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Data-based dynamic compartment model: Modeling of E. coli fed-batch fermentation in a 600 m(3) bubble column
Mathematical modeling is a powerful and inexpensive approach to provide a quantitative basis for improvements that minimize the negative effects of bioreactor heterogeneity. For a model to accurately represent a heterogeneous system, a flow model that describes how mass is channeled between differen...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9559308/ https://www.ncbi.nlm.nih.gov/pubmed/36177778 http://dx.doi.org/10.1093/jimb/kuac021 |
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author | Bisgaard, Jonas Zahn, James A Tajsoleiman, Tannaz Rasmussen, Tue Huusom, Jakob K Gernaey, Krist V |
author_facet | Bisgaard, Jonas Zahn, James A Tajsoleiman, Tannaz Rasmussen, Tue Huusom, Jakob K Gernaey, Krist V |
author_sort | Bisgaard, Jonas |
collection | PubMed |
description | Mathematical modeling is a powerful and inexpensive approach to provide a quantitative basis for improvements that minimize the negative effects of bioreactor heterogeneity. For a model to accurately represent a heterogeneous system, a flow model that describes how mass is channeled between different zones of the bioreactor volume is necessary. In this study, a previously developed compartment model approach based on data from flow-following sensor devices was further developed to account for dynamic changes in volume and flow rates and thus enabling simulation of the widely used fed-batch process. The application of the dynamic compartment model was demonstrated in a study of an industrial fermentation process in a 600 m(3) bubble column bioreactor. The flow model was used to evaluate the mixing performance by means of tracer simulations and was coupled with reaction kinetics to simulate concentration gradients in the process. The simulations showed that despite the presence of long mixing times and significant substrate gradients early in the process, improving the heterogeneity did not lead to overall improvements in the process. Improvements could, however, be achieved by modifying the dextrose feeding profile. |
format | Online Article Text |
id | pubmed-9559308 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-95593082022-10-18 Data-based dynamic compartment model: Modeling of E. coli fed-batch fermentation in a 600 m(3) bubble column Bisgaard, Jonas Zahn, James A Tajsoleiman, Tannaz Rasmussen, Tue Huusom, Jakob K Gernaey, Krist V J Ind Microbiol Biotechnol Fermentation, Cell Culture and Bioengineering Mathematical modeling is a powerful and inexpensive approach to provide a quantitative basis for improvements that minimize the negative effects of bioreactor heterogeneity. For a model to accurately represent a heterogeneous system, a flow model that describes how mass is channeled between different zones of the bioreactor volume is necessary. In this study, a previously developed compartment model approach based on data from flow-following sensor devices was further developed to account for dynamic changes in volume and flow rates and thus enabling simulation of the widely used fed-batch process. The application of the dynamic compartment model was demonstrated in a study of an industrial fermentation process in a 600 m(3) bubble column bioreactor. The flow model was used to evaluate the mixing performance by means of tracer simulations and was coupled with reaction kinetics to simulate concentration gradients in the process. The simulations showed that despite the presence of long mixing times and significant substrate gradients early in the process, improving the heterogeneity did not lead to overall improvements in the process. Improvements could, however, be achieved by modifying the dextrose feeding profile. Oxford University Press 2022-09-30 /pmc/articles/PMC9559308/ /pubmed/36177778 http://dx.doi.org/10.1093/jimb/kuac021 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of Society of Industrial Microbiology and Biotechnology. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Fermentation, Cell Culture and Bioengineering Bisgaard, Jonas Zahn, James A Tajsoleiman, Tannaz Rasmussen, Tue Huusom, Jakob K Gernaey, Krist V Data-based dynamic compartment model: Modeling of E. coli fed-batch fermentation in a 600 m(3) bubble column |
title | Data-based dynamic compartment model: Modeling of E. coli fed-batch fermentation in a 600 m(3) bubble column |
title_full | Data-based dynamic compartment model: Modeling of E. coli fed-batch fermentation in a 600 m(3) bubble column |
title_fullStr | Data-based dynamic compartment model: Modeling of E. coli fed-batch fermentation in a 600 m(3) bubble column |
title_full_unstemmed | Data-based dynamic compartment model: Modeling of E. coli fed-batch fermentation in a 600 m(3) bubble column |
title_short | Data-based dynamic compartment model: Modeling of E. coli fed-batch fermentation in a 600 m(3) bubble column |
title_sort | data-based dynamic compartment model: modeling of e. coli fed-batch fermentation in a 600 m(3) bubble column |
topic | Fermentation, Cell Culture and Bioengineering |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9559308/ https://www.ncbi.nlm.nih.gov/pubmed/36177778 http://dx.doi.org/10.1093/jimb/kuac021 |
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