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

基本信息

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

项目摘要

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.

建模和优化技术已发展成为优化矿山设计和生产计划的重要工具，旨在最大化采矿资产从初始评估、开发到关闭的经济价值[1-11]。最近，包括我们的 CRDPJ Grant 411270-10 在内，针对复杂采矿系统开发了新的方法和算法，能够对矿体、采矿、加工和市场不确定性进行明确和联合管理 [11-23]，并显示 NPV 大幅增加和金属生产。从过去的发展来看，以下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）缺乏对采矿综合体的元素进行“整体”优化，以智能地适应揭示的信息。我们的工作为战略随机矿山规划优化方法做出了重大发展，特别是通过使用我们新开发的计算高效的模拟和优化方法，在不确定性下集成并同时优化采矿综合体的关键要素。通过这些贡不确定性（地质/供应）。这一贡献为开发一个先进的、包容各方的框架来处理以前无法解决的几个关键问题开辟了新的可能性。