Towards an integrated, self-learning stochastic mining complex framework and new digital technologies for the sustainable development of mineral resources

为矿产资源的可持续发展建立一个集成的、自学习的随机采矿复杂框架和新的数字技术

• 批准号: RGPIN-2021-02777
• 负责人: Dimitrakopoulos, Roussos
• 金额: $ 6.41万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=761083
• 关键词: Towards; integrated; self; learning; stochastic

Over the past decade, the concept of a mining complex or mineral value chain was introduced to reflect an integrated system that manages the extraction of materials from a group of mines, followed by the treatment of the extracted materials through different interconnected processing facilities. This system generates sellable products delivered to customers and/or the spot market. Mining complexes and related research contribute to the sustainable development of mineral resources and reserves that ensures the continued supply of raw materials and metals we rely upon, while managing environmental aspects. Technical aspects and components of a mining complex are substantially affected by uncertainties (stochasticity) stemming from multiple sources. These range from the valuation of assets to their operational performance and include their ability to adapt to endogenous and exogenous changes. Uncertainty-related effects are compounded with decision-making for a multitude of decisions from extraction to transportation. Critical sources of uncertainty of this integrated system include the quality and quantity of materials produced from the mines (supply uncertainty) and the metal's spot market price (demand uncertainty). Given new technological developments, it is important to address these uncertainties and assimilate new information collected as a mining complex operates, including from various sensors. This needs to be evaluated and used to update models, forecasts and further support complex decision-making. The applicant's ongoing research has generated substantial new developments to address these challenges. This has shifted the related paradigm and contributed to a new framework termed simultaneous stochastic optimization of mining complexes (SSOMC). It has also generated new stochastic optimization - simulation methods that integrate and manage uncertainty, including but not limited to geological uncertainty, while maximizing productivity, extending life of asset, and increasing return on investment. The new framework jointly optimizes components of a mining complex, including mine production schedules, blending, stockpiling, aspects of material processing streams and capital investments that define the critical bottlenecks in the mining system. These contributions support the development of a new generation of intelligent risk-management technologies. The proposed 5-year research program aims to build upon our previous work to develop the next level of state-of-the-art approaches. It intends to increase understanding of a new stochastic self-learning, modelling and optimization framework and related technologies by: developing an all-inclusive supply-meets-demand approach for SSOMC to respond to unveiling information; extending high-order quantification of spatial uncertainty to include statistical learning; and exploring intelligent approaches for jointly optimizing different time scales, while addressing multi-source data assimilation.

在过去的十年中，引入了采矿复合物或矿物质价值链的概念，以反映一个集成的系统，该系统通过不同的互连处理设施来管理一组矿山中提取材料的材料，然后处理提取的材料。该系统生成可销售给客户和/或现货市场的可销售产品。采矿综合体和相关研究有助于矿产资源和储量的可持续发展，以确保我们依靠的原材料和金属的持续供应，同时管理环境方面。采矿综合体的技术方面和组成部分基本上受到了多个来源的不确定性（随机性）的影响。这些范围从资产的估值到其运营绩效，包括它们适应内源性和外源性变化的能力。与不确定性相关的效果与决策有关从提取到运输的多种决策都复杂化。该集成系统的不确定性的关键来源包括矿山（供应不确定性）生产的材料的质量和数量，以及金属的现货市场价格（需求不确定性）。鉴于新的技术发展，重要的是要解决这些不确定性并吸收作为采矿综合体运行的新信息，包括来自各种传感器。需要对此进行评估，并用于更新模型，预测和进一步支持复杂的决策。申请人正在进行的研究产生了大量的新发展，以应对这些挑战。这改变了相关的范式，并有助于新的框架，该框架同时对采矿配合物（SSOMC）进行了随机优化。它还产生了新的随机优化 - 仿真方法，这些方法整合和管理不确定性，包括但不限于地质不确定性，同时最大程度地提高生产率，延长资产的寿命以及提高投资回报率。新框架共同优化了采矿综合体的组成部分，包括矿井生产计划，混合，储存，物料加工流的各个方面以及定义采矿系统中关键瓶颈的资本投资。这些贡献支持新一代智能风险管理技术的发展。拟议的5年研究计划旨在基于我们以前的工作，以开发新的最新方法。它打算提高对新的随机自学习，建模和优化框架以及相关技术的理解：为SSOMC开发一种包罗万象的供求方法，以响应发布信息；扩展对空间不确定性的高级定量，以包括统计学习；并探索智能方法，以共同优化不同的时间尺度，同时解决多源数据同化。