A benchmark simulation to verify an inhibition model on decay stage for nitrification
Bibliographic record
Abstract
Activated Sludge Models (ASMs) are widely used for biological wastewater treatment plant design, optimisation and operation. In commonly used ASMs, the nitrification process is modelled as a one-step process. However, in some process configurations, it is desirable to model the concentration of nitrite nitrogen through a two-step nitrification process. In this study, the benchmark datasets published by the Water Environment Research Foundation (WERF) were used to develop a two-step nitrification model considering the kinetics of Ammonium Oxidising Bacteria (AOB) and Nitrite Oxidising Bacteria (NOB). The WERF datasets were collected from a chemostat reactor fed about 1,000 mg-NH3-N/L synthetic influent with at different sludge retention times of 20, 10 and 5-d, whereas the pH in the reactor varied in the range of 5.8 and 8.8. Supplemental laboratory batch experiments were conducted to assess the toxicity of nitrite-N on nitrifying bacteria. These tests suggested that 500 mg-N/L of nitrite at pH 7.3 was toxic to NOB and resulted in continuous decrease in bulk oxygen uptake rate. To model this phenomenon, a poisoning model was used instead of the traditional Haldane-type inhibition model. The poisoning model for NOB and AOB with different threshold poisonings for unionised NO2-N and NH3-N concentrations could successfully reproduce the three WERF datasets.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".