Reconciliation Procedure for Gas−Liquid Interfacial Area and Mass-Transfer Coefficient in Randomly Packed Towers
Bibliographic record
Abstract
Interfacial areas ( a w ) and volumetric mass-transfer coefficients ( k L a w, K L a w, k G a w, and K G a w ) required for randomly packed tower design were gathered from the literature to generate a working database including over 3780 measurements. A set of artificial neural network correlations for the gas−liquid interfacial area and the pure local mass-transfer coefficients was proposed. Thus, the gas−liquid interfacial area and the pure local mass-transfer coefficients ( k γ, where γ = G or L) were extracted using a reconciliation procedure which combined actually measured interfacial areas with pseudo interfacial areas inferred from the actually measured volumetric mass-transfer coefficients. The neural network weights of the two a w and k γ correlations were adjusted using a least-squares composite criterion simultaneously over the five mass-transfer parameters. The first correlation representing the gas−liquid interfacial area [ a w / a T = f ( Re L, Fr L, Eo L,χ, K )] yielded an average absolute relative error (AARE) of 22.5% for the 325 measurements available. The second one, representing either k G or k L, was also implemented using the following structure: Sh γ = f ( Re γ, Fr γ, Sc γ,χ). The combination of both correlation predictions (i.e., k γ a w ) yielded an AARE of 24.4% for the local and global volumetric mass-transfer coefficients (3455 data).
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".