Multiple dynamic kernel clustering based online monitoring for batch processes

Since weak faults induced by large fluctuations under poor initial conditions could not be effectively detected by traditional multivariate statistical monitoring methods, a novel kernel principal component analysis monitoring strategy based on multiple dynamic kernel clustering (DKCPCA) was proposed to improve weak faults detection performance for multi-stage batch processes. The proposed method firstly combined auto-regressive moving average exogenous time series model and kernel principal component analysis (KPCA). The dynamic kernel PCA model was built for each batch in each stage. Then hierarchical clustering was implemented through load matrix similarity among batch models. Finally, the batch data belonging to the same cluster were unfolded to build dynamic kernel PCA model again. The multiple models were established along with different cluster numbers. When online monitoring, multiple model selection strategy was given to improve monitoring precision. The monitoring method was applied to fault detection for benchmark of fed-batch penicillin production. The monitoring results showed that the proposed method had better performance than DKPCA and MKPCA. ©, 2014, Chemical Industry Press. All right reserved.