Effects of sodium sulfide concentration on the solid and solution chemistry of a biosolids slurry for phosphorus recovery and reuse
Bibliographic record
Abstract
Municipal biosolids contain organic and inorganic phosphorus (P) that could be recovered for reuse as P fertilizer. Inorganic P compounds include iron phosphates that precipitate and/or adsorbed phosphate ions as a consequence of soluble iron addition in order not to exceed total phosphorus (TP) emission limits. The inorganic orthophosphate (o-Pi) minerals within biosolids can have low solubilities. One P recovery strategy is to maximize the dissolution of o-Pi from biosolids for reuse. Dissolving iron phosphates in biosolids by adding sodium sulfide was assessed as an o-Pi dissolution strategy. 10 % w/w biosolids slurries with a total phosphorus (TP) of 0.97 ± 0.03 mmol P/dry g were mixed with sulfide/TP (S2−/TP) molar ratios from 0 to 4 for up to 96 h. The maximum o-Pi concentration (48 ± 7 mM, or 42 ± 6 % of TP) was obtained for 4 S2−/TP after 24 h at room temperature (RT). Iron concentrations measured by colorimetry (ferrozine) reduced from 0.6 ± 0.1 mM to less than 0.01 mM (S2−/TP > 1). X-ray diffraction and FTIR suggest that sulfide treatment preferentially dissolved amorphous o-Pi-containing solids, vivianite, and minerals with iron, aluminum, phosphate, sulfate, and other subsitutions. Poorly crystalline erdite (NaFeS2 ·2H2O) was detected in products after S2−/TP treatment ratios ≥ 2. Incubation at RT or 37 °C did not affect the o-Pi concentrations for 0 or 4 S2−/TP over 47 h. Sulfide addition could also increase the risk of construction material corrosion, and reduce the efficiency of P recovery by precipitation. There are disadvantages to using sulfide to dissolve o-Pi from biosolids as a potential P recovery process.
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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 | 0.001 |
| 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) | 0.002 | 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".