基于BP神经网络的球磨机磨矿浓度的预测分析  

Predictive analysis of ground pulp density of ball mill based on BP neural network

作　　者：李娟娟[1] 于淳[2] 郭纲[1] 杨东平[1] 

机构地区：[1]空军航空大学,吉林长春130022 [2]中国人民解放军装甲兵技术学院,吉林长春130117

出　　处：《节能》2010年第8期20-22,共3页Energy Conservation

基　　金：鞍钢集团矿业公司科技开发项目(项目编号:2009-科A30)

摘　　要：对球磨机磨矿过程的能耗进行分析,并利用灰色系统关联度分析法分析影响能耗因数,找出主要影响因数,通过BP神经网络建立球磨机磨矿浓度预测模型对磨矿浓度进行预测,对于降低球磨机能耗、解决冶金企业能耗问题具有一定的指导意义。Ball mill grinding process on the energy analysis and using of gray correlation analysis of factors affecting energy consumption are introduced.The major influencing factors in reducing the energy consumption of ball mill were identified.In order to forecast ground pulp density by the minute,the prediction model of ground pulp density established based on BP neural network,There is some guidance to resolve the issue metallurgy enterprise energy consumption.

关 键 词：球磨机 能耗分析 磨矿浓度 BP神经网络 

分 类 号：TD928.9[矿业工程—选矿]