Dynamic and local gas holdup studies in external loop recirculating airlift reactor with two rolls of fiberglass packing using electrical resistance tomography
Bibliographic record
Abstract
Abstract BACKGROUND: Airlift bioreactors have been used extensively in biotechnology industries in recent years in a variety of arrangements and applications. The insertion of packing inside the bioreactors has the potential to provide high productivity within a compact size through utilizing immobilized species. RESULTS: A novel recirculating external loop airlift bioreactor that has two rolls of fiberglass packing and a gas distributor in between was designed and built. Electrical resistance tomography (ERT) images showed that the gas holdup increased after installing the packing and the gas distributor. Gas holdup in the riser increased with decreasing static liquid height in the bioreactor. This decreased the liquid superficial velocity, which contributed to a higher gas holdup in the bioreactor. Results also showed that riser gas holdup varied slightly with different sparger configurations. Higher gas holdup increases the oxygen mass transfer rate by increasing the residence time and interfacial mass transfer area. CONCLUSION: ERT results showed that fiberglass packing with an installed gas distributor in bioreactors can achieve higher gas holdup at higher superficial gas velocity. This can contribute to improved conversion in bioreactors with packing through utilizing higher biomass concentrations and higher oxygen concentration. © 2012 Society of Chemical Industry
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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) | 0.001 | 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".