Effective sludge dewatering technique using the combination of Fenton's reagent and CPAM
Bibliographic record
Abstract
Abstract In this study, the combination of Fenton's reagent and cationic polyacrylamide (CPAM) was used in sludge conditioning for the enhancement of sludge dewatering performance. The effects of CPAM, H2O2, and Fe2+dosages and pH on the moisture content (MC) of the filter cake, the specific resistance of filtration (SRF), and the residual turbidity of the supernatant (RT) were investigated. To observe influencing mechanisms of sludge dewatering further, optical microscopy and scanning electron microscopy (SEM) were employed. Results demonstrated that the sludge dewatering performance obtained by the combination of Fenton's reagent and CPAM was significantly better than that obtained using Fenton's reagent or CPAM alone. The optimum conditions of sludge conditioning were as follows: 400 mg · L−1Fe2+, 4 g · L−1H2O2, 40 mg · L−1CPAM, and pH 4. The SRF, MC, and RT at optimum conditions were reduced to minimum values of 1.06 × 1012 m/kg, 58.9 %, and 3.7, respectively. The optical microscopy and SEM analyses of sludge flocs confirmed that they were more conducive to sludge dewatering after the combination of Fenton oxidation and flocculation. The sludge dewatering results demonstrated that the combination of Fenton and flocculation process exhibited excellent performance in enhancing sludge dewatering and is a promising pretreatment approach to sludge disposal.
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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) | 0.000 | 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".