Seamless Mass Transfer Correlations for Packed Beds Bridging Random and Structured Packings
Bibliographic record
Abstract
A unifying correlative approach for the gas−liquid mass transfer in both structured- and random-packing containing towers was developed based on a two-correlation kernel. Two databanks consisting of 861 experiments for structured packings and 4291 experiments for random packings were merged and concerned the volumetric local and overall gas- and liquid-side mass transfer coefficients k G a w, k L a w, K G a w, and K L a w, the effective gas−liquid interfacial area, a w, and the height equivalent to a theoretical plate, HETP. The three-phase systems were representative of absorption, desorption and distillation applications. Two correlations have emerged, the first to evaluate the local gas- or liquid-side mass transfer coefficient ( k γ ), the second to correlate the effective gas−liquid interfacial area ( a w ). A reconciliation method was used to calibrate and validate the two-correlation kernel ( k γ, a w ) owing to a broad domain of applicability and embracing indifferently both structured- and random-packing columns. The results proved satisfactory and statistical analysis yielded, respectively, 22.1% and 17.6% for the mean and standard deviation for the absolute relative error (ARE) regarding the mass transfer parameters applicable to structured packings. These correlations had also the capacity to predict the same parameters for the random packings with a mean and standard deviation for ARE, respectively, of 26.3% and 24.4%.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".