Exploitation
Elham Lotfi; Javad Gholamnejad; Mehdi Najafi; Mohammad Sadegh Zamani
Abstract
In the context of open pit mining operations, long-term production scheduling faces significant challenges due to inherent uncertainties, particularly in commodity prices. Traditional mathematical models often adopt a single-point estimation strategy for commodity price, leading to suboptimal mine plans ...
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In the context of open pit mining operations, long-term production scheduling faces significant challenges due to inherent uncertainties, particularly in commodity prices. Traditional mathematical models often adopt a single-point estimation strategy for commodity price, leading to suboptimal mine plans and missed production targets. The simultaneous effect of commodity price uncertainty on the cut-off grade and long-term production scheduling is less considered. This paper introduces a novel model for optimizing open pit mine long-term production scheduling under commodity price uncertainty considering a dynamic cut-off grade strategy, based on a two-stage Stochastic Production Programming (SPP) framework. The presented model seeks to identify optimal mining block sequences, maximizing total discounted cash flow while penalizing deviations from production targets. To illustrate the model's efficiency, it was implemented in a copper mine. First, the Geometric Brownian Motion (GBM) model is used to quantify the future commodity price. Then, both deterministic and SPP models were solved using GAMS software. The results showed that the practical NPV obtained from the SPP model is approximately 3% higher than the DPP model, while all constraints are satisfied.
Exploitation
J. Gholamnejad; A. Azimi; M.R. Teymouri
Abstract
Stockpiling and blending play a major role in maintaining the quantity and quality of the raw materials fed into processing plants, especially the cement, iron ore and steel making, and coal-fired power generation industries that usually require a much uniformed feed. Due to the variable nature of such ...
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Stockpiling and blending play a major role in maintaining the quantity and quality of the raw materials fed into processing plants, especially the cement, iron ore and steel making, and coal-fired power generation industries that usually require a much uniformed feed. Due to the variable nature of such materials, they even come from the same source and the produced ores or concentrates are seldom homogeneous enough to be directly fed to the processing plant ore furnaces. Processing plants in iron ore mines need uniform feed properties in terms of each variable (in this work, iron phosphorous ratio and Fe content in magnetite phase) grade of ore, and therefore, homogenization of iron ore from different benches of an open pit or ore dumps has become an essential part of modern mine scheduling. When ore dumps are considered as an ore source, the final grade of the material leaving the dump to the blending bed cannot be easily determined. This difficulty contributes to mixing the materials of different grades in a dump. In this work, the ore dump elements were treated as normally distributed random variables. Then a stochastic programming model was formulated in an iron ore mine in order to determine the optimum amount of ore dispatched from different bench levels in open pit and also four ore dumps to a windrow-type blending bed in order to provide a mixed material of homogenous composition. The chance-constrained programming technique was used to obtain the equivalent deterministic non-linear programming problem of the primary model. The resulting non-linear model was then solved using LINGO. The results obtained showed a better feed grade for the processing plant with a higher probability of grade blending constraint satisfaction.
