A sequential quality prediction algorithm based on information increment matrix is proposed for multi-phase batch processes. It can overcome the limits of some phase partition algorithms

which cannot cope with the sequence and dynamics of the processes and may inevitably divide the samples with discontinuous time sequence but similar characteristics into the same phase. First

the 3D data is transformed into 2D data by batch-wise unfolding and splitted into extended time slices that equipped with quality variables. Then a sliding window is used to divide the sub-phases according to information increment of extended time slices. PLS models of each sub-phase constitute the global quality prediction strategy. The proposed algorithm takes the correlations among variables into consideration and uses information increment to capture the dynamics. The feasibility and effectiveness of the proposed algorithm are illustrated by a penicillin simulation platform and an industrial application of E. coli fermentation

respectively.