An efficient approach to automate the manual trial and error calibration of activated sludge models
Bibliographic record
Abstract
An efficient approach is introduced to help automate the rather tedious manual trial and error way of model calibration currently used in activated sludge modeling practice. To this end, we have evaluated a Monte Carlo based calibration approach consisting of four steps: (i) parameter subset selection, (ii) defining parameter space, (iii) parameter sampling for Monte Carlo simulations and (iv) selecting the best Monte Carlo simulation thereby providing the calibrated parameter values. The approach was evaluated on a formerly calibrated full-scale ASM2d model for a domestic plant (located in The Netherlands), using in total 3 months of dynamic oxygen, ammonia and nitrate sensor data. The Monte Carlo calibrated model was validated successfully using ammonia, oxygen and nitrate data collected at high measurement frequency. Statistical analysis of the residuals using mean absolute error (MAE), root mean square error (RMSE) and Janus coefficient showed that the calibrated model was able to provide statistically accurate and valid predictions for ammonium, oxygen and nitrate. This shows that this pragmatic approach can perform the task of model calibration and therefore be used in practice to save the valuable time of modelers spent on this step of activated sludge modeling. The high computational demand is a downside of this approach but this can be overcome by using distributed computing. Overall we expect that the use of such systems analysis tools in the application of activated sludge models will improve the quality of model predictions and their use in decision making.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".