Statistical monitoring of fed-batch process using dynamic multiway neighborhood preserving embedding

A multivariate statistical process control (MSPC) method using dynamic multiway neighborhood preserving embedding (DMNPE) is proposed for fed-batch process monitoring. Different from principal component analysis (PCA) which aims at preserving the global Euclidean structure of the data set, neighborhood preserving embedding aims to preserve the local neighborhood structure of the data set. The neighborhood preserving property enables NPE to find more meaningful intrinsic information hidden in the high-dimensional observations compared with PCA. Moreover, the robustness of NPE is better than that of PCA. On the other hand, a dynamic monitoring approach based on moving window technique is employed to deal with the time-variant property of the dynamic processes. An industrial cephalosporin fed-batch fermentation process is used to demonstrate the performance of the DMNPE. The results show the advantages of DMNPE over those methods such as dynamic multiway PCA (DMPCA), static multiway NPE (SMNPE) and static multiway PCA (SMPCA) in fed-batch process monitoring. Finally, the robustness of the DMNPE monitoring is tested by adding noises to the original data sets.