Enhancing sludge dewaterability and phosphate removal through a novel chemical dosing strategy using ferric chloride and hydrogen peroxide
Bibliographic record
Abstract
Abstract In this study, we replicated full‐scale centrifuge dewatering utilized in water resource recovery facilities (WRRFs) by using the Higgins modified centrifuge technique and demonstrated that analogous cake solid content and centrate suspended solids were attainable while applying a lower polymer dosage. Furthermore, we demonstrated a dramatic reduction in the concentration of phosphate (P) in anaerobically digested sludge (ADS) under various reaction conditions. H2O2 was employed to convert embedded iron in ADS, in the form of FeS, to Fe (II) and Fe (III), which subsequently reacted to precipitate phosphate compounds, dropping the in situ P concentration by nearly 50%. Adding ferric chloride (220 mg/L) in ADS enhanced the P‐removal to more than 80%. Finally, simultaneous dosing of Fe and H2O2 boosted P‐removal efficiency to higher than 90%. The role of Fe in strengthening the flocs and increasing the dewaterability was also substantiated by demonstrating a 2% growth in the cake solid content when ADS was conditioned with Fe + H2O2 preceding polymer treatment. The outcome of this work confirms that a deeper understanding of centrifuge operational parameters and physico‐chemical properties of wastewater sludge would result in improved performance of municipal WRRFs. Practitioner points Dosing hydrogen peroxide effectively converted iron embedded in sludge from Fe (II) to Fe (III). Simultaneous dosing of iron and hydrogen peroxide boosted P removal efficiency. The role of iron in strengthening flocs and enhancing dewaterability was observed, as it increased cake solid content in centrifuged sludge. An advanced bench‐scale test protocol was employed to optimize polymer dose, simultaneously reducing polymer consumption while maximizing cake solid content and centrate quality.
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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 | 0.000 |
| 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".