Design Strategy for a Simultaneous Nitrification/Denitrification of a Slaughterhouse Wastewater in a Sequencing Batch Reactor: ASM2D Modeling and Verification
Bibliographic record
Abstract
Sequencing Batch Reactor (SBR) was used to treat slaughterhouse wastewater which contains average Chemical Oxygen Demand (COD) concentration of 5000 mg l(-1) and ammonium of 360 mgN l(-1). Nitrification/denitrification process was conducted for nitrogen removal. The influent wastewater as internal carbon source and sodium acetate as an external one was used for completing denitrification to achieve the simultaneous organic matter removal (95-96%) and nitrogen removal (95-97%). In addition, the dynamic SBR simulation model for biological nitrogen removal based on the Activated Sludge Model No. 2d (ASM2d) and GPS-X software is presented. The experimental study for the calibration and validation of the model was carried out using laboratory SBR. The study showed that the model provides a powerful tool to reduce the experimental expenditure and time to find the optimum strategy.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".