Optimization of the combined beneficiation scheme and increase in the performance of highly efficient refractory gold-bearing ores under development

Askarova, G Y; Shautenov, Mels; Nogaeva, Kulzhamal

doi:10.1088/1757-899x/1047/1/012036

Cited by 3 publications

(1 citation statement)

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“…For an individual mine, the characteristics of the mining ore, the processing technology, the equipment, and the agent can be assumed to be constant. In this case, there are relationships between ore concentration ratio and mining grade, between concentrate grade and mining grade, between concentrate grade and ore concentration ratio, and between concentrate grade and concentrate selling price [26][27][28][29][30]. These relationships are modeled by equations (10-12):…”

Section: ) Beneficiation Processmentioning

confidence: 99%

Overall Dynamic Optimization of Metal Mine Technical Indicators Considering Spatial Distribution of Ore Grade Using AADE

2022

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In optimizing the production of a metal mine, either the overall dynamic relations between technical indicators or the spatial distribution of the ore grade are usually considered, but few studies have considered both factors together. These two factors in combination have a greater effect on the optimization of mine production in terms of economic benefit and resource utilization than they do individually. We proposed an overall dynamic optimization model of technical indicators of metal mine production that considers the spatial distribution of the ore grade to better optimize the technical indicators and improve sustainable development of mineral resources. We incorporated an adaptive mutation strategy and adaptive control parameters into a differential evolution algorithm (AADE) in order to overcome the drawbacks of the differential evolution algorithm in solving this optimization model. The adaptive mutation strategy and adaptive control parameters were used to increase the rate of convergence and improve the search for a global maximum. To assess the performance of AADE, we used a real case and four test functions (the Sphere, Griewank, Rastrigin and Rosenbrock functions) in tests that compared AADE with a standard genetic algorithm, a standard differential evolution algorithm and the recently developed adaptive differential evolution algorithm. The results indicate that the optimization model we created is better aligned with mine production processes than current optimization models. In optimizing the technical indicators of metal mine production to maximize economic benefits, AADE performed significantly better than the other three algorithms tested in terms of convergence rate and global search ability. INDEX TERMSMetal mine, technical indicator, overall dynamic relation, ore grade distribution, AADE.

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