Interfacial Mass Transfer in Randomly Packed Towers: A Confident Correlation for Environmental Applications
Bibliographic record
Abstract
Volumetric mass-transfer coefficients (kLa(w), KLa(w), kGa(w), kGa(w)) required for randomly dumped packed tower design were gathered from the literature to generate a working database comprehending 2675 measurements relevant to water and air pollution abatement processes. The cross-examination of two important correlations predicting mass-transfer coefficients was achieved through this database (Onda correlation, 1968; Billet and Schultes correlation, 1993). Some limitations regarding either the level of accuracy or the application range came to light with this investigation. Artificial neural network (ANN) modeling is then proposed allowing all four mass-transfer coefficients predictions. A single ANN correlation was built to predict the dimensionless gas (or liquid) film Sherwood number (ShL/G) as a function of six dimensionless groups, namely, the liquid Reynolds (ReL), Froude (FrL), Eotvös (EoL) numbers, the gas (or liquid) Schmidt number (ScL/G), the Lockhart-Martinelli parameter (chi), and a bed-characterizing number (K). Using the ANN correlation and the two-film theory, a reconciliation procedure was further implemented resulting in better predictions of the gas (or liquid) overall volumetric mass-transfer coefficients. The resulting correlation yielded an absolute average relative error of 22.1% and a standard deviation of 21.1% based on whole database while the ANN predictions remain in accordance with the physical evidence reported in the literature.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".