Heat and Mass Transfer in Cocurrent Gas−Liquid Packed Beds. Analysis, Recommendations, and New Correlations
Bibliographic record
Abstract
Meticulous inspection of the literature has unveiled the weakness of several empirical methods for predicting the macroscopic mass- and heat-transfer characteristics relevant to gas−liquid cocurrent downflow and upflow packed-bed reactors. In response, using a wide experimental database consisting of 5279 measurements for trickle beds (downflow) and 1974 measurements for packed bubble columns (upflow), a set of reliable correlations has been recommended for the prediction of the gas−liquid interfacial area ( a gl ), the volumetric liquid- ( k l a ) and gas-side ( k g a ) mass-transfer coefficients, the wall (η e k lw ) and bed (η e k ls ) liquid−solid mass-transfer coefficients, the wall heat-transfer coefficient ( h w ), the bed effective radial thermal conductivity (λ e ), and the particle-to-fluid heat-transfer coefficient ( h p ). Some of these correlations are from the literature, and others have been developed by combining artificial neural networks and dimensional analysis. The accuracy of the proposed correlations surpasses by far the performances of the available methods sometimes by up to a 10-fold reduction in scatter. Notwithstanding the substantial reduction in scatter, these correlations have been thoroughly tested for phenomenological consistency and have been shown to restore the expected trends documented in the database.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".