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

从加拿大硬岩矿床中浓缩对储能应用至关重要的矿物

• 批准号: RGPIN-2020-04290
• 负责人: Gibson, Charlotte
• 金额: $ 2.04万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=739868
• 关键词: Concentrating; Minerals; Critical; Energy; Storage

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.

2018年，联合国政府间气候变化专门委员会发布了一份报告，指出了必须减少全球二氧化碳排放的规模和紧迫性。这样做的一个不可避免的结果将是出现限制燃烧化石燃料作为能源并促进使用可再生“绿色”能源的全球政策。太阳能和风能等绿色能源的来源并不一致，高峰供应期很少与高峰需求期相匹配，需要电池储存。锂（Li）和镁（Mg）是用于储存能量以为电动汽车和电网供电的电池的主要成分。锂和镁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，制定战略以重建此类关键矿物的国内供应。在加拿大，已发现200万吨锂和超过5000万吨镁资源，其中大部分分别包含在锂辉石和菱镁矿中。与锂辉石/菱镁矿浓缩相关的技术挑战对商业生产造成了障碍，因为用于升级这些矿物的方法可能很复杂;近年来几乎没有重大改进。拟议研究的长期目标是通过开发新型矿物工艺生产高品位锂辉石和菱镁矿精矿，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代表了从其自身的脉石（废物）矿物中选择性分离的挑战，该计划的短期目标是开发浮选试剂系统，该系统对相关脉石矿物表现出对感兴趣的矿物的选择性。这项工作将通过对加拿大矿床的纯矿物（微浮选）和真实的矿石（分批浮选试验）进行实验室试验来完成。该计划将使用统计和机器学习模型作为工具，以量化影响锂辉石矿石研磨过程中不同解理面生成的因素以及随后对浮选的影响。统计建模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，他们将为加拿大采矿业做出重要贡献，利用主题专业知识与数据处理和机器学习技能的罕见组合。