Effect of Bubble‐Induced Surface Turbulence on Gas–Liquid Mass Transfer in Diffused Aeration Systems
Bibliographic record
Abstract
Models that predict volatilization of organic compounds from wastewater treatment basins may underestimate emission rates if the surfaces are considered as quiescent. In reality, the water surface may be agitated by subsurface aeration, increasing mass transfer across the tank surface air-water interface. This study investigated the effect of turbulence, induced by diffused bubble aeration, on mass transfer at the water surface of a pilot aeration basin. The mass transfer of ammonia from an enclosed headspace over the basin to acidified water was measured when different diffuser types and airflow rates were applied. Oxygen-transfer tests were conducted immediately following each ammonia-transfer test. Increasing airflow rates through fine- and coarse-bubble diffusers had a significant effect on the ammonia mass-transfer rate. Experimental mass-transfer parameters (K(L)a's) for surface volatilization derived with aeration present were up to 48% higher than the K(L)a values for quiescent conditions over the range of conditions tested. No effect of diffuser type on ammonia transfer could be determined. The study results infer an effect on oxygen transfer into the water at the surface and potential transfer of volatile organic compounds, if present, from the water. The results of the ammonia mass-transfer experiments suggest that adjustments to the existing mass transfer correlations for surface volatilization from aeration basins may be in order. Such adjustments will have the greatest effect on predictions for the less volatile compounds, under conditions of low airflow rates.
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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".