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

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尾矿库和废石堆的排水受到污染。当矿山废物中的硫化物矿物氧化并在排水中产生污染物，导致酸性水域的酸性矿山排水(AMD)或近中性水域的污染中性排水(CND)时，可能会发生这种污染。潜在有害废物的成功管理和回收在很大程度上取决于使用预测性测试来确定受污染矿井排水的潜在发生情况。这些测试必须尽可能准确和具有代表性，以确保可以有效和高效地避免或减轻AMD或CND造成的环境退化。对这些预测性测试的解释需要对所研究材料的组成以及控制矿山废物污染物释放的不同地球化学和微生物机制有广泛的了解。这对新兴的稀土元素(REE)和锂(Li)大宗商品以及北极的矿山尤为关键。在历史上，CND的发病和质量预测使用了最初为AMD预测而开发的相同技术(例如，酸碱核算、湿度细胞)。然而，这些方法通常寻求评估酸性条件的开始，而不是预测预期的确切浓度，这对CND至关重要。此外，关于微生物参与CND生成的知识仍然相对不确定，特别是在寒冷的温度下；生物过程可能在零度以下的硫化物氧化中有很大的参与。因此，需要更多的研究来改进CND的预测，并获得关于CND产生的微生物学的更多知识。此外，对清洁能源日益增长的需求刺激了许多REE和Li采矿项目的发展。这些新的作业需要对矿山废物中稀土和锂浸出的地球化学控制进行严格的调查，因为目前文献中只发现了非常稀少的数据。因此，需要进行更多的研究，以显著提高对这些问题的认识。研究计划分为两个主要主题：(A)改进常温和低温下的CND预测，(B)评估含稀土和锂的矿山废物的金属浸出潜力。实现这些目标所使用的方法将彻底的物理、化学、矿物学和微生物学表征与矿山排水动态预测测试相结合，以更好地了解污染物释放的机理。该计划将培养本科生和研究生，从而为加拿大采矿业和研究界提供急需的矿山环境管理专家。