Chemical phosphorus removal to extremely low levels: experience of two plants in the Washington, DC area
Bibliographic record
Abstract
Chemical phosphorus removal using metal (iron and aluminium) salts is frequently used to control effluent soluble phosphorus levels in wastewater treatment plants. In the Washington DC area effluent phosphorus requirements are extremely stringent to protect the Chesapeake Bay. Full-scale data from two plants in the area were analysed to establish phosphate behaviour in the presence of iron. Titration experiments and mathematical modelling were performed to determine the role of ferric phosphate and hydroxide precipitation and other mechanisms that may potentially be involved in phosphorus removal. Iron addition is described in the model using a chemical equilibrium approach extended with surface charges and adsorption. The model verifies key observations from full-scale data: (a) extremely low orthophosphate levels can be achieved over a wide range of pH values, (b) a mixture of ferric phosphate and ferric hydroxide precipitate is forming with the hydroxide acting as sorbent, (c) molar ratios of Fe/P (iron dosed to phosphate removed) vary widely (1.0-3.9) based on the technology used and residual phosphate levels. The model will be a useful tool for engineers to optimise preliminary, simultaneous and tertiary P removal, both for design and plant operation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".