Real-Time Operation of Water-Supply Canal Systems under Limited Electrical Power and/or Water Availability
Bibliographic record
Abstract
Water-supply systems (WSSs) and electrical power systems (EPSs) are highly interdependent critical infrastructures. The electrical energy required for pumping in WSSs and cooling water required for power plants in EPSs are major interdependencies. Failure of either of the two independently operated infrastructures can lead to a cascading failure of both the systems. A combined operations control methodology for WSSs and EPSs taking into consideration the inherent interdependencies is required to ensure reliable operations. An optimization-simulation model is presented for the real-time operation of water-supply canal systems (WSCSs) under critical conditions during short-term and long-term emergency events such as limited electrical energy and/or limited water availability, electrical grid failures, extreme droughts, or other severe conditions related to natural and manmade disasters. WSCSs are used for the conveyance of raw water from sources such as lakes, reservoirs, or rivers to water treatment plants that supply treated water to consumers through water distribution systems (WDSs). The approach interfaces the optimization-simulation model for WSCSs with an optimization-simulation model for WDSs to provide for a comprehensive decision-making tool for the control of WSCSs and WDSs. Two WSCSs optimization methodologies are presented including a nonlinear programming approach and an optimization-simulation approach that interfaces a genetic algorithm (MATLAB) with the US Army Corps of Engineers Hydraulic Engineering Center’s (HEC) River Analysis System (HEC-RAS) simulation model. A steady-state analysis of the WSCSs is performed for each time period of operation. The new methodologies for determining pump and gate operations under limited power and/or water availability are illustrated using two example canal systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".