Advanced nonlinear gas phase control for autotrophic cultivations of different mutants of Ralstonia eutropha. (April 2020)

• Record Type: Journal Article
• Authors: Neddermeyer, Flavia; King, Rudibert
• Is Part Of: Journal of process control. Volume 88(2020)
• Page Start: 10
• Page End: 18
• Publication Date: 2020-04
• Subjects: Autotrophic cultivation -- Ralstonia eutropha -- Model-based control -- Gas phase control -- Model adaption -- Mutant strains; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2020.02.002
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: The paper presents a gas phase controller for autotrophic cultivations independent of metabolic information of the strain cultivated. As a basic component, a feedforward disturbance rejection based on highly varying estimated gas transfer rates is used to unburden the controller. This controller allows for running closed-loop controlled fermentations with a new strain without performing prior experiments. By this, enough cells can be produced for further biological studies. Additionally, informative data is obtained for modeling of the biotic part for a new strain. Results are shown for the wild type strain of Ralstonia eutropha and two mutants cultivated with an explosive gas mixture of H2, O2 and CO2 . Abstract: This paper presents a control architecture suitable for autotrophic cultivations in a stirred tank reactor feeding a gas mixture of H2 /CO2 /O2 . No metabolic information about the cultivated strain is required for the gas phase controller, which can also be used as part of the overall control for the entire process. A gain-scheduled MIMO-PI controller connected to an adaptive feedforward disturbance rejection is proposed. The latter exploits the estimated gas transfer rates of a Kalman filter solely based on a description of the non-biotic processes occurring. Thus, without further knowledge of the autotrophic strain, experiments can be run to generate data and to develop a process model including strain-specific biological details. The wild-type strain H16 of Ralstonia eutropha and two mutants, HF805 and H798, are used to demonstrate how this can be done.