Smart mining complexes: large-scale stochastic optimization, high-order simulation and self-learning decision support systems for sustainable development of mineral resources

智能矿山综合体：矿产资源可持续发展的大规模随机优化、高阶模拟和自学习决策支持系统

• 批准号: 500414-2016
• 负责人: Dimitrakopoulos, Roussos
• 金额: $ 25.74万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=694868
• 关键词: Smart; mining; complexes; large; scale

Modelling and optimization technologies have evolved to become an essential tool for optimal mine designs and production schedules, aiming to maximize the economic value of mining assets from their initial assessment and development to closure [1-11]. Recently, including our CRDPJ Grant 411270-10, new methodologies and algorithms have been developed for complex mining systems that enable the explicit and joint management of orebody, mining, processing and market uncertainties [11-23], and show substantial increases in both NPV and metal production. It is evident in all past developments, that the following areas are still main open areas for research: (a) computational complexity of large scale mining optimization problems, (b) endogenous and exogenous uncertainties in the key parameters (geological/mining, financial), and (c) absence of 'holistic' optimization of the elements of a mining complex that intelligently adapts to unveiling information. Our work contributes substantial developments to strategic stochastic mine planning optimization methods, specifically, by integrating and simultaneously optimizing critical elements of mining complexes under uncertainty using our newly developed, computationally efficient simulation and optimization approaches. Through these contributions, we have changed the mine planning paradigm in several ways, particularly by shifting the focus of the optimizers from the assessment of economic values of extracted materials to the economic values of the products generated and sold to customers, while accounting for major sources of uncertainty (geological/supply). This contribution opens new possibilities to develop an advanced, all-inclusive framework to deal with several critical issues that could not be previously addressed.

建模和优化技术已经发展成为优化矿山设计和生产计划的重要工具，旨在最大限度地提高采矿资产从最初评估和开发到关闭的经济价值[1-11]。最近，包括我们的CRDPJ赠款411270-10，已经为复杂的采矿系统开发了新的方法和算法，使矿体，采矿，加工和市场不确定性的明确和联合管理成为可能[11-23]，并显示净现值和金属产量的大幅增加。 很明显，在所有过去的发展，以下领域仍然是主要的开放领域的研究：（a）大规模采矿优化问题的计算复杂性，（B）内源性和外源性的不确定性的关键参数（地质/采矿，金融），和（c）缺乏“整体”优化的元素的采矿综合体，智能地适应揭开信息。 我们的工作为战略性随机矿山规划优化方法做出了重大贡献，具体而言，通过使用我们新开发的计算效率高的模拟和优化方法，在不确定性下整合并同时优化采矿综合体的关键要素。通过这些贡献，我们在几个方面改变了矿山规划模式，特别是通过将优化者的重点从评估开采材料的经济价值转移到生产和销售给客户的产品的经济价值，同时考虑到不确定性的主要来源（地质/供应）。这一贡献为制定一个先进的、包容各方的框架以处理以前无法处理的若干关键问题开辟了新的可能性。