Developing New Global Stochastic Optimization and High-order Stochastic Models for Optimizing Mining Complexes under Uncertainty

开发新的全局随机优化和高阶随机模型以优化不确定性下的采矿综合体

• 批准号: 411270-2010
• 负责人: Dimitrakopoulos, RoussosGeorgios
• 金额: $ 10.6万
• 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: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=573417
• 关键词: Developing; New; Global; Stochastic; Optimization

Modelling and optimization techniques have become a standard core aspect of mine design and production scheduling(MDPS) because they maximize the economic value contributed by ore production from a mine and define a technical plan to be followed from a mine's development to its closure. MDPS optimization is a complex problem to address due to its large scale, the unavailability of a truly optimal net present value (NPV) solution, uncertainty in the key parameters involved (geological/mining, financial, environmental) and the absence of a method for global or simultaneous optimization of the individual elements of a mining complex. Over the past several years, developments in stochastic methods that integrate and manage uncertainty in MDPS, in particular geological uncertainty, show substantial increases in NPV and metal production. To take these developments to the next level, research efforts need to focus on developing optimization that integrates uncertainty in a global sense and simultaneously consider all elements of a mining complex. . Based on our past research outcomes, global optimization of mining complexes will be addressed through the development of two complementary elements: (i) a new stochastic combinatorial optimization framework for MDPS that integrates multiple mines, material types, ore/waste processing streams and generates different product specifications suitable for a diverse group of commodities and mining complexes; and (ii) new 'high-order' spatial mathematical models of uncertainty for multiple material types generating inputs for (i), and suitable for modelling complex non-linear, non-Gaussian geology and spatial architectures. The proposed research aims to contribute new methods to the Canadian and global mining industry that will change the way problem-solving in the field is approached and impact on: (a) risk management and maximization of return on investment; (b) economic performance and sustainability; (c) enhancement of production and product supply; (d) objective and technically defendable decision-making; and (e) training highly qualified personnel.

建模和优化技术已成为矿山设计和生产调度（MDPS）的标准核心方面，因为它们最大限度地提高了矿山矿石生产的经济价值，并确定了从矿山开发到关闭的技术计划。MDPS优化是一个复杂的问题，以解决由于其规模大，一个真正的最佳净现值（NPV）解决方案的不可用性，在所涉及的关键参数（地质/采矿，金融，环境）的不确定性和缺乏一个方法的全局或同时优化的单个元素的采矿综合体。 在过去的几年中，随机方法的发展，整合和管理MDPS的不确定性，特别是地质的不确定性，净现值和金属产量大幅增加。为了将这些发展提升到一个新的水平，研究工作需要专注于开发优化，在全球范围内整合不确定性，同时考虑采矿综合体的所有要素。. 根据我们过去的研究成果，将通过开发两个互补要素来解决采矿综合体的全球优化问题：㈠ MDPS的新随机组合优化框架，该框架整合了多种矿山、材料类型、矿石/废物加工流程，并产生适合各种商品和采矿综合体的不同产品规格;和（ii）新的“高阶”空间数学模型的不确定性为多种材料类型生成输入（i），并适合于模拟复杂的非线性，非高斯地质和空间架构。拟议的研究旨在为加拿大和全球采矿业提供新的方法，这些方法将改变解决该领域问题的方法，并影响到：（a）风险管理和投资回报最大化;（B）经济业绩和可持续性;（c）提高生产和产品供应;（d）客观和技术上可行的决策;（e）培训高素质的人员。