Multivariate Statistical Batch Process Monitoring Using Dynamic Independent Component Analysis

Use of independent component analysis (ICA) in developing statistical monitoring charts for batch processes has been reported previously. This paper extends the previous work by introducing time lag shifts to include process dynamics in the ICA model. Comparison of the dynamic ICA based method with other batch process monitoring approaches based on static ICA, static principal component analysis (PCA) and dynamic PCA is made for an industrial batch polymerization reactor and a simulated fed-batch penicillin fermentation process. For all the faults studied, the dynamic ICA method was found to be the only approach that detected all the faults, and it detected the faults earlier with less ambiguity than other approaches.