Improvements to mine drainage prediction for CND and REE/Li-bearing mine wastes

CND 和含 REE/Li 矿山废物的矿山排水预测的改进

• 批准号: RGPIN-2017-05177
• 负责人: Plante, Benoît
• 金额: $ 1.6万
• 依托单位: Université du Québec en Abitibi-Témiscamingue
• 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=757528
• 关键词: Improvements; mine; drainage; prediction; CND; Improvements; mine; drainage; prediction; CND

One of the most serious environmental issues facing the mining industry worldwide is the contamination of drainage waters from tailings ponds and waste rock piles. Such contamination may occur when sulfide minerals in the mine wastes oxidize and generate contaminants in the drainage waters, leading to acid mine drainage (AMD) in acidic waters or to contaminated neutral drainage (CND) in near-neutral waters. The successful management and reclamation of potentially harmful wastes greatly depends on the use of predictive tests to determine the potential occurrence of contaminated mine drainage. These tests must be as accurate and representative as possible to ensure that environmental degradation by AMD or CND can be effectively and efficiently avoided or mitigated. The interpretation of these predictive tests requires an extensive knowledge of the composition of the studied materials, as well as of the different geochemical and microbiological mechanisms controlling the release of contaminants from mine wastes. This is especially critical for the emerging commodities of rare earth elements (REE) and lithium (Li), as well as mines in the Arctic. Historically, the onset and quality of CND have been predicted using the same techniques which were originally developed for AMD prediction (e.g. acid-base accounting, humidity cells). However, these methods generally seek to assess the onset of acidic conditions rather than to predict the exact concentrations to be expected, which is essential for CND. In addition, knowledge on the microbiological involvement in CND-generation is still relatively uncertain, particularly for cold temperatures; biotic processes might be significantly involved in sub-zero sulfide oxidation. Thus, more research is needed to improve CND prediction and acquire additional knowledge on the microbiology of CND generation. In addition, the increasing demand for clean energy has spurred the development of numerous REE and Li mining projects. These new operations require rigorous investigation of the geochemical controls on REE and Li leaching from mine wastes as there are currently only very sparse data found in the literature. Thus, more research is needed in order to significantly improve knowledge on these issues.The research program is divided into two main themes: (A) Improvements to CND prediction in ambient and cold temperatures, and (B) Evaluation of the metal leaching potential of REE- and Li-bearing mine wastes. The methodology used to reach these objectives combines thorough physical, chemical, mineralogical, and microbiological characterizations with kinetic mine drainage prediction tests to better understand the mechanisms underlying contaminant release. The program will train undergraduate and graduate students, thus providing the Canadian mining industry and research community with much needed experts in the environmental management of mines.

全球采矿业面临的最严重的环境问题之一是污染尾矿池塘和废岩堆的排水水。当矿山中的硫化物矿物质氧化并在排水水中产生污染物时，可能会发生这种污染，从而导致酸性水域中的酸性矿山排水（AMD）或在近乎中性水中污染的中性排水（CND）。成功管理和填海可能有害废物在很大程度上取决于使用预测测试来确定受污染的矿山排水的可能发生。这些测试必须尽可能准确和代表性，以确保可以有效地避免或减轻AMD或CND的环境降解。这些预测测试的解释需要广泛了解所研究材料的组成以及控制污染物从矿山废物中释放的不同地球化学和微生物机制。这对于稀土元素（REE）和锂（Li）以及北极地区的新兴商品尤其重要。从历史上看，CND的发作和质量已经使用最初用于AMD预测开发的相同技术（例如酸碱会计，湿度细胞）。但是，这些方法通常寻求评估酸性条件的发作，而不是预测预期的确切浓度，这对于CND至关重要。此外，关于CND产生的微生物参与的知识仍然相对不确定，尤其是对于寒冷的温度。生物过程可能显着参与零下硫化物氧化。因此，需要更多的研究来改善CND预测并获得有关CND生成微生物学的更多知识。此外，对清洁能源的需求不断增长，这刺激了许多REE和LI采矿项目的发展。这些新操作需要严格研究REE和Li从矿井中浸出的地球化学控制，因为目前在文献中只发现非常稀疏的数据。因此，为了显着提高这些问题的知识，需要进行更多的研究。研究计划分为两个主要主题：（a）改进环境和寒冷温度的CND预测，以及（b）评估REE和LIE-BAIREARGIAN矿山废物的金属浸出潜力。实现这些目标的方法结合了彻底的物理，化学，矿物学和微生物特征，并进行了动力学矿山排水预测测试，以更好地了解污染物释放的机制。该计划将培训本科生和研究生，从而为加拿大矿业行业和研究社区提供矿山环境管理急需的专家。