Carbon and Phosphorus Removal from Primary Municipal Wastewater Using Recovered Aluminum
Bibliographic record
Abstract
In this work, recovery of aluminum from coagulated primary sludge and its reuse potential as secondary coagulant were investigated. The recovery process consisted of releasing the particle-bound aluminum from primary sludge by acidification (HCl or H 2 SO 4 ), followed by separation using centrifugation for dissolved coagulant recovery. The recovered coagulant was then reused for treating primary wastewater and overall coagulation efficiency was determined. While with fresh alum, the removal efficiencies of total suspended solids, chemical oxygen demand, total phosphorus, and total nitrogen were 85%, 65%, 80% and 33%, respectively, a drop in removal efficiency of total suspended solids and chemical oxygen demand was observed for recovered aluminum (85–60% and 65–50%, respectively). Nitrogen concentration remained almost constant with each cycle, while phosphorus in the effluent increased by 1 mg/L and 3 mg/L in the first and second cycle, respectively. Precipitation of various aluminum species was modeled for determining the recovery potential of aluminum at low pH. Preliminary cost analysis indicates that optimum recovery of aluminum occurred at a pH of 1.5 for both acids. Struvite precipitation effectively removed increased phosphorus solubilized by acidification at the end of second cycle, however, it also decreased the amount of aluminum available for recycle.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".