Treatment of slaughterhouse wastewater in a sequencing batch reactor: Simulation vs experimental studies
Bibliographic record
Abstract
In wastewater treatment, the objective of process optimization is primarily to obtain a good treatment efficiency of a specific pollutant. The operational objective of increased productivity has also to be met. This includes a sufficient reduction in the duration of a batch process through batch scheduling. The aim of this paper is thus to find the best cycles for simultaneous carbon, nitrogen and phosphorus (CNP) removal from slaughterhouse wastewater in a sequencing batch reactor (SBR) using GPS-X software and ASM2d model. Simulations with different aeration strategies, residence time, sludge age and feed strategies were carried out to determine the best system performance. The simulation results showed best performance with a system comprised of two equal feeds operated at 48 h hydraulic retention time (HRT) and 20 d solids retention time (SRT). Simulation also showed that addition of metal salts was necessary to reduce the level of phosphorus (P) to meet the requirement (P<1 mg l(-1)). The addition of acetate was also necessary to complete the denitrification process. The simulated results were compared against the experimental results obtained from laboratory SBR. The simulated results of COD, nitrates/nitrites and ammonia removal were very close to the experimental results. A diference of 2-4% between the simulated COD and the experimental COD was observed and that could be attributed to the error in evaluation of the inert COD. For ammonia removal, the simulated (99.9%) and experimental (93-100%) results were practically identical. However, a notable difference in o-PO4 concentration was observed (38% removal by simulation against 78% removal through experiments). After metallic salts addition, P removal efficiency was 98% or 1% less than that observed through experimental results.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".