Quantitative methods for sustainability analysis and integrated management of mining systems

矿山开发项目是资本密集和有风险的，因此必须分阶段设计。早期阶段的预算相对较少，并决定是否追求需要更大预算的后续阶段。这种多相方法对可持续工程至关重要，被称为前端加载（FEL）。它要求对经济、社会和环境影响进行全系统的定量分析，在每一阶段内加以应用，并须达到多目标最优化；它决定最终设计是否应该实现。此外，如果设计得到实际实施，定量分析将成为对所产生的采矿系统进行综合和可持续管理的基础。

Mine development projects are capital intensive and risky, and must therefore be engineered in phases. Early phases have relatively small budgets, and determine whether or not to pursue the subsequent phases that require larger budgets. This multiphase approach is essential to sustainable engineering, and is known as Front End Loading (FEL). It requires system-wide quantitative analysis of economic, social and environmental impacts, applied within every phase, and is subject to multiobjective optimization; it determines if the final design should be implemented. Furthermore, if the design is actually implemented, the quantitative analysis becomes the basis for integrated and sustainable management of the resulting mining system. The current proposal is to develop the quantitative frameworks to evaluate modern control and automation strategies, which apply geostatistical and geometallurgical modeling, for system-wide coordination of the mineral value chain. Early within a project, the system may be represented with only the principle material and energy flows, as in standard Life-Cycle Assessment, supported by static Monte Carlo simulation. The more developed models require dynamic representations, supported by discrete event simulation; they can describe unplanned events, and/or surges in material and energy inputs, as well as environmental releases. It is ultimately the dynamic surging that presents environmental risks, whenever system tolerances are approached or exceeded. Variations in the orebody, equipment, and market conditions can trigger changes in the mode of operation. The standardization of operational practices ensures that coherent instructions are sent to the various unit operations throughout the mine, in response to events and trends. Moreover, operational data is continually accumulated, which supports process optimization through machine learning. For example, a change in the rock type may require a change in the blasting pattern, as well as corresponding (machine learnt) changes in the downstream comminution parameters. Alternating operational modes are thus an integrated response to process variation. However, the available modes are limited by the equipment that is selected, and must therefore be considered early within the design process. Considering the competitive nature of the mining industry, the sustainability of mines may indeed depend on the intelligent coordination of automated technology.