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

2018年，联合国政府间气候变化专门委员会发布了一份报告，阐述了必须减少全球二氧化碳排放的规模和紧迫性。这一趋势的必然结果将是出台全球政策，限制化石燃料作为一种能源的燃烧，并促进可再生“绿色”能源的使用。太阳能和风能等绿色能源的来源并不一致，供应高峰期很少与需求高峰期相匹配，因此需要电池存储。锂(Li)和镁(Mg)是电池的主要成分，用于储存能量，为电动汽车和电网提供动力。锂和镁都被列为对美国国家安全和经济至关重要的35种矿物中的2种，2019年6月，美国和加拿大政府宣布了一项计划，以制定一项战略，以重建此类关键矿物的国内供应。在加拿大，已查明了200万吨锂和5000多吨镁资源，其中大部分分别包含在锂辉石和菱镁矿矿物中。与锂辉石/菱镁矿浓缩有关的技术挑战对商业化生产造成了障碍，因为用于提炼这些矿物的方法可能很复杂；近年来几乎没有取得重大改进。拟议研究的长期目标是通过开发生产高品位锂辉石和菱镁矿精矿的新矿物工艺，确保未来国内用于储能应用的矿物供应。由于锂辉石和菱镁矿系统都是从各自的脉石(废料)矿物中选择性分离的挑战，该计划的短期目标是开发浮选药剂系统，证明其对感兴趣的矿物的选择性高于伴生的脉石矿物。这项工作将通过对加拿大矿藏的纯矿物(微浮选)和真实矿石(批量浮选试验)进行实验室测试来完成。该计划将使用统计和机器学习模型作为工具，量化影响锂辉石矿石磨矿过程中不同解理平面产生的因素以及随后对浮选的影响。统计模型的准确性将与机器学习模型进行比较，以评估机器学习在矿物加工应用中的潜在用途。总体而言，该研究计划将通过实验室研究和先进建模技术的结合应用，使国内能够供应广泛实施绿色能源所需的矿物。拟议的研究计划将为高素质的人才提供培训；3名本科生、2名MASC学生和3名博士生，他们将利用学科专业知识与数据处理和机器学习技能的罕见组合，为加拿大采矿业做出重要贡献。

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.