Fault Detetion for Fermentation Process Based on Multiphase Dynamic PCA

In industrial manufacturing,most fermentation processes are inherently multiphase and uneven-length batch processes in nature.Based on different dynamic nonlinear characteristics of different fermentation phases,a new strategy is proposed by using multi-phase dynamic principal component analysis(PCA) for fermentation process monitoring.Using Gaussian mixture model(GMM) clustering arithmetic,fermentation process data are divided into several operation stages,since GMM is adopted to discriminate different operation modes.Then,run-to-run variations among different instances of a phase are synchronized by using dynamic time warping(DTW),and sub-phase dynamic PCA models are developed for every phase.Finally,the proposed method is applied to monitor both the industrial processes of fed-batch penicillin production and interleukin-2 production in recombinant E.coli.Results demonstrate that fewer false alarms and small fault detection delay are obtained and the algorithm is proved to be efficient.