters operative in specific case studies, 2) based on the identified case study parameters and electrochemical testing performed in bench scale reactors, develop conceptual models related to the process, 3) using the models, combined with the analytical parameters identified in 1), define testing strategies for process improvement, 4) where appropriate, and warranted in the case study, evaluate strategies for process improvement, and 5) develop an analytical decision tree relating analytical approach to understanding specific metallurgical problems with the potential to provide process optimization solutions.
The aim of the project is to develop, modify, and apply new tools and methodologies to better identify factors controlling process efficiency with an overall goal of improving metallurgical performance and the potential to aid in the development of future metallurgical processes. New knowledge, regarding individual and common factors affecting metallurgical performance generated through the individual sponsor processes investigations will be delivered to all six of our industry partners. The ultimate outcome will be the development of specific analytical protocols and testing strategies which together will define the components of an analytical decision tree. This decision tree, will be a novel asset for industry metallurgists as it will provide an analytical pathway which capitalizes on the strengths of the various ASC tools in order to better understand a particular process and improve performance.
Led by: Professor Brian Hart, The University of Western Ontario
Professors Zhenghe Xu, Qi Liu, Qingxia Liu, and Hongbo Zeng, University of Alberta
Professor Faiçal Larachi, Université Laval
In collaboration with Allen Pratt and Tesfaye Negeri, CANMET, Mining and Mineral Sciences Laboratories, Natural Resources Canada
In Partnership with:
Glencore Zinc (Matagami Mines)
Glencore Nickel (Strathcona Mill)
Agnico Eagle Mines (Laronde Mine)
Teck Metals (Red Dog Mine)
IAMGOLD (Niobec Mine)
Barrick Gold Corp (Barrack Technology Centre)