金属矿山组合品位的智能系统优化  

Intelligent System Optimization for Combinational Grades in Metal Mine

作　　者：诸克军[1] 郭海湘[1,2] 高艳卉[1] 王德银[1] 

机构地区：[1]中国地质大学经济管理学院,武汉430074 [2]西安交通大学管理学院,西安710049

出　　处：《系统管理学报》2010年第5期509-513,共5页Journal of Systems & Management

基　　金：国家自然科学基金资助项目(70573101);中国博士后基金资助项目(20090461293);中国地质大学(武汉)资源环境经济研究中心开放基金资助项目(2009B012)

摘　　要：合理确定的截止品位与入选品位关系到企业的经济效益和资源的可持续性利用,有着重要的理论意义与明显的应用价值。根据经济学＂边际收益与边际成本相等时利润最大＂,采用随机模拟,神经网络,模糊系统和遗传算法集成为智能系统找出使收益净现值最大时的截止品位和入选品位。经大冶铁矿实际应用15个月,不仅使总收益净现值增加166.7~171万元,而且多利用矿石21 979.6吨。Optimal choice of cut-off grade and beneficiation feed grade in mining influences economic effectiveness of enterprises and resource sustainability,and is of theoretical and practical significance.According to the classical economic profit maximization theory（profit reaches the maximum when marginal revenue equals marginal cost）,this paper integrates stochastic simulation,neural network,fuzzy system and genetic algorithm into an intelligent system to find the cut-off grade and beneficiation feed grade that maximizes the net benefit in present value.Not only an increase of NPV of 1667~1710 thousand Yuan was achieved,but also 21 979.6 tones more ores were used,due to application result of Daye mine for 15 months.

关 键 词：智能优化 截止品位 入选品位 

分 类 号：F830.49[经济管理—金融学]