Experiments and a three‐phase computational fluid dynamics (CFD) simulation coupled with population balance equations of a stirred tank bioreactor for high cell density cultivation
Bibliographic record
Abstract
Abstract This research describes a three dimensional (3‐D) computational fluid dynamics (CFD) simulation coupled with population balance equations (PBE) to study hydrodynamics and biomass production in a laboratory‐scale stirred‐tank bioreactor. The gas‐liquid‐solid flow was modelled using a Eulerian multiphase and turbulence (RNG) model. The energy dissipation rates, gas holdup, flow patterns, Sauter mean bubble diameter, and volumetric mass transfer coefficient were investigated for three different types of impeller using a multiple reference frame (MRF) model within the whole multiphase bioreactor. The effects of aeration rate and impeller speed on gas holdup and volumetric mass transfer coefficient were investigated owing to oxygen limitation in high cell density cultivation (HCDC). As high viscosity puts a limit on the efficiency of the bioreactor, the influences of viscosity on Sauter mean diameter, gas holdup, and volumetric mass transfer coefficient were also assessed. To determine growth kinetics as well as gas holdup, a set of experiments was performed. The numerical results of gas holdup and kLa were compared with the experimental data. Obtained results suggest that the Scaba impeller results in higher values of volumetric mass transfer coefficient, and subsequently higher biomass concentrations. One of the greatest problems in HCDC is feed accumulation in particular places. As depletion of substrate occurs near the impeller, the best spot for feeding purposes is in the vicinity of the impeller. Current research gives insight into the determination of the optimal operating conditions of HCDC in stirred bioreactors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".