深锥浓密机底流浓度预测与外部结构参数优化  

Underflow concentration prediction and external structure parameter optimization of deep cone thickener

作　　者：王新民[1] 张国庆[1] 赵建文[1] 李帅[1] 

机构地区：[1]中南大学资源与安全工程学院,长沙410083

出　　处：《重庆大学学报（自然科学版）》2015年第6期1-7,共7页Journal of Chongqing University

基　　金："十一五"科技支撑计划课题资助项目(2008BAB32B03)~~

摘　　要：针对困扰支持向量机(SVM)模型参数选择问题,结合遗传算法(GA),建立了深锥浓密机底流放砂浓度的GA-SVM预测模型,研究了不同结构参数状态下底流浓度的变化规律,进行了深锥浓密机的外部结构参数优化选择。以司家营铁矿为例,在最优底流放砂浓度为72%的条件下,经外部结构参数优化后的深锥浓密机锥高10m、锥角为30°,系统稳定可靠、底流连续均匀,动力荷载较同类矿山降低约15%,压耙停机故障降低80%。To overcome the difficulty of choosing appropriate external structure parameters for support vector machine（SVM）models,the genetic algorithm（GA）is introduced and a GA-SVM optimal prediction model of underflow concentration is built.The change laws of thickener underflow concentration are discussed under different parameters,and the structure parameters of deep cone thickener are optimized.Sijiaying iron mine is taken as an example,and the results show that with the optimal underflow concentration of 72%,the optimized external structure parameters of deep cone thickener are 10 m high and 30 degree cone.The optimized deep cone thickener in Sijiaying runs steady with continuous underflow concentration flowing.Compared with other similar thickeners,its energy load and fault probability are reduced by 15% and 80%respectively.

关 键 词：深锥浓密机 底流浓度 外部结构参数 支持向量机 遗传算法 

分 类 号：X753[环境科学与工程—环境工程]