A hybrid framework for process monitoring: Enhancing data-driven methodologies with state and parameter estimation. (August 2020)

• Record Type: Journal Article
• Authors: Destro, Francesco; Facco, Pierantonio; García Muñoz, Salvador; Bezzo, Fabrizio; Barolo, Massimiliano
• Is Part Of: Journal of process control. Volume 92(2020)
• Page Start: 333
• Page End: 351
• Publication Date: 2020-08
• Subjects: Fault detection -- Fault diagnosis -- Process monitoring -- Hybrid modeling -- State estimation -- Industry 4.0 -- Extended Kalman filter; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2020.06.002
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract: In this study we bridge traditional standalone data-driven and knowledge-driven process monitoring approaches by proposing a novel hybrid framework that exploits the advantages of both simultaneously. Namely, we design a process monitoring system based on a data-driven model that includes two different data types: i ) "actual" data coming from sensor measurements, and ii ) "virtual" data coming from a state estimator, based on a first-principles model of the system under investigation. We test the proposed approach on two simulated case studies: a continuous polycondensation process for the synthesis of poly-ethylene terephthalate, and a fed-batch fermentation process for the manufacturing of penicillin. The hybrid monitoring model shows superior fault detection and diagnosis performances with respect to conventional monitoring techniques, even when the first-principles model is relatively simple and process/model mismatch exists. Graphical abstract: Highlights: Monitoring system built on a hybrid knowledge-driven/data-driven framework. Knowledge-driven block estimates states, and passes them to data-driven block. Augmented data matrix including measurements and estimated states. Improved detection and diagnosis than with data- or knowledge-driven in isolation.