Phosphorus Co-Precipitation in the Biological Treatment of Slaughterhouse Wastewater in a Sequencing Batch Reactor
Bibliographic record
Abstract
The effect of phosphorus co-precipitation with ferric chloride (FeCl3) dosing on biological phosphorus (P) along with carbon (C) and nitrogen (N) removal was investigated in a sequencing batch reactor (SBR) for slaughterhouse wastewater treatment. Additional phosphorus removal due to chemical co-precipitation was evaluated as the difference in System P removal between Phase 1 (control—without FeCl3 dosing) and Phase 3 (with FeCl3 dosing). Phase 2 was mainly studied to improve nitrification/denitrification process with acetate addition by Filali-Meknassi et al. in 2004. Both systems (Phases 1 and 3) exhibited high P removal but a co-precipitation with FeCl3 dosing only allowed us to have an orthophosphates (o-PO4) concentration below 1mgP∕L in the effluent. Without FeCl3 addition, the total P concentration was reduced from 85±12 to 14±2mgP∕L (84% removal), whereas with the addition of FeCl3 an additional 11mgP∕L was removed bringing the effluent P concentration to 3mgP∕L (as total P). Although, during simulation of aerobic phase, a release of phosphorus was observed. The study showed that the ASM2d model with the adjustment (calibration) of seven kinetic parameters (ηNO3, μAUT, bAUT, KO2, KNH4, Kh, μPAO, and bPAO) was capable of predicting the behavior of the laboratory SBR activated sludge and provided the profile of nutrients (phosphorus, NH4–N, NOX–N, and COD) removal.
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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 | 0.000 |
| 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) | 0.000 | 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".