Hybrid‐EKF: Hybrid model coupled with extended Kalman filter for real‐time monitoring and control of mammalian cell culture. Issue 9 (16th June 2020)

• Record Type: Journal Article
• Authors: Narayanan, Harini; Behle, Lars; Luna, Martin F.; Sokolov, Michael; Guillén‐Gosálbez, Gonzalo; Morbidelli, Massimo; Butté, Alessandro
• Is Part Of: Biotechnology and bioengineering. Volume 117:Issue 9(2020)
• Page Start: 2703
• Page End: 2714
• Publication Date: 2020-06-16
• Subjects: adaptive control -- bioprocessing -- extended Kalman filter -- hybrid models -- process monitoring; Biotechnology -- Periodicals; Bioengineering -- Periodicals; 660.6
• DOI: 10.1002/bit.27437
• Languages: English
• ISSNs: 0006-3592

Abstract

Abstract: In a decade when Industry 4.0 and quality by design are major technology drivers of biopharma, automated and adaptive process monitoring and control are inevitable requirements and model‐based solutions are key enablers in fulfilling these goals. Despite strong advancement in process digitalization, in most cases, the generated datasets are not sufficient for relying on purely data‐driven methods, whereas the underlying complex bioprocesses are still not completely understood. In this regard, hybrid models are emerging as a timely pragmatic solution to synergistically combine available process data and mechanistic understanding. In this study, we show a novel application of the hybrid‐EKF framework, that is, hybrid models coupled with an extended Kalman filter for real‐time monitoring, control, and automated decision‐making in mammalian cell culture processing. We show that, in the considered application, the predictive monitoring accuracy of such a framework improves by at least 35% when developed with hybrid models with respect to industrial benchmark tools based on PLS models. In addition, we also highlight the advantages of this approach in industrial applications related to conditional process feeding and process monitoring. With regard to the latter, for an industrial use case, we demonstrate that the application of hybrid‐EKF as a soft sensor for titer shows a 50% improvement in prediction accuracy compared with state‐of‐the‐art soft sensor tools. Abstract : A novel application of hybrid‐ EKF, a framework coupling hybrid model with extended Kalman filter, for real‐time monitoring and control of cell culture processes was demonstrated in this work. Such a modeling framework enables real‐time monitoring, control and autonomous decision making that are key drivers for establishing smart factories in the digital era. Subsequently, two use cases were presented to highlight the strengths of the proposed framework in comparison to the industrial benchmark tools.