Enhanced Oxygen Mass Transfer in an External Loop Airlift Bioreactor Using a Packed Bed
Bibliographic record
Abstract
A small quantity of nylon mesh packing inserted in the riser section of an external loop airlift bioreactor (ELAB) was found to increase the overall volumetric oxygen mass transfer coefficient by a factor of 3.73 compared to an unpacked riser. The packing increased gas holdup, decreased bubble size, and decreased liquid circulation rates in the bioreactor, all of which contributed to the dramatic improvement in oxygen mass transfer. A dynamic, spatial model was developed to predict the mass transfer behavior between air bubbles and the continuous liquid phase in the ELAB with and without a packed bed. The model demonstrated superior accuracy compared to simulating the ELAB as a well-mixed vessel and also correctly predicted the cyclical behavior in liquid oxygen concentrations. The oxygen mass transfer coefficient was determined as a best fitting parameter of the model and was found to increase to 4.2 × 10 -3 s -1 using a small amount of packing (96.3% porosity) compared to the unpacked ELAB. This is similar to values measured in well-mixed bioreactors operating at the same aeration rates. The ELAB containing a packed bed is a novel bioreactor with much higher mass transfer and increased surface area for cell immobilization, and therefore it has potential to greatly enhance gas−liquid fermentations and other gas−liquid biochemical operations.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".