Optimization of Real‐Time Control With Green and Gray Infrastructure Design for a Cost‐Effective Mitigation of Combined Sewer Overflows
Bibliographic record
Abstract
Abstract An innovative optimization‐simulation framework is applied to a case study of the Province of Quebec, Canada, to optimize the spatial distribution of green infrastructure (GI), the capacity and location of gray infrastructure, and the parameters specific to real‐time control (RTC) operating rules of a sewer system for reducing combined sewer overflows (CSOs) frequency and volume. GI, gray infrastructure, and RTC are applied either individually or in integration through eight optimization scenarios which are simulated over a nine‐year period of historical rainfall data. Among all scenarios, spatial optimization of GI with RTC leads to maximal CSO volume reduction (98%) and is the most cost‐effective option analyzed (70$/m3 of seasonal average CSO reduction compared to 140$/m3 for the scenario involving gray infrastructure alone). However, it requires a high GI implementation level and the CSO frequency under this scenario is sensitive to varying GI design parameters. The findings suggest that the best alternative for CSO control is the integration of the optimization of green and gray infrastructures with RTC as it still provides high CSO volume reduction (95%) and remains a cost‐effective solution (90$/m3 of CSO reduction), while providing robustness under cost and design uncertainties.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".