Abstract: On the basis of particle swarm optimization（PSO）algorithm and support vector machine（SVM），the PSO algorithm with last out mechanism was applied to optimize the parameters of SVM. Then，a mutant ICPSO-LSSVM model for predicting soft sensor of the evaporation process in alumina production was constructed and compared with PSO-LSSVM and LSSVM models. Results illustrated that ICPSO-LSSVM is featured with more accuracy and better generalization ability and performance，with which samples with prediction relative error less than 5% are as high as 92.5%，the maximal relative error is only 8.1%，and the MSE is 0.05153. The implement experimental results indicated that the model with more accuracy can basically realize the real-time and online estimation for output concentration.

摘要： 在分析混沌粒子群优化算法（CPSO）和最小二乘支持向量机（SVM）理论基础上，以某氧化铝厂蒸发过程为对象，采用带有末位淘汰机制的混沌粒子群优化算法优化支持向量机的参数，建立了基于变异CPSO算法的LS-SVM的氧化铝蒸发过程软测量模型，并与PSO-LSSVM、LSSVM模型比较，研究表明，ICPSO-LSSVM模型预测准确，泛化性能好，且该模型预测结果中相对误差小于5%的样本达到92.5%，最大相对误差仅为8.1%，均方差MSE为0.05153，模型具有较高的精度，其现场实施结果表明基本可以实现出口浓度的实时在线预估。