Model‐based investigation of the chemical phosphorus removal potential of the peroxide regenerated iron‐sulfide control technology
Bibliographic record
Abstract
In this study, the potential of using peroxide regenerated iron-sulfide control (PRI-SC®) for chemical phosphorus removal utilizing the existing iron sulfide found in wastewaters was investigated in batch tests and compared in full-scale facility-wide simulations to using iron salts. PRI-SC is a combination treatment that utilizes iron salts and hydrogen peroxide in a synergetic fashion, where hydrogen peroxide is used in regenerating the spent iron salt in situ in the form of iron sulfide, yielding ferric iron and colloidal sulfur. A simplified kinetic model was developed, calibrated, and integrated into a facility-wide model to simulate the process at the full-scale. Experimental results showed that dosing hydrogen peroxide, even at doses lower than the stoichiometrically required to oxidize iron sulfide, freed, and oxidized sulfide bound ferrous iron to ferric iron, which was consequently hydrolyzed and affected phosphorus removal. Higher dosing of hydrogen peroxide did not affect change in the speciation of sulfur remaining predominantly as elemental sulfur. Simulations showed that the application of PRI-SC with supplemental ferric iron dosing was able to cut the costs of chemicals addition up to 53% while maintaining a steady-state effluent phosphate concentration below 0.01 mg/L. PRACTITIONER POINTS: The kinetic model was used to optimize ferric iron and hydrogen peroxide dosing. The developed model can be integrated in existing wastewater process simulators. Dosing hydrogen peroxide effectively oxidized ferrous iron to ferric iron. The combination of hydrogen peroxide and iron salts can reduce the chemical addition cost by 53%.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".