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 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 | 0.001 | 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".