Semi‐theoretical prediction of volumetric mass transfer coefficients in bubble columns with organic liquids at ambient and elevated temperatures
Bibliographic record
Abstract
Abstract A semi‐theoretical approach for predicting kLa values (referred to liquid volume) in 18 organic liquids [acetone, aniline, 1‐butanol, benzene, cyclohexane, decalin, 1,2‐dichloroethane, 1,4‐dioxane, ethanol (96%), ethylacetate, ethylbenzene, ligroin, methanol, nitrobenzene, 2‐propanol, tetralin, toluene, and xylene] at various operating conditions (including elevated temperatures and pressures) was developed. It was found that the approach is applicable regardless of the hydrodynamic regime (at uG ≤ 0.1 m/s). Temperatures up to 353 K and pressures up to 0.5 MPa were tested. Two different distributors (multiple‐hole and single‐hole type) were employed. The liquid‐phase mass transfer coefficient kL was calculated theoretically from the penetration theory on the basis of original definition of gas–liquid contact time. The interfacial area a was defined with respect to the liquid volume. It was found that their product kLa must be multiplied by some correction factor in order to take account of the non‐spherical (ellipsoidal) shape of the bubbles. When the correction term is correlated to both the Eötvös number (Eo) and the dimensionless temperature ratio, 198 experimental kLa values can be fitted reasonably well (average relative error 9.3%).
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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 | 0.001 |
| 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 | 0.001 | 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".