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

在过去十年中，引入了采矿综合体或矿物价值链的概念，以反映一个综合系统，该系统管理从一组矿山中提取材料，然后通过不同的相互关联的加工设施处理提取的材料。该系统产生可销售的产品，交付给客户和/或现货市场。采矿综合体和有关研究有助于矿物资源和储备的可持续发展，确保我们所依赖的原材料和金属的持续供应，同时管理环境方面的问题。采矿综合设施的技术方面和组成部分受到多种来源的不确定性（随机性）的重大影响。这些范围从资产的估值到他们的经营业绩，包括他们适应内源性和外源性变化的能力。与不确定性相关的影响与从开采到运输的众多决策相结合。这个综合系统的主要不确定性来源包括矿山生产的材料的质量和数量（供应不确定性）和金属的现货市场价格（需求不确定性）。鉴于新的技术发展，必须处理这些不确定因素，并吸收采矿综合设施运作时收集到的新信息，包括从各种传感器收集到的信息。这需要进行评估，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已经产生了实质性的新发展，以应对这些挑战。这已经改变了相关的范式，并促成了一个新的框架，称为采矿复合体的同步随机优化（SSOMC）。它还产生了新的随机优化模拟方法，这些方法集成和管理不确定性，包括但不限于地质不确定性，同时最大限度地提高生产率，延长资产寿命，增加投资回报。新的框架共同优化了采矿综合设施的组成部分，包括矿山生产时间表、混合、储存、材料加工流的各个方面和确定采矿系统关键瓶颈的资本投资。这些贡献支持了新一代智能风险管理技术的发展。拟议的5年研究计划旨在以我们以前的工作为基础，开发出更先进的方法。它打算通过以下方式增加对一种新的随机自我学习、建模和优化框架及相关技术的理解：为SSOMC开发一种全面的供应满足需求的方法来应对披露信息；将空间不确定性的高阶量化扩展到统计学习；探索不同时间尺度联合优化的智能方法，同时解决多源数据同化问题。

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.