Literature DB >> 26228573

Bioreactor control improves bioprocess performance.

Rimvydas Simutis1, Andreas Lübbert2.   

Abstract

The performance of bioreactors is not only determined by productivity but also by process quality, which is mainly determined by variances in the process variables. As fluctuations in these quantities directly affect the variability in the product properties, combatting distortions is the main task of practical quality assurance. The straightforward way of reducing this variability is keeping the product formation process tightly under control. Purpose of this keynote is to show that there is enough evidence in literature showing that the performance of the fermentation processes can significantly be improved by feedback control. Most of the currently used open loop control procedures can be replaced by relatively simple feedback techniques. It is shown by practical examples that such a retrofitting does not require significant changes in the well-established equipment. Feedback techniques are best in assuring high reproducibility of the industrial cultivation processes and thus in assuring the quality of their products. Many developments in supervising and controlling industrial fermentations can directly be taken over in manufacturing processes. Even simple feedback controllers can efficiently improve the product quality. It's the time now that manufacturers follow the developments in most other industries and improve process quality by automatic feedback control.
Copyright © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Keywords:  Automation; Bioprocess Performance; Feedback Control; Fermentation; Process State Estimation

Mesh:

Year:  2015        PMID: 26228573     DOI: 10.1002/biot.201500016

Source DB:  PubMed          Journal:  Biotechnol J        ISSN: 1860-6768            Impact factor:   4.677


  9 in total

1.  Usage of Digital Twins Along a Typical Process Development Cycle.

Authors:  Peter Sinner; Sven Daume; Christoph Herwig; Julian Kager
Journal:  Adv Biochem Eng Biotechnol       Date:  2021       Impact factor: 2.635

2.  Systematic molecular evolution enables robust biomolecule discovery.

Authors:  Erika A DeBenedictis; Emma J Chory; Dana W Gretton; Brian Wang; Stefan Golas; Kevin M Esvelt
Journal:  Nat Methods       Date:  2021-12-30       Impact factor: 28.547

3.  Hybrid Approach to State Estimation for Bioprocess Control.

Authors:  Rimvydas Simutis; Andreas Lübbert
Journal:  Bioengineering (Basel)       Date:  2017-03-08

4.  Deep reinforcement learning for the control of microbial co-cultures in bioreactors.

Authors:  Neythen J Treloar; Alex J H Fedorec; Brian Ingalls; Chris P Barnes
Journal:  PLoS Comput Biol       Date:  2020-04-10       Impact factor: 4.475

5.  Generic estimator of biomass concentration for Escherichia coli and Saccharomyces cerevisiae fed-batch cultures based on cumulative oxygen consumption rate.

Authors:  Renaldas Urniezius; Arnas Survyla; Dziugas Paulauskas; Vladas Algirdas Bumelis; Vytautas Galvanauskas
Journal:  Microb Cell Fact       Date:  2019-11-05       Impact factor: 5.328

Review 6.  Measurement Techniques to Resolve and Control Population Dynamics of Mixed-Culture Processes.

Authors:  Ivan Schlembach; Alexander Grünberger; Miriam A Rosenbaum; Lars Regestein
Journal:  Trends Biotechnol       Date:  2021-02-08       Impact factor: 21.942

7.  From Physics to Bioengineering: Microbial Cultivation Process Design and Feeding Rate Control Based on Relative Entropy Using Nuisance Time.

Authors:  Renaldas Urniezius; Vytautas Galvanauskas; Arnas Survyla; Rimvydas Simutis; Donatas Levisauskas
Journal:  Entropy (Basel)       Date:  2018-10-11       Impact factor: 2.524

8.  Model predictive control for steady-state performance in integrated continuous bioprocesses.

Authors:  Magdalena Pappenreiter; Sebastian Döbele; Gerald Striedner; Alois Jungbauer; Bernhard Sissolak
Journal:  Bioprocess Biosyst Eng       Date:  2022-08-02       Impact factor: 3.434

9.  Process intensification for the continuous production of an antimicrobial peptide in stably-transformed Sf-9 insect cells.

Authors:  Lukas Käßer; Maximilian Rotter; Luca Coletta; Denise Salzig; Peter Czermak
Journal:  Sci Rep       Date:  2022-01-20       Impact factor: 4.379

  9 in total

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