Multivariate statistical process control of an industrial-scale fed-batch simulator. (4th January 2020)

• Record Type: Journal Article
• Authors: Duran-Villalobos, Carlos A.; Goldrick, Stephen; Lennox, Barry
• Is Part Of: Computers & chemical engineering. Volume 132(2020)
• Publication Date: 2020-01-04
• Subjects: Optimal control -- Batch to batch optimisation -- Model predictive control -- Data-driven modelling -- Missing data methods -- Partial least square regression; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2019.106620
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: The proposed B2B strategy improved yield. The proposed MPC strategy improved consistency. The performance was similar for the PMP or TSR missing data algorithms. Results were improved using a bootstrap calculation for the validity constraints. Abstract: This article presents an improved batch-to-batch optimisation technique that is shown to be able to bring the yield closer to its set-point from one batch to the next. In addition, an innovative Model Predictive Control technique is proposed that over multiple batches, reduces the variability in yield that occurs as a result of random variations in raw material properties and in-batch process fluctuations. The proposed controller uses validity constraints to restrict the decisional space to that described by the identification dataset that was used to develop an adaptive multi-way partial least squares model of the process. A further contribution of this article is the formulation of a bootstrap calculation to determine confidence intervals within the hard constraints imposed on model validity. The proposed control strategy was applied to a realistic industrial-scale fed-batch penicillin simulator, where its performance was demonstrated to provide improved consistency and yield when compared with nominal operation.