Prediction of enhancement factor for mass transfer coefficient in regular packed liquid–liquid extraction columns
Bibliographic record
Abstract
Abstract Mass transfer coefficients are one of the most important parameters for the design of liquid–liquid extraction columns. The mass transfer coefficients of single drops in a pilot structured packed column have been measured using toluene/acetic acid/water and n ‐butyl acetate/acetic acid/water systems. Current research shows that theoretical models have failed to predict mass transfer coefficient precisely and are not reliable for design. In this work an empirical correlation for prediction of enhancement factor is developed. Dispersed phase mass transfer coefficients predicted by the proposed correlation are in good compatibility with experimental results. Les cœfficients de transfert de masse figurent parmi les paramètres les plus importants à prendre en compte lors de la conception de colonnes d'extraction liquide‐liquide. Les cœfficients de transfert de masse de chaque goutte dans une colonne à garnissage structuré pilote ont été mesurés grâce à des systèmes à base de toluène/d'acide acétique/d'eau et d'acétate de n‐butyle/d'acide acétique/et d'eau. La comparaison entre les modèles théoriques et les résultats expérimentaux de cœfficients de transfert de masse a montré que ces modèles ne sont pas assez précis pour être appliqués à la conception. Dans cette recherche, les facteurs d'accentuation du cœfficient de diffusion ont été déterminés expérimentalement et une corrélation empirique a été atteinte pour le facteur d'accentuation. Les cœfficients de transfert de masse de la phase dispersée qui avait été prévus grâce à la corrélation proposée s'harmonisent bien aux résultats des expériences. © 2010 Canadian Society for Chemical Engineering
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".