露天矿多目标配矿模型与优化算法研究

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英文篇名：Research of Multi-objective Ore Blending Model and Optimization Algorithm in Open-pit Mine 作者：顾清华 ; 孟倩倩 ; 卢才武 ; 马龙 英文作者：GU Qinghua;MENG Qianqian;LU Caiwu;MA Long;School of Management,Xian University of Architecture and Technology; 关键词：露天矿 ; 多金属 ; 多目标 ; 配矿 ; 自适应粒子群算法 英文关键词：Open-pit mine;;Multi metal;;Multi-objective;;Ore blending;;Adaptive particle swarm optimization algorithm 中文刊名：KYYK 英文刊名：Mining Research and Development 机构：西安建筑科技大学管理学院; 出版日期：2019-02-25 出版单位：矿业研究与开发 年：2019 期：v.39;No.223 基金：国家自然科学基金资助项目(51774228);; 陕西省自然科学基金项目(2017JM5043) 语种：中文; 页：KYYK201902004 页数：6 CN：02 ISSN：43-1215/TD 分类号：24-29

摘要

针对露天矿的多金属多目标短期配矿问题,提出了基于自适应粒子群算法的露天矿配矿优化方法。首先,结合矿山配矿实际生产要求和指标,构建了以运输功和配矿品位偏差最小为目标函数的多目标短期配矿模型。其次,在基本粒子群算法的基础上,采用Kent映射产生初始种群,使种群分布更加均匀;并将自适应概率引入到粒子群算法,提高了种群的多样性和算法的全局搜索能力。最后,以一个算例和三道庄露天矿配矿的实际数据为例,进行仿真验证,仿真结果表明:该优化方法适用于解决露天矿多金属多目标的配矿问题,可以有效降低配矿过程中的运输功和品位偏差。
        In view of the problem of multi metal and multi-objective short-term ore blending in open-pit mine,an optimization method for open-pit mine blending was put forward based on adaptive particle swarm optimization algorithm.Firstly,combined with the actual production requirements and indexes of ore blending,a multi-objective short-term ore blending model was constructed with the objective of minimizing the deviation of transport power and ore blending grade.Secondly,based on the basic particle swarm optimization algorithm,the initial population was generated by kent mapping,so that the initial population was evenly distributed.The adaptive probability was introduced into the particle swarm optimization algorithm,which improved the diversity of the population and the global search ability of the algorithm.Finally,Taking a numerical example and the actual data of three open-pit mines as an example,the simulation and verification were carried out.The simulation results showed that the ore blending model could effectively solve the problem of multi metal and multi-objective ore blending in open-pit mine,and effectively reduce the transport power and grade deviation during the ore blending process.

引文

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