Inner-phase and inter-phase analysis based operating performance assessment and nonoptimal cause identification for multiphase batch processes

Abstract

Batch processes play a significant role in modern industrial processes. Nevertheless, the process operating performance may degrade from optimal level, which cancels the economic profits of the plant, and effective techniques for operating performance assessment are essential. Although multimodel approaches are proposed to fit its multiphase characteristic, the effect of combined action of multiple phases on the operating performance of the overall batch, which is very important for operating performance assessment, is neglected. In this study, a novel inner-phase and inter-phase analysis based operating performance assessment and nonoptimal cause identification strategy is proposed to overcome it. The key characteristic of the proposed method is that the inter-phase assessment models are developed based on the inner-phase assessment models of each phase, which takes the correlations and interactions between phases into consideration and reveals the combined effect of multiple phases on the operating performance of the overall batch. Furthermore, online local and global assessments are performed to master the operating performance from different perspectives and improve the algorithm performance. Possible cause variables can be determined by variable contributions under nonoptimal level. The effectiveness of the proposed methodology is demonstrated through a fed-batch penicillin fermentation process and a injection molding process.