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Operating Performance Assessment Based on Performance-Relevant Independent Component Analysis for Non-Gaussian Batch Processes

As an effective way to ensure the economic benefits of enterprises, the process operating performance assessment has attracted more and more attentions from industry and academia in recent years. In this paper, a operating performance assessment method based on performance-relevant independent component analysis (PR-ICA) is proposed for multiphase batch processes with non-Gaussian distribution. To identify the phases with different characteristics, a new PR-ICA based phase division method is developed first, which can not only ensure that each phase has consecutive samplings but also retains the specific performance-relevant variation information of different phases. Then the assessment models of each phase are established based on PR-ICA with the separated process data after offline phase division. Furthermore, the similarity between the performance-relevant variation information of the online data and that of the corresponding phase is calculated and used to define the assessment index for online assessment. For the nonoptimal operating performance, it is to identify the responsible cause variables through the variable contribution rate based nonoptimal cause identification method. Finally, the effectiveness of the proposed assessment method is demonstrated through the fed-batch penicillin fermentation process.