Trajectory tracking of batch product quality using intermittent measurements and moving window estimation. (January 2015)

• Record Type: Journal Article
• Authors: Lopez-Montero, Eduardo B.; Wan, Jian; Marjanovic, Ognjen
• Is Part Of: Journal of process control. Volume 25(2015:Jan.)
• Page Start: 115
• Page End: 128
• Publication Date: 2015-01
• Subjects: Batch process control -- Projection to latent structures -- Intermittent measurements -- Disturbance rejection -- Model predictive control; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2014.11.009
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract : Highlights: This paper proposes trajectory tracking predictive control of batch product quality. Proposed approach uses Projection to Latent Structures (PLS) model. Trajectory tracking controller uses intermittent measurements of product quality. Proposed controller is applied to simulated fed-batch fermentation process. Results show ability to track desired trajectory and reject unmeasured disturbances. Abstract: In order to meet tight product quality specifications for batch/semi-batch processes, it is vital to monitor and control product quality throughout the batch duration. The ideal strategy is to achieve end-product quality specifications through trajectory tracking control during a batch run. However, due to the lack of in situ sensors for continuous monitoring of batch product quality, the measurements are usually implemented by laboratory assays and are inherently intermittent. Therefore, direct trajectory tracking of batch product quality is challenging in such applications. This paper proposes a practical approach to realise trajectory tracking control of batch product quality in those situations where only intermittent measurements are available. The first step of the approach consists in identifying a projection to latent structures (PLS) model to identify a relationship between readily measured process variable trajectories and intermittently measured batch product quality. Then the identified PLS-based prediction model is transformed into recursive formulation by utilising missing data imputation algorithms. Such recursive formulation allows identified PLS-based model to be readily incorporated as a predictor into standard model predictive control (MPC) framework. Case study employing simulated fed-batch fermentation process used to manufacture penicillin was employed to illustrate the principle and the effectiveness of the proposed approach.