Managing Hazardous Municipal Wastewater: A Membrane-Integrated Hybrid Approach for Fast and Effective Treatment in Low Temperature Environment
Bibliographic record
Abstract
Protection of natural water resources like lakes from the onslaught of hazardous municipal wastewater is often a challenge particularly in the cold regions. For treatment of enormous quantity of municipal wastewater, biological treatment is normally adopted but high COD (Chemical Oxygen demand) of such wastewater turns biological treatment slow and difficult. At low temperature environment, effective treatment of such municipal wastewater becomes extremely difficult due to weakened microbial activities. The present study was carried out with a hybrid approach comprising chemical treatment and membrane separation under psychrophilic conditions. Well–known Fenton’s treatment was adopted under response surface optimized conditions that helped recovery of nitrogen and phosphorus nutrients as value–added struvite fertilizer or magnesium ammonium phosphate (NH4MgPO4∙6H2O). The optimal COD removal was found to be 96% at a low temperature of 15oC and pH of 6.3 using Fe2+/H2O2 ratio of 0.10 and of H2O2 1.9 g/l with reaction time of 2 h. Down–stream purification of the struvite-free water by microfiltration and nanofiltration largely fouling–free flat sheet cross flow membrane modules ultimately turned the treated water reusable through reduction of dissolved solids, conductivity and salinity.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".