Adaptive control of the E. coli-specific growth rate in fed-batch cultivation based on oxygen uptake rate

In this study, an automatic control system is developed for the setpoint control of the cell biomass specific growth rate (SGR) in fed-batch cultivation processes. The feedback signal in the control system is obtained from the oxygen uptake rate (OUR) measurement-based SGR estimator. The OUR online...

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Veröffentlicht in:Computational and structural biotechnology journal 2023-01, Vol.21, p.5785-5795
Hauptverfasser: Urniezius, Renaldas, Masaitis, Deividas, Levisauskas, Donatas, Survyla, Arnas, Babilius, Povilas, Godoladze, Dziuljeta
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Sprache:eng
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Zusammenfassung:In this study, an automatic control system is developed for the setpoint control of the cell biomass specific growth rate (SGR) in fed-batch cultivation processes. The feedback signal in the control system is obtained from the oxygen uptake rate (OUR) measurement-based SGR estimator. The OUR online measurements adapt the system controller to time-varying operating conditions. The developed approach of the PI controller adaptation is presented and discussed. The feasibility of the control system for tracking a desired biomass growth time profile is demonstrated with numerical simulations and fed-batch culture E.coli control experiments in a laboratory-scale bioreactor. The procedure was cross-validated with the open-loop digital twin SGR estimator, as well as with the adaptive control of the SGR, by tracking a desired setpoint time profile. The digital twin behavior statistically showed less of a bias when compared to SGR estimator performance. However, the adaptation—when using first principles—was outperformed 30 times by the model predictive controller in a robustness check scenario. •The adaptive control of specific growth rate (SGR) relies only upon the oxygen uptake rate in growth-limiting fed-batch bioprocesses.•The adaptation is based on the controlled process's first principles-based tendency model.•The procedure was cross-validated with an open-loop digital twin, a closed-loop SGR estimator, and model predictive control behavior.
ISSN:2001-0370
2001-0370
DOI:10.1016/j.csbj.2023.11.033