Application of Real-Time Control and Source Control Solutions to Reduce Combined Sewer Overflows: A Review of Approaches and Performances
Bibliographic record
Abstract
Real-time control (RTC) and source control solutions (SCSs) can be cost-effective and reliable ways to improve the performance and mitigate the negative impacts of urban drainage systems. In this paper, we review and discuss different approaches to applying RTC and SCSs for combined sewer overflow management. Applications of RTC and SCSs have been classified into three main categories in previous studies: (1) RTC applied individually to a sewer system, (2) RTC applied to a sewer system in addition to passive SCSs, and (3) RTC applied directly to SCSs. In this paper, we compare the RTC techniques and strategies typically employed within each category. We highlight the benefits and point out major gaps and issues that have not been investigated previously with the aim of promoting the long-term implementation of RTC for urban drainage systems. The findings indicate that RTC of urban drainage systems has been widely applied in different contexts and with different control objectives, but few studies have implemented RTC and SCSs in combination. Significant improvements have been observed in terms of both water quality and quantity when RTC and SCSs are integrated; however, more studies are required in order to resolve some technical issues and to investigate the cost effectiveness of individual and combined solutions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".