Bibliographic record
Abstract
Design of air stripping packed towers used to remove volatile organic compounds requires an estimate of the overall mass transfer coefficient (KLa), which is frequently obtained via physically based parametric correlations. Parametric correlations have some shortcomings and produce predictions with relatively large deviations for full-scale, modern application of air stripping towers. In this study, neural network (NN) technology, a powerful new nonparametric approach, is used to analyze mass transfer characteristics in air stripping towers and to simulate KLa. A large database that is representative of current applications of air stripping towers was assembled for this analysis. The KLa predictions by neural networks were superior to both the Onda model [Onda, K., Takeuchi, H., and Okumoto, Y. (1968). “Mass transfer between gas and liquid phases in packed columns.” J. Chem. Eng. Jpn., 1, 56–62.] and an improved Onda model [Djebber, Y. and Narbaitz, R. M. (unpublished)], the best existing parametric models for air stripping applications. The average absolute error for the validation, as well as for the development data, were found to be less than 19%. The NN model was able to simulate the sudden increase in KLa at high gas loading rates. Also, it simulated more realistically the effect of the packing depth and liquid flow.
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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 | 0.000 | 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".