Process Modeling and Its Application in Municipal Wastewater Treatment Plant Based on Seasonal Temperature Variations: A Case Study in Eastern China
Bibliographic record
Abstract
Based on the impact of seasonal temperature variations on wastewater treatment plants (WWTPs), a mathematical model of the Anaerobic–Anoxic–Oxic (AAO) process at a municipal WWTP in Eastern China was developed using GPS-X 8.5 software. A sensitivity analysis was conducted on 128 parameters, and key influential parameters were identified and adjusted accordingly. The model’s accuracy was validated using historical monitoring data, and the validation confirmed its ability to reflect operational conditions across different seasons. To address seasonal challenges observed in historical data, several scenarios were simulated. The results show that the maximum treatment capacity of the WWTP is approximately 125% of the design capacity. Under low winter temperatures, the treatment efficiency can be enhanced by reducing the dissolved oxygen (DO) levels in the oxic tank to 1.5–2 mg/L and increasing both the internal reflux ratios to approximately 150% and external reflux ratios to 100%. During summer rainstorms, the risk of exceeding the discharge limits can be mitigated by appropriately increasing the dosage of the flocculant poly-aluminum chloride (PAC). Additionally, carbon source supplementation strategies were proposed based on varying influent carbon-to-nitrogen ratios (C/N). These findings provided precise operational strategies for the WWTP, effectively reducing the effluent concentrations of COD, TN, NH4+-N, and TP by 3.1%, 12.7%, 24.1%, and 18.9%, respectively, while also achieving a 24.2% reduction in the carbon source dosage.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".