Goodfellow Ryan, Dimitrakopoulos Roussos
COSMO - Stochastic Mine Planning Laboratory, McGill University, 3450 University Street, Montreal, QC H3A 0E8 Canada.
Math Geosci. 2017;49(3):341-360. doi: 10.1007/s11004-017-9680-3. Epub 2017 Mar 2.
Recent developments in modelling and optimization approaches for the production of mineral and energy resources have resulted in new simultaneous stochastic optimization frameworks and related digital technologies. A mining complex is a type of value chain whereby raw materials (minerals) extracted from various mineral deposits are transformed into a set of sellable products, using the available processing streams. The supply of materials extracted from a group of mines represents a major source of uncertainty in mining operations and mineral value chains. The simultaneous stochastic optimization of mining complexes, presented herein, aims to address major limitations of past approaches by modelling and optimizing several interrelated aspects of the mineral value chain in a single model. This single optimization model integrates material extraction from a set of sources along with their uncertainty, the related risk management, blending, stockpiling, non-linear transformations that occur in the available processing streams, the utilization of processing streams, and, finally, the transportation of products to customers. Uncertainty in materials extracted from the related mineral deposits of a mining complex is represented by a group of stochastic simulations. This paper presents a two-stage stochastic mixed integer nonlinear programming formulation for modelling and optimizing a mining complex, along with a metaheuristic-based solver that facilitates the practical optimization of exceptionally large mathematical formulations. The distinct advantages of the approach presented herein are demonstrated through two case studies, where the stochastic framework is compared to past approaches that ignore uncertainty. Results demonstrate major improvements in both meeting forecasted production targets and net present value. Concepts and methods presented in this paper for the simultaneous stochastic optimization for mining complexes may be adopted and applied to the optimization of smart oil fields.
矿产和能源资源生产的建模与优化方法的最新进展催生了新的同步随机优化框架及相关数字技术。采矿综合体是一种价值链类型，即从各种矿床中开采出的原材料（矿物）利用现有的加工流程转化为一系列可销售产品。从一组矿山开采的物料供应是采矿作业和矿产价值链中不确定性的主要来源。本文提出的采矿综合体同步随机优化旨在通过在单一模型中对矿产价值链的几个相互关联的方面进行建模和优化，来解决以往方法的主要局限性。这个单一的优化模型整合了从一组来源进行的物料开采及其不确定性、相关风险管理、混合、储存、现有加工流程中发生的非线性转换、加工流程的利用，以及最终将产品运输给客户。从采矿综合体相关矿床开采的物料的不确定性由一组随机模拟表示。本文提出了一种两阶段随机混合整数非线性规划公式，用于对采矿综合体进行建模和优化，以及一种基于元启发式算法的求解器，便于对超大数学公式进行实际优化。通过两个案例研究展示了本文提出方法的显著优势，将随机框架与忽略不确定性的以往方法进行了比较。结果表明，在实现预测生产目标和净现值方面都有重大改进。本文提出的用于采矿综合体同步随机优化的概念和方法可被采用并应用于智能油田的优化。
