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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=761110
• 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，阐述了减少全球二氧化碳排放的重要性和紧迫性。其不可避免的结果将是限制化石燃料作为能源的燃烧并促进可再生“绿色”能源的使用的全球政策的出现。太阳能和风能等绿色能源的来源并不一致，供应高峰期与需求高峰期很少匹配，需要电池存储。锂 (Li) 和镁 (Mg) 是电池的主要成分，用于存储能量，为电动汽车和电网提供动力。锂和镁均被列为对美国国家安全和经济至关重要的 35 种矿物中的两种，2019 年 6 月，美国和加拿大政府宣布了一项计划，制定重建此类关键矿物国内供应的战略。加拿大已探明锂资源量为200万吨，镁资源量超过50吨，其中大部分分别蕴藏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；近年来几乎没有取得重大改进。拟议研究的长期目标是通过开发新型矿物工艺来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对从其本身的脉石（废）矿物中进行选择性分离提出了挑战，因此该计划的短期目标是开发浮选试剂系统，以证明对相关矿物相对于相关脉石矿物的选择性。这项工作将通过对加拿大矿床的纯矿物（微浮选）和真实矿石（批量浮选测试）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。将统计模型的准确性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，实现国内广泛实施绿色能源所需的矿物供应。拟议的研究计划将为高素质人才提供培训； 3 名本科生、2 名硕士生和 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结合，为加拿大采矿业做出重要贡献。