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Flow-following sensor devices: A tool for bridging data and model predictions in large-scale fermentations
Production-scale fermentation processes in industrial biotechnology experience gradients in process variables, such as dissolved gases, pH and substrate concentrations, which can potentially affect the production organism and therefore the yield and profitability of the processes. However, the exten...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Research Network of Computational and Structural Biotechnology
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7595931/ https://www.ncbi.nlm.nih.gov/pubmed/33163151 http://dx.doi.org/10.1016/j.csbj.2020.10.004 |
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author | Bisgaard, Jonas Muldbak, Monica Cornelissen, Sjef Tajsoleiman, Tannaz Huusom, Jakob K. Rasmussen, Tue Gernaey, Krist V. |
author_facet | Bisgaard, Jonas Muldbak, Monica Cornelissen, Sjef Tajsoleiman, Tannaz Huusom, Jakob K. Rasmussen, Tue Gernaey, Krist V. |
author_sort | Bisgaard, Jonas |
collection | PubMed |
description | Production-scale fermentation processes in industrial biotechnology experience gradients in process variables, such as dissolved gases, pH and substrate concentrations, which can potentially affect the production organism and therefore the yield and profitability of the processes. However, the extent of the heterogeneity is unclear, as it is currently a challenge at large scale to obtain representative measurements from different zones of the reactor volume. Computational fluid dynamics (CFD) models have proven to be a valuable tool for better understanding the environment inside bioreactors. Without detailed measurements to support the CFD predictions, the validity of CFD models is debatable. A promising technology to obtain such measurements from different zones in the bioreactors are flow-following sensor devices, whose development has recently benefitted from advancements in microelectronics and sensor technology. This paper presents the state of the art within flow-following sensor device technology and addresses how the technology can be used in large-scale bioreactors to improve the understanding of the process itself and to test the validity of detailed computational models of the bioreactors in the future. |
format | Online Article Text |
id | pubmed-7595931 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Research Network of Computational and Structural Biotechnology |
record_format | MEDLINE/PubMed |
spelling | pubmed-75959312020-11-06 Flow-following sensor devices: A tool for bridging data and model predictions in large-scale fermentations Bisgaard, Jonas Muldbak, Monica Cornelissen, Sjef Tajsoleiman, Tannaz Huusom, Jakob K. Rasmussen, Tue Gernaey, Krist V. Comput Struct Biotechnol J Review Production-scale fermentation processes in industrial biotechnology experience gradients in process variables, such as dissolved gases, pH and substrate concentrations, which can potentially affect the production organism and therefore the yield and profitability of the processes. However, the extent of the heterogeneity is unclear, as it is currently a challenge at large scale to obtain representative measurements from different zones of the reactor volume. Computational fluid dynamics (CFD) models have proven to be a valuable tool for better understanding the environment inside bioreactors. Without detailed measurements to support the CFD predictions, the validity of CFD models is debatable. A promising technology to obtain such measurements from different zones in the bioreactors are flow-following sensor devices, whose development has recently benefitted from advancements in microelectronics and sensor technology. This paper presents the state of the art within flow-following sensor device technology and addresses how the technology can be used in large-scale bioreactors to improve the understanding of the process itself and to test the validity of detailed computational models of the bioreactors in the future. Research Network of Computational and Structural Biotechnology 2020-10-15 /pmc/articles/PMC7595931/ /pubmed/33163151 http://dx.doi.org/10.1016/j.csbj.2020.10.004 Text en © 2020 The Author(s) http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Bisgaard, Jonas Muldbak, Monica Cornelissen, Sjef Tajsoleiman, Tannaz Huusom, Jakob K. Rasmussen, Tue Gernaey, Krist V. Flow-following sensor devices: A tool for bridging data and model predictions in large-scale fermentations |
title | Flow-following sensor devices: A tool for bridging data and model predictions in large-scale fermentations |
title_full | Flow-following sensor devices: A tool for bridging data and model predictions in large-scale fermentations |
title_fullStr | Flow-following sensor devices: A tool for bridging data and model predictions in large-scale fermentations |
title_full_unstemmed | Flow-following sensor devices: A tool for bridging data and model predictions in large-scale fermentations |
title_short | Flow-following sensor devices: A tool for bridging data and model predictions in large-scale fermentations |
title_sort | flow-following sensor devices: a tool for bridging data and model predictions in large-scale fermentations |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7595931/ https://www.ncbi.nlm.nih.gov/pubmed/33163151 http://dx.doi.org/10.1016/j.csbj.2020.10.004 |
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