Abstract: The multi-phases characteristics of the batch process directly influenced accuracy of multivariate statistical analysis process modeling. A fault detection method of multi-phases batch processes based on parallel factor analysis 2 (PARAFAC2) phase partition was presented for the multi-phases characteristics of the batch process. Firstly, a group of time-slice matrix models were built based on PARAFAC2, to get the control limits of time-slice matrices. Secondly, each time-slice matrix was added into the time-block matrices chronologically from the initial moment, and established models based on PARAFAC2 for the time-block matrices, to get the control limits of time-block matrices. Thirdly, the points of phase partition were found by evaluating the difference between the control limits of time-slice matrices and time-block matrices, then the optimal phase partition result was chosen according to the phase partition combination index(PPCI). Finally the MPCA models for each phase was built and the fault detection of batch process was realized. The proposed method preserved the three-way structural characteristics and data integrity of the batch process, considered chronological sequence in the actual operation of the batch process comprehensively, thus improved the accuracy of phase partition. The simulation experiments of penicillin fermentation process verified the effectiveness of the proposed method.

Key words: batch processes, phase partition, PARAFAC2, fault detection