Uncertainty analysis of WWTP control strategies made feasible
Bibliographic record
Abstract
The control of wastewater treatment plants can help to achieve good effluent quality, in a complex, highly non-linear environment. A key but time-demanding component of such modelling studies is uncertainty analysis (UA). The general aims of this paper are (a) to evaluate methods for reduction of the time necessary to conduct an UA, and (b) to evaluate the sensitivity of parameters and model subsystems. Two UA studies on the Benchmark Simulation Model no. 2 (BSM2) are used to illustrate how the above mentioned aims can be achieved: (1) robustness of performance evaluations against changing operation and design conditions; and (2) uncertainty of performance evaluations for a given plant layout and operation. The main conclusions are: (1) solver settings have a large impact on simulation speed and require proper attention; (2) to reach convergence in Monte Carlo simulations with Latin Hypercube Sampling, the number of simulations should be at least 50 times the number of sampled parameters, which is more than what is reported in similar studies; and (3) the number of uncertain parameters that needs to be considered to make a proper uncertainty assessment of a model can be reduced significantly by omitting parameters that have little influence.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".