Investigation, biokinetic calculation, and modelling of a real combined industrial wastewater biological treatment process by activated sludge models
Bibliographic record
Abstract
Background: Treatment of combined industrial wastewater from industrial parks is one of the most complex and difficult wastewater treatment processes. Also, the accuracy of biological models for the prediction of the performance of these processes has not been sufficiently evaluated. Therefore, in this study, the International Association on Water Quality (IAWQ(-Activated Sludge Model No. 1 (ASM1) was implemented for the Jey industrial park in Isfahan province, Iran. Methods: The Jey IPWWTP process is a combination of anaerobic and aerobic biological processes. To evaluate the overall performance of IPWWTP, organic compounds, suspended solids, nutrients, attached biomass, and some operating parameters were measured during 6 months. Then, the biokinetic coefficients of aerobic processes were determined using Monod equations. Finally, the aerobic processes were modeled using ASM1 implemented in STOAT software. Results: The values of the biokinetic coefficients K, Y, Ks, Kd, and µmax were calculated as 2.7d- , 0.34 mg VSS/mg COD, 133.36 mg/L COD, 0.03d- , and 0.93d- , respectively. Based on the default coefficients and conditions of the ASM model, the difference between the experiments and model prediction was about 2 to 98%. After calibrating the ASM model, the difference between the experiments and prediction in all parameters was reduced to less than 10%. Conclusion: Investigations showed that the default coefficients and operation conditions of the ASM1 model do not have good predictability for complex industrial wastewaters and the outputs show a low accuracy compared to the experiments. After calibrating the kinetic coefficients and operating conditions, the model performance is acceptable and the predictions show a good agreement with the experiments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".