An ultrasound/O3 and UV/O3 process for atrazine manufacturing wastewater treatment: a multiple scale experimental study
Bibliographic record
Abstract
Abstract An ultraviolet (UV) and ultrasound (US) enhanced ozonation method were developed to investigate their efficiency on the removal of atrazine and chemical oxygen demand (COD) in authentic atrazine manufacturing wastewater. The bench-scale tests suggested a positive effect of UV and US on the degradation of atrazine within a limited energy range. The pilot-scale flow-through system was further tested by using response surface methodology. The results showed that O3 and its interaction with UV promoted the degradation of both COD and atrazine while its interaction with US inhibited the removal of COD but promoted the removal of atrazine. The optimal removal rate of atrazine (96.9%) was achieved in the condition of 6.86 W/L UV, 1.96 g/L·h O3 and 294 W/L US. Chloride ions hindered the atrazine degradation, but the generated free chlorine radicals were still able to react with atrazine. In terms of energy-effectiveness, the configuration of 14.7 W/L UV and 1.96 g/L·h O3 is the best option, which have the electrical energy per order of 181.6 kWh/m3 for atrazine and 0.13 kWh/g COD. These method and findings could be helpful in the development of energy-efficient advanced oxidation processes in treating wastewater with high salinity and COD loadings.
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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 | 0.001 |
| 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".