Release of phosphorus from biological nutrient removal sludges: A study of sludge pretreatment methods to optimize phosphorus release for subsequent recovery purposes
Bibliographic record
Abstract
As part of on-going studies related to developmentimplementation of new technologies and approaches for enhancing recovery of phosphorus from municipal wastes, the effectiveness of various methods of pretreating waste activated sludges (WASs) from a biological nutrient removal (BNR) process was investigated to determine suitable methods for maximizing phosphorus release. The additions of a strong base (NaOH), strong acid (HCl), organic acid (citric acid), and sodium acetate to waste sludge all facilitated the release of phosphate into solution. The maximum phosphate release was obtained by adding 4.9 mmole/L (~400 mg/L) of acetate to an intermittently mixed digester. Excess amounts of acetate did not further increase the phosphate concentrations. Acetate addition, which triggers carbon storage, a unique characteristic of enhanced biological phosphorus removal (EBPR) processes, releases internally stored phosphate compounds and is recommended as a method for releasing phosphates from EBPR sludges. Along with phosphates, various cations (most notably magnesium and potassium) were also mobilized and released into solution during digestion using acetate. These ions can be beneficially reused for struvite crystallization. Key words: acetate addition, caustic addition, phosphorus release, P-solubilization, sludge treatment, struvite.
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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".