关键词: 支持向量机, 小波分析, 混沌粒子群算法, 离子浓度, 在线估计

Abstract: The influent ionic concentration of the second stage is an important factor for the operation optimization of cobalt removal purification process in zinc hydrometallurgy.With the difficulty in online measurement of the influent ionic concentration，a soft sensing model of ionic concentration is proposed based on the time series analysis of manual measurement results，using the Wavelet Analysis（WA） and Support Vector Machine（SVM）.The time series of ionic concentration are decomposed into multiple subsequences by using wavelet decomposition and these subsequences are reconstructed into each subspace in phase space.Then，the SVM model is built in every subspace and the parameters in SVM model are optimized by using the chaotic Particle Swarm（PSO） algorithm.Finally，the outputs of each subsequence model are synthesized as the soft sensing result of influent ionic concentration.The verified results of the production data show that the proposed model has a high precision and the samples with relative error less than 10% are up to 97.5%.It indicates that the online prediction precision can meet the technological requirement of practical production.

Key words: support vector machine, wavelet analysis, chaotic Particle Swarm Optimization（PSO）, ionic concentration, prediction online