Quality relevant over-complete independent component analysis based monitoring for non-linear and non-Gaussian batch process

Abstract

A large number of multivariate statistical methods have been applied to process monitoring, but conventional methods only extract limited feature information that often cannot effectively monitor the quality related changes of production characteristics in batch processes. In order to improve the effect of the process monitoring, this paper proposes a batch process monitoring method based on Multistage Over-complete Independent Component Analysis (OICA) algorithm. Firstly, the Affinity Propagation algorithm (AP) is used to divide the batch production process. Secondly, the extra quality information extracted by Partial Least Squares (PLS) algorithm is input into OICA algorithm. Finally, a monitoring model is established for process monitoring in each sub-stage. The effectiveness of the proposed method has been verified by comparing with the conventional methods in the fed-batch penicillin fermentation process.