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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.001 |
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 teacher head, 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".