基于ICA算法的多金属矿山边界品位优化研究

Research on Optimization of Cut−off Grade in Polymetallic Mines Using Imperialist Competitive Algorithm

  • 摘要: 边界品位是矿山开采的一个重要决策参数,在多金属矿山开采项目中,确定合理的边界品位是为后续开采获取更大经济效益的基础。针对某矿山采选二阶段生产流程,以最大净现值法为基础,利用综合品位构建了基于ICA算法的多金属矿山边界品位动态优化模型,实现了该银铅矿最佳边界品位的动态确定。实例应用表明:该模型适用于多金属矿山边界品位的确定。在矿山寿命期间,通过ICA算法所确定的银铅多金属最佳边界品位为2.619%,后期下降至1.331%,矿山总净现值为127457.53万元;对比Lane法,该模型具有全局搜索能力,对矿山后期边界品位指标的动态优化更具优势,为矿山确定合理的边界品位指标提供了新思路。

     

    Abstract: Cut−off grade is an important decision parameter in mining operations. In multi metal mining projects, determining a reasonable cut−off grade is the foundation for obtaining greater economic benefits for subsequent mining. Based on the maximum net present value method, a dynamic optimization model for the comprehensive cut−off grade of a polymetallic mine based on ICA algorithm was constructed for the two−stage production process of mining and selection. The optimal cut−off grade of a certain silver lead mine was dynamically determined. The example application shows that the model is suitable for determining the cut−off grade of polymetallic mines. During the lifespan of the mine, the optimal cut−off grade for a certain silver lead polymetallic material determined by the ICA algorithm was 2.619%, which later decreased to 1.331%. The total net present value of the mine was 1274.5753 million yuan; Compared with the Lane method, this model has global search ability and is more advantageous in dynamic optimization of cut−off grade indicators in the later stage of mining, providing new ideas for determining reasonable cut−off grade indicators in mines.

     

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