Automatization of a penicillin production process with soft sensors and an adaptive controller based on neuro fuzzy systems

• Published in: Control engineering practice Vol. 12; no. 9; pp. 1073 - 1090
• Main Authors: Araúzo-Bravo, Marcos J., Cano-Izquierdo, José M., Gómez-Sánchez, Eduardo, López-Nieto, Manuel J., Dimitriadis, Yannis A., López-Coronado, Juan
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.09.2004
• Subjects: Adaptive control; Fuzzy hybrid systems; Intelligent knowledge-based systems; Process control; Soft sensing; Soft sensing; Intelligent knowledge-based systems; Adaptive control; Fuzzy hybrid systems; Process control
• ISSN: 0967-0661; 1873-6939
• DOI: 10.1016/j.conengprac.2003.11.002

This paper addresses the automatization of a penicillin production process with the development of soft sensors as well as Internal Model Controllers (IMC) for a penicillin fermentation plant using modules based on FasArt and FasBack neuro-fuzzy systems. While soft sensors are intended to aid the human supervision of the process currently being conducted at pilot plants, the proposed controller will make the automatization feasible and eliminate the need for human operator. FasArt and FasBack feature fast stable learning and good MIMO identification, which makes them suitable for the development of adaptive controllers and soft sensors. In this paper, these modules are evaluated by training the neuro-fuzzy systems first on simulated data and then applying the resulting IMC controllers to a simulated plant. Moreover, training the systems on data coming from a real pilot plant, and evaluating the controller performance on the same real plant. Results show that the trend of reference is captured, thus allowing high penicillin production. Moreover, soft sensors derived for biomass, viscosity and penicillin are very accurate. In addition, on-line adaptive capabilities were implemented and tested with FasBack, since this system presents learning guided by error minimization as new data samples arrive. With these features, adaptive IMC controllers can be implemented and are helpful when dynamics have been poorly learned or the plant parameters vary with time, since the performance of static models and controllers can be improved through adaptation.