On the Potential of Global Reactive Real Time Control in Reducing Overflows from Combined Sewers' Interceptors
Bibliographic record
Abstract
We proposed in this paper a RTC strategy allowing surcharged flows in combined interceptors in order to minimize volumes of untreated water discharged to receiving waters during rain events. The strategy uses a newly developed hydraulic model to compute the impact of the controlled variables' values on flows and water depths in the sewer. The selection of the controlled variables' bounds and the formulation of the objective function to be minimized guarantee the convexity of this last function. Consequently, use of a robust local optimizer is adequate to find its global minimum. Application of the elaborated control strategy on the combined interceptor of the city of Laval, Canada confirmed that allowing surcharged flows in the interceptor leads to considerable decreases in total overflow volumes. It was also shown that, assuming proper locations of the controlled regulators, a global reactive control strategy was adequate when controlling a surcharged sewer. Many operational benefits can be gained from the application of a reactive rather than predictive control. Among others, reactive control eliminates the need to resort to rainfall predictions when planning regulation actions; problems related to the acquisition of these data and to their associated uncertainties can then be eradicated. However, surcharged flows are seldom allowed in every pipe of a sewer network to control and some sections always remain under the free surface regime. More studies should be conducted on such sewers to evaluate the impact of the prediction horizon length on overflow volumes and on compliance with operational constraints when free surface flows occur. Conclusions of this paper regarding global reactive control of surcharged interceptors should also be corroborated by optimizations performed on other combined interceptors and using different rain events. A strategy to determine the optimal number and position of the controlled regulators could then be defined according to the results obtained.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".