Geometallurgical modeling of mining complexes: testing causal hypothesis to improve plant performance

采矿综合体的地质冶金建模：测试因果假设以提高工厂性能

• 批准号: 554627-2020
• 负责人: Ortiz, Julian
• 金额: $ 2.91万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=705670
• 关键词: Geometallurgical; modeling; mining; complexes; testing

Raw materials such as metals and industrial minerals are required to sustain the current societal needs. Despite efforts towards recycling and reusing materials, the developing nations and the transition to the low-carbon economy requires new mining endeavours to satisfy the demand for metals and other strategic commodities such as rare earths elements. To make the mining industry as sustainable as possible, the extraction of raw materials must be effective and efficient. In order to achieve this, this research project proposes the development of predictive tools based on artificial intelligence, in collaboration with ArcelorMittal, a multinational steel manufacturing corporation. In particular, we propose developing causal models for each unit operation, in order to model the mining cycle as a system, that can be jointly analyzed and optimized. Causal models are mathematical models based on graphical representations and probability that facilitate the analysis of causal relationships. A graphical representation of a process entails identifying the input factors that have an effect on a response, and finding the links between these variables. The models then use statistical analysis to compute probability of events. A plausible causal model can be proposed and tested with the data available at the mine site. If the inference model is deemed correct, counterfactuals can be analyzed, that is, the probability of an unobserved phenomenon can be calculated. Therefore, these models allow decisions and predictions accounting for the possibility of scenarios that are not observed in the available data. This possibility will benefit the industry by improving the understanding of the processes and operations, and by allowing the prediction of responses of each unit operation during the process of the material extracted from the mine, therefore building an interconnected system of operations, that can be jointly optimized to increase efficiency, reduce the use of water and energy and minimize the waste generation.

需要金属和工业矿物等原材料来维持当前的社会需求。尽管发展中国家正在努力回收和再利用材料，但发展中国家和向低碳经济转型需要新的采矿努力，以满足对金属和其他战略商品（如稀土元素）的需求。为了使采矿业尽可能地可持续发展，原材料的提取必须是有效和高效的。为了实现这一目标，该研究项目提议与跨国钢铁制造公司安赛乐米塔尔合作，开发基于人工智能的预测工具。特别是，我们建议为每个单元操作建立因果模型，以便将采矿周期建模为一个系统，可以共同分析和优化。