Hydraulic and disinfection efficiency of an ozonation contactor for a municipal water treatment plant using computational fluid dynamics
Bibliographic record
Abstract
ABSTRACT Determination of hydrodynamic characteristics and disinfection efficiency are important factors in the operational study of the ozone contactors. In the present work, a full‐scale ozonation contactor for a municipal water treatment plant was simulated by computational fluid dynamics (CFD) approach. The Eulerian multiphase, k–ϵ turbulence, and kinetic‐based reaction models were implemented in a finite volume framework to predict the hydraulic parameters, ozone concentration distribution, total organic carbon (TOC) reduction, and concentration‐contact time (CT) values in the contactor. The reaction kinetics for ozone decay was determined by experimental data collected via the contactor. The quality of mesh near the walls and the results independence on the mesh density were probed to determine the optimal grid density. For the first time, the accuracy of the model was evaluated in contrast with experimental data of TOC reductions during ozonation process. The pulse tracer study and flow field indicated recirculation, dead zones, and therefore non‐optimized baffle configuration in the contactor. The predicted CT values indicated that in the cold and hot months, the ozone dosage must increase by a factor of 3–5 relative to the current dosage values to achieve disinfection log‐inactivation credit for pathogens. Using the CFD simulation results, the correlation between ozone dosage and CT values for the hot and cold months in the ozonation contactor was predicted.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".