Tailing propensity in the ultraviolet disinfection of trickling filter and activated sludge wastewater treatment processes
Bibliographic record
Abstract
In this paper, the effect of suspended flocs on the tailing of ultraviolet (UV) disinfection kinetics of secondary effluents was examined. To achieve this goal, final effluents produced in two processes for treating wastewater; namely, a trickling filter system and an activated sludge system, were collected and their UV disinfection were compared. Tailing of the UV dose response curve was controlled by the fraction of flocs that are both culturable and UV-resistant, referred to as the 'tailing propensity'. Using this parameter, the contribution of various floc size fractions in reducing the UV disinfection efficiency of wastewater samples was quantified. Activated sludge flocs larger than 125 μm exhibited as much as 35 times greater tailing propensity than smaller flocs in the range of 20-25 μm. Within a fixed size range, the tailing propensity of flocs generated in the trickling filter system was 3 to 8 times higher than that of activated sludge flocs, and this difference increased with the floc size. A mathematical model was developed to predict the UV disinfection of secondary effluents from suspended particle size distribution data. The model showed good agreement with experimental results.
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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) | 0.000 | 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 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".