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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...

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Autores principales: Bisgaard, Jonas, Muldbak, Monica, Cornelissen, Sjef, Tajsoleiman, Tannaz, Huusom, Jakob K., Rasmussen, Tue, Gernaey, Krist V.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Research Network of Computational and Structural Biotechnology 2020
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.
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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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