Concentrating Minerals Critical to Energy Storage Applications from Canadian Hard Rock Deposits

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In 2018, the United Nations' Intergovernmental Panel on Climate Change released a report stating the magnitude and urgency with which global CO2 emissions must be reduced. An inevitable outcome of this will be the advent of global policy that limits the burning of fossil fuels as an energy source and promotes the use of renewable "green" energy sources. Sources of green energy, such as solar and wind, are not consistent and periods of peak supply rarely match with periods of peak demand, requiring battery storage. Lithium (Li) and magnesium (Mg) are major components of batteries used to store energy to power electric vehicles and electrical grids. Both Li and Mg were listed as 2 of 35 minerals critical to the national security and economy of the United States and in June of 2019, the United States and Canadian governments announced a plan to develop a strategy to rebuild domestic supply of such critical minerals. In Canada, 2 million tonnes of Li and over 50Mt of Mg resources have been identified, the majority of which are contained in the minerals spodumene and magnesite, respectively. Technical challenges associated with spodumene/magnesite concentration create a barrier to commercial production since the methods used to upgrade these minerals can be complex; few major improvements have been made in recent years. The long-term goal of the proposed research is to secure future domestic supply of minerals used in energy storage applications through the development of novel mineral processes to produce high grade spodumene and magnesite mineral concentrates. As both spodumene and magnesite systems represent challenges for selective separations from their own gangue (waste) minerals, the short-term goal of the program is to develop flotation reagent systems that demonstrate selectivity towards minerals of interest over associated gangue minerals. This work will be accomplished through laboratory testing on pure minerals (micro-flotation) and on real ores (batch flotation tests) from Canadian deposits. The program will use statistical and machine learning models as tools to quantify factors affecting the generation of different cleavage planes during the grinding of spodumene ores and the subsequent effect on flotation. The accuracy of statistical modelling will be compared to machine learning models to assess the potential uses for machine learning in mineral processing applications. Overall, the research program will enable the domestic supply of minerals required for the widespread implementation of green energy through combined application of laboratory studies and advanced modelling techniques. The proposed research program will provide training for highly qualified personnel; 3 undergraduate students, 2 MASc students and 3 PhD students, who will make important contributions to the Canadian mining industry, leveraging a rare combination of subject matter expertise with data processing and machine learning skills.