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，联合国气候变化的政府间小组发布了一份报告，指出必须减少全球二氧化碳排放的规模和紧迫性。不可避免的结果将是全球政策的出现，它限制了化石燃料作为能源的燃烧，并促进了可再生“绿色”能源的使用。绿色能源（例如太阳能和风能）的来源不一致，高峰供应周期很少与峰值需求期匹配，需要存储电池。锂（LI）和镁（MG）是用于存储能量的电池的主要组件，用于电动汽车和电网。 Li和MG都被列为对美国国家安全和经济至关重要的35个矿物中的2个，2019年6月，美国和加拿大政府宣布了一项计划，制定一项战略来重建此类关键矿物的国内供应。在加拿大，已经确定了200万吨LI和超过50mt的毫克资源，其中大多数分别包含在矿物质的Spodumene和Magnesite中。与Spodumene/Magnesite浓度相关的技术挑战为商业生产带来了障碍，因为用于升级这些矿物质的方法可能很复杂。近年来，很少有重大改进。拟议的研究的长期目标是通过开发新型矿物工艺来确保未来在储能应用中使用的矿物，从而生产高级spodumene和镁铁矿矿物质浓缩物。由于Spodumene和Magnesite Systems都代表了与自己的脉管（废物）矿物的选择性分离的挑战，因此该计划的短期目标是开发浮选试剂系统，这些试剂系统对相关的甘型矿物质表现出对矿物的选择性。这项工作将通过对纯矿物质（微盘）和加拿大沉积物的真实矿石（批处理浮选测试）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量化因素在Spodumene矿石磨碎过程中产生不同裂解平面以及随后对浮选的影响的因素。将统计建模的准确性与机器学习模型进行比较，以评估矿物处理应用中机器学习的潜在用途。总体而言，该研究计划将通过联合应用实验室研究和先进的建模技术来实施广泛实施绿色能源所需的矿物质。拟议的研究计划将为高素质的人员提供培训； 3名本科生，2名MASC学生和3名博士生，他们将为加拿大矿业行业做出重要贡献，利用了罕见的主题专业知识与数据处理和机器学习技能的组合。