On-line Fermentation Process Monitoring using Kalman Filter and Multiway Kernel Principal Component Analysis

• 1、College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124

Abstract：A new method was developed based on kalman filter(KF) and multiway kernel principal component analysis(MKPCA) for on-line batch process monitoring. Three-way batch data of normal batch process are unfolded batch-wise. Then KPCA is used to capture the nonlinear characteristics within normal batch processes and set up the more accurate model of monitored process. The on-line monitoring uses a Kalman filter which can estimate the entire trajectory of the current batch run. Comparison of the monitoring performance of the method with that of the traditional multiway principal component analysis(MPCA) method on a benchmark fed-batch penicillin fermentation process shows that the proposed method had better monitoring performance, and that fewer false alarms and small fault detection delay were obtained. In both off-line analysis and on-line batch monitoring, the proposed approach can effectively extract the nonlinear relationships among the process variables.

Keywords： automatic control technology multiway kernel principal component analysis kalman filter fermentation