Numerical investigation on mixing behaviour in the contact zone of an industrial‐scale dissolved air flotation tank
Bibliographic record
Abstract
Abstract The mixing behaviour of the wastewater and the recycle bubbly water in the contact zone could affect the separation performance of dissolved air flotation systems. In this study, the mixing characteristics in the contact zone of an industrial‐scale dissolved air flotation tank were investigated using the computational fluid dynamics method. At first, the influence of the turbulence model on the simulation results was clarified. It is demonstrated that the realizable k‐ε model could predict the gas holdup distribution with better accuracy, and the maximum relative deviation between the predicted gas holdup and the experimental results is only 10%. The influence of the geometry and operating parameters on the mixing process was then studied numerically. It is found that the mixing performance in the contact zone could be improved by decreasing the height position of the nozzle inlet in a certain range. The reason is that microbubbles disperse better in the bottom region and a double circulation flow state forms in the contact zone when the nozzle inlet is installed at a low height. Additionally, the mixing process could be further enhanced by decreasing the diameter of the nozzle inlet. As to the operating conditions, increasing the recycle ratio could not only increase the gas holdup but also improve the mixing performance in the contact zone. When the recycle flow rate remains constant, increasing the wastewater flow rate will lead to a decrease in gas holdup and deterioration of the mixing effect.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".