基于历史监测记录统计分析的尾矿库运行优化

I. Ezama, Santiago pastine, J. Pinheiro
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引用次数: 0

摘要

尾矿储存设施(TSF)的运行控制和触发行动响应计划通常会测量一些操作方面,以评估风险,并确保TSF不超出某些设计考虑。通常用于此目的的测量包括地形测量和信标定位,密度和湿度测量,以及放电特性等。对来自TSF的累积历史操作数据集进行统计分析,可以提高对所考虑参数的可变性水平的了解,并通过增加对不确定性和操作期间预期偏差的潜在理解,潜在地增强基于风险的决策(ICMM 2021)。在设计输入中包含概率数据集,允许基于性能的方法来管理TSF,考虑由性能目标定义的一系列场景,确定关键因素以及时识别它们,并纳入有效的纠正或“故障”控制测量,可以通过对TSF行为的连续预测来改进。采用这种方法是为了优化Cerro Vanguardia TSF的沉积策略,并延长其矿山寿命(LOM),允许更严格的建设规划和推迟几年的建设。这在当前的COVID-19应急期间带来了相当大的好处。对持续运行监测数据的统计分析被充分纳入运行设计优化,并根据“尽可能低的合理可行”(ALARP)的角度选择适当的风险承受水平。识别基于绩效的情景,并制定及时决策的计划,以预测合理的经济/财务规划,以及基于概率的事件选择,以触发及时的操作调整。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Operational optimisation of a tailings storage facility based on statistical analysis of historical monitoring records
Operational control and trigger action response plans for tailings storage facilities (TSFs) routinely measure a number of operational aspects to assess risk and ensure the TSF does not exceed certain design considerations. Measurements that are routinely used for this purpose include topographical surveys and beacon settlements, density and moisture measurements, and discharge characteristics, among others. A statistical analysis of the cumulative historical operational datasets from a TSF allows improved knowledge of the level of variability of the considered parameters, and potentially enhances risk-based decision-making by increasing the understanding of uncertainties and the potential for expected deviations during operations (ICMM 2021). The inclusion of a probabilistic dataset in the design inputs allows a performance-based approach to managing a TSF, considering a series of scenarios defined by performance objectives, determining key factors to identify them in a timely manner, and incorporating effective corrective or ‘failure’ control measurements, that can be improved with sequential forecasts of the TSF behaviour. This approach was undertaken to optimise the deposition strategy of the Cerro Vanguardia TSF and expand its life-of-mine (LOM), allowing for tighter construction planning and deferral of construction by several years. This had considerable benefits during the current COVID-19 contingency. Statistical analysis of ongoing operational monitoring data was adequately incorporated into operational design optimisation and to select an adequate level of risk tolerance based on an ‘as low as reasonably practical’ (ALARP) perspective. Performance-based scenarios were identified and plans for timely decision-making were established to forecast reasonable economical/financial planning, and a probability-based events selection to trigger timely operational adjustments.
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