基于智能算法的经济合理放矿截止品位与入选品位  

Economic and Rational Cut-off Grade and Ore Dressing Grade Based on Intelligent Algorithm

作　　者：刘昶[1] 许梦国[1] 王平[1] 程爱平[1] 黄永材 商欢迪 LIU Chang XU Mengguo WANG Ping CHENG Aiping HUANG Yongcai SHANG Huandi(College of Resource and Environmental Engineering, Wuhan University of Science and Technology, Wuhan, Hubei 430081, China)

机构地区：[1]武汉科技大学资源与环境工程学院,湖北武汉430081

出　　处：《矿业研究与开发》2017年第6期39-43,共5页Mining Research and Development

摘　　要：随着矿石价格的不断变化以及矿山设备的不断更新,为了达到矿山经济效益的最大化,通过智能算法来优化放矿截止品位与入选品位。以程潮铁矿2015年的生产指标作为参数,建立金属回收率、成本与截止品位和入选品位的BP神经网络,确定它们之间的函数关系。通过遗传算法,在模糊理论的基础上以精矿量与利润两个经济指标构建综合隶属度模型,优选出经济合理的截止品位与入选品位组合。结论证明,程潮铁矿的截止品位选取为15.70%,入选品位选取为23.13%时,精矿量提升了8474.77t,利润增加了5709407.58元。With the constant change of the ore price and the continuous updating of the mining equipments,in order to maximize the economic benefits of mines, the cut-off grade and the ore dressing grade could be optimized by intelligent algorithm. Taking the production indexes in 2015 of Chengehao Iron Mine as parameters,a BP neural network between metal recovery and cost with cut-off grade and ore dressing grade was established, and their functional relationships were determined. By genetic algo- rithm,the comprehensive membership degree model of the two economic indexes including the concentrate quantity and the profit was built based on the fuzzy theory, so as to select the economic and rational combination of cut-off grade and ore dress- ing grade. The conclusion proved that the concentrate quantity was increased to 8474.77 tons and the profits were improved to 5709407.58 RMB when the cut-off grade was 15.70% and the ore dressing grade was 23.13% in Chengchao Iron Mine.

关 键 词：截止品位 入选品位 智能算法 经济效益 BP神经网络 

分 类 号：TD862[矿业工程—金属矿开采]