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Empirical mill throughput modelling and linear programming for blend optimisation at the Phu Kham copper-gold operation, Laos

机译:老挝PHU KHAM铜金运行中混合优化的经验磨机吞吐量建模与线性规划

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PanAust's Phu Kham copper-gold operation (Phu Kham) in Laos is currently limited by throughput in the Semi Autogenous Grinding (SAG) mill. The ability to accurately predict SAG mill throughput (throughput) is therefore critical to accurately forecast copper and gold production. During the fourth quarter of 2016 the proportion of hard unweathered rocks in the mill feed increased, resulting in a period of lower than expected throughput. These low throughput rates prompted a study with the aim of identifying a practical solution for throughput predictions. The study identified an empirical modelling approach based on actual SAG mill throughput rates. The empirical model was then optimised by linear programming to identify the ideal feed blend of unweathered rocks to achieve a maximum SAG mill throughput. Rock strength was the initial focus of the throughput study at Phu Kham because previous studies had identified a strong link between rock strength and throughput. It was determined that there was insufficient valid data to adequately model but sufficient data to qualitatively characterise rock strength. The data identified that the weathering and lithology had a controlling influence on the rock strength. Complicating throughput predictions were operational improvements (changes to blasting and mill settings) implemented in response to the lower than expected throughput. This created uncertainty with the throughput equations. A self-learning empirical modelling method was developed to predict the throughput by incorporating fundamental rock properties with operational practices and improvements. The method is based on the proportion of mill feed with similar weathering and lithology (a proxy for comminution performance) and the actual mill throughput using the SAG mill as the analytical instrument. The modelling method simultaneously incorporated the sum and interaction of unmodelled influences including blasting, crushing and mill settings. The empirical modelling method worked at Phu Kham because the lithology and weathering has an intuitive and observable controlling influence on SAG mill throughput. The modelling method is described along with a worked example of feed blend optimisation using linear programming.
机译:Laos的Phu Kham铜金运营(Phu Kham)目前受到半自动研磨(SAG)磨机中的产量的限制。因此,可以准确地预测落下轧机吞吐量(吞吐量)是至关重要的,以准确地预测铜和金生产。在2016年第四季度,轧机饲料中硬暖的岩石的比例增加,导致吞吐量低于预期的时期。这些低吞吐率促使旨在识别吞吐量预测的实用解决方案的研究。该研究确定了一种基于实际SAG轧机吞吐率的实证建模方法。然后通过线性规划优化了经验模型,以确定未曝气岩石的理想饲料混合物,以实现最大的落下磨机吞吐量。岩石力量是Phu Kham吞吐量研究的初步焦点,因为之前的研究已经确定了岩石强度与吞吐量之间的强烈联系。确定有效数据不足以充分模型,但足够的数据以定性表征岩石强度。数据发现风化和岩性对岩石强度的控制影响。复杂的吞吐量预测是响应低于预期的吞吐量而实施的运营改进(对爆破和磨机设置的变化)。这与吞吐量方程创建了不确定性。开发了一种自学习实证建模方法,通过将基本岩石特性与运营实践和改进纳入基础岩石性能来预测吞吐量。该方法基于轧机饲料的比例,具有类似的风化和岩性(粉碎性能代理)和使用SAG磨机作为分析仪器的实际研磨吞吐量。建模方法同时结合了未掩模的影响的总和和相互作用,包括喷砂,破碎和研磨装置。经验模型方法在Phu Kham工作,因为岩性和风化具有直观和可观察到的对SAG Mill产量的控制影响。使用线性编程描述了建模方法以及使用线性编程的饲料混合优化的实例。

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