On-line Monitoring of Batch Processes Using Kalman Filter and Multivariate Statistical Methods

Multiway principal component analysis (MPCA) has been implemented to batch process monitoring widely and effectively. In general, when applying MPCA method for on-line monitoring, the unknown future data from the current time until the end of the batch have to be estimated, but it is always difficult to foresee the future behaviour precisely. In this paper, a novel method is proposed by using Kalman filter to recursively estimate the complete state of process and then using MPCA to detect abnormal batch runs. Effectiveness of the proposed method is validated on a simulated benchmark fed-batch penicillin fermentation process.