A novel stage-based KPLS-PLS monitoring and quality prediction approach for batch processes

A novel KPLS-PLS batch monitoring and quality prediction approach based on fuzzy clustering soft-partition is proposed to solve the stage-transition monitoring and prediction problem in multistage batch processes. The proposed method calculates firstly similarity indices between different time-slice data matrices of batch processes, then phase division algorithm is designed by fuzzy clustering based on the similarity index, following by a fuzzy membership grade transition identification step. By setting a series of KPLS and PLS models with time-varying covariance structures for transitions and steady phases, it reflects objectively the diversity of transitional characteristics, capture the nonlinear relationships among process variables of the transition and can monitor and predict batch processes more accurately and efficiently. The superiority of the proposed method is illustrated by applying it to industrial application of fed-batch penicillin fermentation process. The results clearly demonstrate the effectiveness and feasibility of the proposed method Streszczenie: Zaproponowano nową metodę KPLS ( kernel partial least squers) – PLS monitorowania i przewidywania wieloetapowych procesów wsadowych. Metoda oparta została o klastrowanie rozmyte, pozwala na wykrycie przejść między etapami i dokładniejsze przewidywanie przebiegu procesu przez uniknięcie wpływu nieliniowości. Wyższość proponowanej metody zilustrowano wykorzystując ją do badania przemysłowego procesu fermentacji wsadu pożywki penicyliny. Nowa etapowa metoda KPLS – PLS badania monitoringu i przewidywania jakości procesów wsadowych