Combined physico-chemical treatment of secondary settled municipal wastewater in a multifunctional reactor
Bibliographic record
Abstract
In this paper, the physico-chemical treatment of municipal wastewater for the simultaneous removal of pollutant indicators (chemical oxygen demand (COD) and total coliforms) and organic contaminants (total phenols) was investigated and assessed. A secondary settled effluent was subjected to coagulation, disinfection and absorption in a multifunctional reactor by dosing, simultaneously, aluminum polychloride (dose range: 0-150 μL/L), natural zeolites (dose range: 0-150 mg/L), sodium hypochlorite (dose range: 0-7.5 mg/L) and powder activated carbon (dose range: 0-30 mg/L). The treatment process was optimized using computational fluid dynamics (CFD) and response surface methodology. Specifically, a Latin square technique was employed to generate 16 combinations of treating agent types and concentrations which were pilot tested on an 8 m(3)/h multifunctional reactor fed by a secondary effluent with COD and total coliform concentrations ranging from ≈20 to 120 mg/L and from 10(5) to 10(6) CFU/100 mL, respectively. Results were promising, indicating that removal yields up to 71% in COD and 5.4 log in total coliforms were obtained using an optimal combination of aluminum polychloride (dose range ≈ 84-106 μL/L), powder activated carbon ≈ 5 mg/L, natural zeolite (dose range ≈ 34-70 mg/L) and sodium hypochlorite (dose range ≈ 3.4-5.6 mg/L), with all treating agents playing a statistically significant role in determining the overall treatment performance. Remarkably, the combined process was also able to remove ≈ 50% of total phenols, a micropollutant known to be recalcitrant to conventional wastewater treatments.
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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 | 0.000 | 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".