基于LS-SVM的浮选药剂量优化设定

针对矿物浮选在线检测X荧光分析仪缺失、人工检测严重滞后的问题,提出基于最小二乘支持向量机(Least Squares Support Vector Machine,LS-SVM)的浮选药剂量优化设定方法。该方法首先利用历史数据建立基于LS-SVM的药剂量优化模型,然后采用该模型实现浮选药剂量的优化设定。工业数据仿真结果表明,所提方法能够实现浮选生产过程的的指标要求。

LS-SVM Based Optimal Setting of Reagent Dosage for Flotation

X-ray fluorescence analyzer is in deficiency and manual detection results in large time delay in present online detection of mineral flotation. Aiming at these issues, a novel optimal setting of reagent dosage based on least squares support vector machine (LS-SVM) is proposed. Through obtaining a large amount of history process data from flotation running, LS-SVM is used to implement optimal setting of reagent dosage for flotation process. The simulation results based on experiment data prove the effectiveness of the proposed method.