Chemical phosphorus removal model based on equilibrium chemistry
Bibliographic record
Abstract
Regulations in many regions of the world require total phosphorus (TP) levels lower than 0.10 mgP/L (100 microgP/L) in effluents, resulting in the need to achieve very low ortho-phosphate (OP) concentrations. Chemical precipitation is a widely used technology for controlling effluent OP discharge, either on its own or supplementing biological methods. The various chemical and physico-chemical mechanisms that result in extremely low residual OP levels are complex and depend on pH. In practice, engineering calculations frequently use an empirical precipitation model. This model requires pH as input and predicts the lowest achievable OP residual of 35 microgP/L at a narrow optimum pH of 6.9-7.0, when an excess of ferric is added. The model has been combined with a biokinetic and weak acid/base chemistry based pH model, to allow accurate prediction of pH, OP residuals and chemical sludge production. Analysis of effluent data from the Blue Plains plant shows that residuals as low as 10 microgP/L OP can be achieved regularly, over a wider pH range. The precipitation model was recalibrated to match the newly available data. Subsequently it was compared with a new, mechanistic precipitation model based on solubility and dissociation constants for actual chemical compounds. The need for more accurate measurement of extremely low OP concentrations and considering the role of organics, adsorption and coagulation in chemical phosphorus removal is demonstrated.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".