Bench to full-scale enhanced primary treatment of municipal wastewater under wet weather flow for minimized pollution load: evaluation of chemical addition and process control indicators
Bibliographic record
Abstract
The enhanced primary treatment of municipal primary influent under wet weather conditions was studied through a comprehensive approach from bench to full scale. The study delivered a practical solution for managing seasonal fluctuations in the influent wastewater by determining the most effective operation conditions for coagulation–flocculation. Three metal-based coagulants were tested through a series of jar tests. Alum outperformed other coagulants since 1 mg of Al added as alum with low mixing was able to remove 22 NTU, 19 mg COD, and 0.8 mg ortho-P. Three-factor analysis of variance (ANOVA) indicated that total suspended solids (TSS) removal depended mostly on rapid mixing while chemical oxygen demand (COD) and ortho-P removals depended on slow mixing and coagulant dose. In bench and full-scale operations, the addition of polymer did not lead to any pronounced improvements. Finally, turbidity and percent ultraviolet transmittance showed good correlation with TSS and ortho-P, which evokes their use as surrogates for micropollutants removal and online process control.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".