Stage-based soft-transition multiple PCA modeling and on-line monitoring strategy for batch processes

For the hard-partition and misclassification problems of stage-based sub-PCA modeling method, a new STMPCA (soft-transition multiple PCA) modeling method is introduced in this article to overcome these disadvantages. The method is based on the idea that process transition can be detected by analyzing changes in the loading matrices, which reveal evolvement of the underlying process behaviours. By setting a series of multiple PCA models with time-varying covariance structures, it reflects objectively the diversity of transitional characteristics and can preferably solve the stage-transition monitoring problem in multistage batch processes. The superiority of the proposed method is illustrated by applying it to both the real three-tank system and the simulation benchmark of fed-batch penicillin fermentation process with more reliable monitoring charts. Both results of real experiment and simulation clearly demonstrate the effectiveness and feasibility of the proposed method, which detects various faults more promptly with desirable reliability.