基于ICA-LSSVM的铜萃取混合模型

由于湿法冶金萃取生产过程中组分的质量浓度在线检测极其困难，因此提出了一种结合独立成分分析与支持向量机的数据建模方法，建立了铜萃取过程组分的质量浓度预测模型.首先利用独立成分分析方法对现场数据进行预处理，然后利用支持向量机建立分配比模型对预测模型中的必要参数进行辨识，从而增强了铜萃取过程组分质量浓度预测模型的准确性.通过萃余液铜质量浓度和反萃余液铜质量浓度的实验证明了该模型更有效、更精确，为进一步控制管理提供了理论基础.

Hybrid Model Based on ICA LSSVM for Copper Extraction

Due to the mass concentration of component was hard to measure on line in hydrometallurgy extraction process，so a data modeling method was proposed by combining independent component analysis with least squares support vector machine(ICA LSSVM)， which was used to establish the forecasting model of copper extraction.First， the independent component analysis was used to pretreat the field data. Then， the least squares support vector machine was used to established distribution ratio model which was used to identify the unknown parameter and enhance the accuracy of model. The simulation results showed that the models were practical and efficient， which could provide a theoretical basis for the control.