Improved monitoring and discrimination of batch processes using correspondence analysis

Correspondence analysis (CA) has recently been proposed as a superior alternative to PCA for the tasks of monitoring and early event detection. Some of the key merits of CA include improved discrimination due to the inherent nonlinear scaling as well as the ability to capture and elegantly represent the joint variable-sample associations, which make it amenable to accommodate serial correlations in the data. In this paper, we propose an extension of the CA algorithm to address some of the key problems associated with monitoring of batch processes. The task of batch process supervision is fraught with problems of serial nonlinear correlations in addition to the need to accommodate variable run lengths of the batches. It is shown in the paper that the differential weighting of points in CA offers a unique advantage of representation of the row (sample) and column (variable) points on the same plot ("Bi-plot"), which reveals the prevailing association structure between them. This joint display of observations and variables facilitates the fault diagnosis step and helps to discriminate the batches based on their performance. The paper also uses the traditional DTW for batch synchronization and a simple Euclidean distance based future data filling method during on-line monitoring. The efficacy of the proposed offline and online monitoring method is validated through a simulation study of a penicillin fed-batch fermentation process.