Multi-model adaptive soft sensor modeling method using local learning and online support vector regression for nonlinear time-variant batch processes. (28th July 2015)

• Record Type: Journal Article
• Authors: Jin, Huaiping; Chen, Xiangguang; Yang, Jianwen; Zhang, Hua; Wang, Li; Wu, Lei
• Is Part Of: Chemical engineering science. Volume 131(2015)
• Page Start: 282
• Page End: 303
• Publication Date: 2015-07-28
• Subjects: Adaptive soft sensor -- Batch process -- Within-batch and between-batch time-variant changes -- Online support vector regression -- Bayesian ensemble learning -- Offset compensation; Chemical engineering -- Periodicals; Génie chimique -- Périodiques; Chemical engineering; Periodicals; Electronic journals; 660
• DOI: 10.1016/j.ces.2015.03.038
• Languages: English
• ISSNs: 0009-2509

Abstract

Abstract: Batch processes are often characterized by inherent nonlinearity, multiplicity of operating phases, and batch-to-batch variations, which poses great challenges for accurate and reliable online prediction of soft sensor. Especially, the soft sensor built with old data may encounter performance deterioration due to a failure of capturing the time-variant behaviors of batch processes, thus adaptive strategies are necessary. Unfortunately, conventional adaptive soft sensors cannot efficiently account for the within-batch as well as between-batch time-variant changes in batch process characteristics, which results in poor prediction accuracy. Therefore, a novel multi-model adaptive soft sensor modeling method is proposed based on the local learning framework and online support vector regression (OSVR) for nonlinear time-variant batch processes. First, a batch process is identified with a set of local domains and then the localized OSVR models are built for all isolated domains. Further, the estimation for a query data is obtained by adaptively combining multiple local models that perform best on the similar samples to the query point. The proposed multi-model OSVR (MOSVR) method provides four types of adaptation strategies: (i) adaptive combination based on Bayesian ensemble learning; (ii) online offset compensation; (iii) incremental updating of local models; and (iv) database updating. The effectiveness of the MOSVR approach and its superiority over traditional adaptive soft sensors in dealing with the within-batch and between-batch shifting dynamics is demonstrated through a simulated fed-batch penicillin fermentation process as well as an industrial fed-batch chlortetracycline fermentation process. Highlights: A novel multi-model adaptive soft sensor modeling method is proposed. The OSVR method is used for local modeling to enable incremental updating. Local models are adaptively combined based on Bayesian ensemble learning. Within-batch and between-batch shifting dynamics can be well addressed. Two case studies of nonlinear time-variant batch processes are reported.