Using Computational Fluid Dynamics Software to Estimate Circulation Time Distributions in Bioreactors

Nonideal mixing in many fermentation processes can lead to concentration gradients in nutrients, oxygen, and pH, among others. These gradients are likely to influence cellular behavior, growth, or yield of the fermentation process. Frequency of exposure to these gradients can be defined by the circu...

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Veröffentlicht in:Biotechnology progress Jg. 19; H. 5; S. 1480 - 1486
Hauptverfasser: Davidson, Kyle M., Sushil, Shrinivasan, Eggleton, Charles D., Marten, Mark R.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: USA American Chemical Society 2003
American Institute of Chemical Engineers
Schlagworte:
Algorithms
Biological and medical sciences
Bioreactors
Biotechnology
Computer Simulation
Fundamental and applied biological sciences. Psychology
Methods. Procedures. Technologies
Models, Theoretical
Motion
Rheology - methods
Software
Stress, Mechanical
Various methods and equipments
Viscosity
Bioreactor
Software
Fluid dynamics
Circulation time
ISSN:8756-7938, 1520-6033
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Zusammenfassung:Nonideal mixing in many fermentation processes can lead to concentration gradients in nutrients, oxygen, and pH, among others. These gradients are likely to influence cellular behavior, growth, or yield of the fermentation process. Frequency of exposure to these gradients can be defined by the circulation time distribution (CTD). There are few examples of CTDs in the literature, and experimental determination of CTD is at best a challenging task. The goal in this study was to determine whether computational fluid dynamics (CFD) software (FLUENT 4 and MixSim) could be used to characterize the CTD in a single‐impeller mixing tank. To accomplish this, CFD software was used to simulate flow fields in three different mixing tanks by meshing the tanks with a grid of elements and solving the Navier‐Stokes equations using the κ‐ϵ turbulence model. Tracer particles were released from a reference zone within the simulated flow fields, particle trajectories were simulated for 30 s, and the time taken for these tracer particles to return to the reference zone was calculated. CTDs determined by experimental measurement, which showed distinct features (log‐normal, bimodal, and unimodal), were compared with CTDs determined using CFD simulation. Reproducing the signal processing procedures used in each of the experiments, CFD simulations captured the characteristic features of the experimentally measured CTDs. The CFD data suggests new signal processing procedures that predict unimodal CTDs for all three tanks.
Bibliographie:ark:/67375/WNG-4KC7RG3G-P
istex:4D29EBE006FD8889A8ABBDCF95B75ACB82150E91
ArticleID:BTPR25580
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SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
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ISSN:8756-7938
1520-6033
DOI:10.1021/bp025580d