Experimental study on aeration efficiency in a pilot‐scale decelerated oxidation ditch equipped with fine bubble diffusers and impellers
Bibliographic record
Abstract
Abstract This work is an experimental study on the oxygen transfer capability and efficiency in a pilot‐scale decelerated oxidation ditch equipped with fine bubble diffusers and impellers (inducing horizontal liquid flows). Aqueous solutions of carboxymethyl cellulose (CMC) exhibiting shear‐thinning rheological properties are selected to simulate activated sludge in the oxidation ditch process. The effect of air flow rate and horizontal liquid velocity along the loop channel on oxygen transfer capability and aeration efficiency is examined in tap water and CMC aqueous solutions. The standard volumetric mass transfer coefficient KLa20, standard oxygen transfer efficiency (SOTE), and standard aeration efficiency (SAE) are introduced to study the oxygen mass transfer performance and energy consumption efficiency in the oxidation ditch. The results show that KLa20, SOTE, and SAE in CMC aqueous solutions follow similar trends observed in tap water. Due to the effect of shear‐thinning rheological properties, KLa20, SOTE, and SAE in CMC aqueous solutions are smaller than those in tap water. Two dimensionless numbers, Froude number and Ohnesorge number are introduced to study the oxygen transfer efficiency through the consideration of the combined influence of aeration, crossflow, gas bubble size, and physical properties of the liquid phase.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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".