Effect of geometric design on performance of square cross‐section concentric‐duct and split airlift bioreactors
Bibliographic record
Abstract
The performance of internal‐loop airlift bioreactors is affected by their geometry, especially the bottom and gas‐liquid separator designs. Despite efforts to understand the impact of geometry on bioreactor hydrodynamics and oxygen transfer, the contribution of each region to the bioreactor performance as a whole has been evaluated separately. Consequently, it has not been possible to define the best overall geometry. This work discusses the influence of the bottom and gas‐liquid separator geometries on the volumetric oxygen transfer coefficient (kLa) and superficial liquid circulation velocity (UL) of 10‐L square cross‐section concentric‐duct airlift (CDA) and split airlift (SA) reactors. Both airlift reactors were operated with distilled water and Saccharomyces cerevisiae cultivation broth. Based on the results it was possible to indicate the best geometric configurations for biotechnological applications. Gas‐liquid separator design was evaluated by changing the openness angle (α) and the gas‐liquid separator volumetric liquid fraction (FGLS), while the free area for liquid flow between riser and downcomer (Ab) was the geometric parameter modified for the bottom. The results showed that liquid circulation was strongly affected by the bottom geometry, while the gas‐liquid separator significantly affected both variables. Combining the impacts of both regions, promising airlift geometries were identified that exhibited low or high hydrodynamics and oxygen transfer levels. These findings highlight the flexibility of airlift bioreactors and provide information required for their design in order to satisfy specific requirements of different aerobic bioprocesses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".