Probabilistic modelling and evaluation of wastewater treatment plant upgrades in a water quality based evaluation context
Bibliographic record
Abstract
Process choice and dimensioning of wastewater treatment plants (WWTPs) is difficult while ensuring regulatory standards are met and cost-efficiency is maintained. This step only accounts for a small fraction of the upfront costs, but can lead to substantial savings. This paper illustrates the results of a systematic methodology to evaluate system upgrade options by means of dynamic modelling. In contrast to conventional practice, the presented approach allows the most appropriate trade-off between cost of measures and effluent quality to be chosen and the reliability of a process layout to be assessed by means of uncertainty analysis. In a hypothetical case study, thirteen WWTP upgrade options are compared in terms of their effluent quality and economic performance. A further comparison of two options with regard to the resulting receiving water quality reveals the paramount importance of this aspect, and highlights the inadequacy of evaluation frameworks limited to the performance relative to a sub-system (WWTP effluent) when a wider perspective (as induced by the EU Water Framework Directive) has to be adopted.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".