基于聚类的多模型蒸发过程软测量建模

Mufti-model Soft Modeling Based on Clustering in Evaporation Process

基于多模型方法具有更强的鲁棒性和精度的思想,结合多效蒸发过程强耦合和高度非线性特征,提出来一种基于差分进化的模糊核聚类的多最小二乘支持向量机的软测量建模方法。该方法依据运用DE-KFCM(差分进化核模糊聚类)对不同工况下的数据样本进行聚类,得到的子集分别用LSSVM(最小二乘支持向量机)构建模型不同工况使用模糊核聚类算法对输入数据进行聚类划分,针对每个聚类子集用最小二乘支持向量机方法建立子模型,通过加权得到系统输出。以氧化铝蒸发过程为例,对出口料液浓度的进行软测量建模,获得了较好的实验结果。

Based on the idea approach based on multi-model with a stronger robustness and precision,combining with strong coupling and the highly nonlinear characteristics in multi-effect evaporation process, a soft sensor modeling method of fuzzy kernel clustering was proposed based on differential evolution and multi-squares support vector machines. The method made clustering samples under different conditions by use of DE-KFCM, each sub-models were constructed using LSSVM using fuzzy clustering algorithm to cluster the input data under different conditions, and finally get the system output by weighting. Taking alumina evaporation process as an example, for the soft sensor modeling of export of feed concentration, the better results have been obtained.