Pressure Sensor Placement for Leak Diagnosis under Demand Uncertainty in Water Distribution Systems
Notice bibliographique
Résumé
Leakages in concealed pipes in urban water distribution systems (WDS) can cause losses of up to 25% of potable water supply in municipalities. These losses are not only a tremendous waste of water but also the energy spent to treat and distribute it. Techniques for leak detection and localization in WDS have evolved considerably since the mid-1950s. Among these methods, model-based leak diagnosis methods (MFD) have been extensively studied in the literature, as they are more economical compared to others. MFD methods infer the existence and position of leaks based on continuously monitoring pressure levels in the WDS and comparing these to the expected values obtained from simulating a calibrated hydraulic model of the WDS. In the event of an anomaly (e.g., a leak), the sampled pressure levels (measured by the sensors) should significantly deviate from expected values which are obtained by simulation under an assumed no-leak condition. Although the methodology is efficient in terms of the number of required sensors and operational person-hours, it is at risk of failing to distinguish between the effect of leaks and water demand variations. This is because both leaks and demand fluctuations have a similar change on pressure levels along the network. This study aims to improve the robustness of the MFD method by explicitly considering the uncertainty in the nodal demands across the WDS. The influence of demand uncertainty on nodal pressure is analyzed by generating model-based system responses that are time-variable and conditional on known data (e.g., total demand across the WDS). Monte Carlo methods are used to generate conditional realizations of spatially variable sets of nodal demands such that simulated states match the available observed system states at the time any pressure observation is sampled. After characterizing the distributions of expected nodal pressures under the no-leak condition, a statistical detection test is defined that asserts the existence of a leak based on evidence from comparing the observations with their corresponding distributions. The performance of the proposed detection analysis is then evaluated in response to multiple synthetic leak and no-leak scenarios. To fine-tune the configuration of the detection test design parameters, its performance is evaluated by computing the false positive and false negative rates across the leak and no-leak scenarios. These two metrics are utilized to solve the sensor placement optimization problem as a multi-objective optimization problem. Results in two synthetic WDS case studies show that under the most influential source of uncertainty in WDS modelling (nodal demands), the proposed detection test functions well and multi-objective optimization can lead to robust sensor placement and other valuable insights.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».