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，因为它们最大化了矿山矿石生产所贡献的经济价值，并定义了从矿山开发到关闭所遵循的技术计划。 MDPS 优化是一个需要解决的复杂问题，因为其规模庞大、无法获得真正最优的净现值 (NPV) 解决方案、所涉及的关键参数（地质/采矿、金融、环境）的不确定性以及缺乏解决问题的方法。对采矿综合体的各个元素进行全局或同时优化。 在过去的几年中，整合和管理 MDPS 不确定性（特别是地质不确定性）的随机方法的发展表明 NPV 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，研究工作需要集中于开发优化，以整合全局意义上的不确定性，并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，采矿综合体的全局优化将通过开发两个互补要素来解决：（i）新的 MDPS 随机组合优化框架，该框架集成了多个矿山、材料类型、矿石/废物处理流并生成不同的适用于多种商品和采矿综合体的产品规格； (ii) 针对多种材料类型的不确定性的新“高阶”空间数学模型，生成 (i) 的输入，并且适合对复杂的非线性、非高斯地质和空间结构进行建模。拟议的研究旨在为加拿大和全球采矿业贡献新方法，这将改变该领域解决问题的方式，并影响： (a) 风险管理和投资回报最大化； (b) 经济绩效和可持续性； (c) 加强生产和产品供应； (d) 客观且技术上可辩护的决策； (e) 培训高素质人才。