Robust Booster Disinfection Scheduling Using Incomplete Mixing Water Quality Model (EPANET-IMX)
Bibliographic record
Abstract
In the realm of practical water distribution systems (WDS), uncertainties in hydraulic and water quality modeling affect the management of WDS making it a multifaceted challenge. Furthermore, the incorporation of water quality considerations into WDS design and management has become paramount, necessitating the use of precise water quality models. The conventional water quality models employed in drinking WDS have faced inaccuracy due to their underlying assumption of instantaneous and complete mixing at junctions. To address this limitation, recent models such as EPANET-IMX (Incomplete Mixing Extension), EPANET-BAM, and AZRED have emerged, incorporating empirical equations to model the nuances of incomplete mixing. These advancements offer improved accuracy for water quality analysis within WDS. However, the presence of uncertainty in disinfectant reaction rates also presents an obstacle to achieving optimal water quality management. Within such systems, determining the scheduling of booster disinfectant dosages is a challenge. In response, this study seeks to determine the optimal dosage schedule for booster disinfectants while accounting for fluctuations in bulk reaction rate coefficients and acknowledging incomplete mixing at junctions. To tackle this uncertain optimization problem, robust optimization principles are employed. The study applies these principles to a small-scale network as an illustrative example, showcasing robust optimal schedules. Two water quality models, namely EPANET and EPANET-IMX, are utilized for water quality simulations. The resulting optimal schedules are compared and analyzed across all three models. It was observed that considering the incomplete mixing varied the optimal booster dosage by about 10%. The findings emphasized the importance of considering incomplete mixing in both water quality analysis and optimization endeavors.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".