Hydrodynamic Characteristics in an External Loop Airlift Bioreactor Containing a Spinning Sparger and a Packed Bed
Bibliographic record
Abstract
An external loop airlift bioreactor (ELAB) and the packed bed bioreactor (PB) design strategies have been combined into one vessel. The hydrodynamic behavior of the combined system has been investigated. Woven nylon packing was placed in the riser section of the ELAB to represent the packed bed. A novel spinning sparger was employed to generate air bubbles. The controlled input variables were packing porosity, packing height, superficial gas velocity, and sparger rotational speed. The hydrodynamic output variables included gas holdup, liquid circulation velocity, axial dispersion, and bubble-size distribution. Gas holdup continuously increased with increases in both the packing height up to 0.8 m and the porosity up to 0.99, but at a porosity of 1.0 (no packing), there was a significant drop in the gas holdup. Increased amounts of packing in the ELAB, whether in the form of packing height or packing density, decreased the liquid circulation rate in the bioreactor because of increased frictional resistance and tortuosity. Packing also decreased the Bodenstein number, indicating greater axial dispersion and enhanced mixing. Bubble sizes were more uniform and had smaller diameters after passing through the packing material. Empirical models are presented which accurately predict gas holdup and liquid circulation velocities as functions of all four independent variables (packing height, packing porosity, gas flow rate, and sparger spinning speed). The optimum hydrodynamic conditions were observed with packing at the highest porosity (0.99) used in this study.
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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 | 0.000 |
| 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".