基于能力露天矿山装载设备的性能测量（英文）

本研究的目的是建立一个综合的能力分析框架模型,以解决系统的危险因素对运输车队额定能力的影响。该模型是基于数据驱动的方法,考虑不同的场景,捕捉采矿设备的突发风险。概率风险评估(PRA)是通过关注故障事件的严重性和可维护性度量来量化生产能力的损失。配置离散事件模拟来表征采矿作业的标称能力。在此基础上,通过标称能力与实际能力的比较,对系统能力进行了分析。通过一个案例研究验证了本文的研究方法,收集了铲运机过去的运行维护现场数据。采用离散事件模拟方法对铲斗标称能力进行了估计,然后,通过开发PRA方法来评估不良场景的影响。研究结果为提高矿山生产能力提供了重要的启示。分析人员对处理高风险水平的关键因素有很好的判断,可以制定一个全面的维护计划来减少和控制损失。

Presentation of a multi-index clustering technique for the mathematical programming of block-cave scheduling

Long-term planning is one of the most important stages that determines the distribution of cash flows over the mine life and the feasibility of the project. However, it is not feasible in block caving to generate a production schedule that will provide optimal operating strategies without considering geotechnical constraints. This paper develops a mixed-integer linear programming(MILP) model to optimize the extraction sequence of drawpoints over multiple time horizons of block-cave mines with respect to the draw control systems. A multi-similarity index clustering technique to solve the MILP model in a reasonable time is also presented. Application and comparison of production scheduling based on the draw control system and clustering technique are illustrated using 325 drawpoints over 15 periods. The results show a significant reduction in the size of the MILP model, and in the time required to solve it.