基于AWLS-SVM的污水处理过程软测量建模

针对污水处理过程建模中样本数据可能存在的测量误差对模型性能的影响,提出一种自适应加权最小二乘支持向量机(AWLS-SVM)回归的软测量建模方法。该方法基于最小二乘支持向量机模型,根据样本拟合误差,并结合改进的指数分布赋权规则,自适应地为每个建模样本分配不同的权值,以降低随机误差对模型性能的影响;同时采用一种全局优化算法——混沌粒子群模拟退火(CPSO-SA)算法对最小二乘支持向量机的模型参数进行优化选择,以提高模型的泛化能力。仿真实验表明,AWLS-SVM模型的预测精度及鲁棒性能优于LS-SVM和WLS-SVM。最后,应用AWLS-SVM方法建立污水处理过程出水水质关键参数的软测量模型,获得了较好的效果。

Soft sensor modeling for wastewater treatment process based onadaptive weighted least squares support vector machines

Aiming at the problem that the presence of outliers in sample data can corrupt the model performance, which leads to undesirable results, a soft sensor modeling method, i.e. the adaptive weighted least squares support vector machine (AWLS SVM) regression method is presented for the modeling of wastewater treatment process. Firstly, in AWLS SVM, the least square support vector machine regression method is employed on the sample data to develop the model and obtain the sample datum fitting error. Secondly, according to the fitting error, a weight is adaptively assigned to each modeling sample via the improved exponential distribution weighting scheme to reduce the influence of random error on model performance. Then, a global optimization algorithm, i.e. the hybrid chaos particle swarm optimization simulated annealing (CPSO SA) algorithm is adopted to select the optimal model parameters of the LS SVM and improve the generalization capability of the model. The simulation experiment results show that the influence of the outliers on the model performance is eliminated in AWLS SVM, and the prediction performance and robustness of the AWLS SVM model are better than those of WLS SVM and LS SVM methods. Furthermore, the AWLS SVM method was applied to develop the soft sensor model for sewage disposing effluent quality in wastewater treatment process, and satisfactory result is obtained.