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, RoussosGeorgios
• 金额: $ 9.95万
• 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: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=608088
• 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.

建模和优化技术已发展成为优化矿山设计和生产计划的重要工具，目的是使矿业资产从最初的评估和开发到关闭的经济价值最大化[1-11]。最近，包括我们的CRDPJ Grant 411270-10，已经为复杂的采矿系统开发了新的方法和算法，使得能够明确地和联合地管理矿体、采矿、加工和市场不确定性[11-23]，并显示净现值和金属产量的大幅增长。从过去的所有发展来看，下列领域仍然是主要的开放研究领域：(A)大规模采矿优化问题的计算复杂性，(B)关键参数(地质/采矿、财务)的内生和外生不确定性，以及(C)智能地适应信息披露的采矿综合要素缺乏“整体”优化。