Pressure-driven Background Leakage Models and their Application for Leak Localization Using a Multi-population Genetic Algorithm

Guan, Yihong; Lv, Mou; Dong, Shen

doi:10.1007/s11269-022-03377-w

Cited by 6 publications

(2 citation statements)

References 27 publications

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“…GAs are biologically motivated adaptive computer techniques based on natural selection and genetic operators as Wang (1991), Naghibi et al (2017), Ding et al (2022), Shirajuddin et al (2023), and Guan et al (2023). These algorithms are often suggested to solve complex optimization problems by Zanfei et al (2020), Meirelles et al (2017), Di Nardo et al (2014), Do et al (2016), and Mambretti and Orsi (2016).…”

Section: Overall Conceptmentioning

confidence: 99%

Enhancing the EPANET Hydraulic Model through Genetic Algorithm Optimization of Pipe Roughness Coefficients

Shiu,

Chung,

Chiang

2023

Water Resour Manage

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Calibrating hydraulic models for water distribution systems (WDS) is crucial during model-building, particularly in determining the roughness coefficients of pipes. However, using a single roughness coefficient based solely on pipe material can lead to significant variations in frictional head losses. To address this issue and enhance computational efficiency, this study utilized genetic algorithm (GA) 1 for optimizing roughness coefficients with the Environmental Protection Agency Network Evaluation Tool (EPANET) hydraulic model. EPANET-GA further considers the spatial characteristics of pipes. The study incorporated an automated calibration process and a user graphic interface to analyze the water head pressures of WDS nodes for the Zhonghe and Yonghe districts. The model was optimized with 34,783 node items, 30,940 pipes, and 140 field measurements. Results reveal that the optimized roughness coefficient produces a high correlation coefficient (0.90) with the measured data in a time slot. Besides, a low standard error (8.93%) was achieved for 24-hour predictions. Furthermore, in the Shelin-Beitou district, the consideration of spatial characteristics was incorporated as constraints during the calibration process. The improved outcomes indicate that the EPANET-GA is a reliable reference for WDS design and routing scenarios in practice.

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Section: Overall Conceptmentioning

confidence: 99%

Enhancing the EPANET Hydraulic Model through Genetic Algorithm Optimization of Pipe Roughness Coefficients

Shiu,

Chung,

Chiang

2023

Water Resour Manage

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“…The results have showed that the ICA can localize multi simultaneous leakages with their priority. Guan et al 2023 proposed a model-based techniques to accurately locate the vicinity of leak localization in WDSs. Two hydraulic leakage model, namely traditional hydraulic leakage model (THLM) and the pressure-driven background leakage model (PDBLM), were considered for leakage detection using a multi-population genetic algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Real Time Optimization Based Sequential Convex Program for Pressure Management in Water Distribution Systems

Dai

2023

Water Resour Manage

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Optimal pressure regulation to water leakage reduction in water distribution systems (WDSs) is one of the most priority tasks for water utilities worldwide. Mathematically, this engineering problem can be casted into a nonlinear program (NLP) where decision variables are valve settings and relative speeds of pumps. With given demand parameters, by formulating and solving the NLP, control quantities will be computed and applied to the system for the control purpose. In practice, the demand parameters change continuously in time, and solving the formulated NLP requires a huge computation time, as a result, the control quantities obtained from the last NLP are remained to regulate the system until solution of the current NLP (with updated parameters) is available and so on.For this reason, how fast the solution of the NLP is deduced is vital for improving the control system performance. In this paper, we proposed to apply a new real time scheme based sequential convex program for computing approximated control profiles with respect to the change of water demand patterns. In particular, instead of solving the NLP with newly estimated demand pattern to full accuracy, only one convex NLP is solved in the computation framework of the sequential convex program (SCP) to get approximated solution with acceptable accuracy and in a near real time fashion. To demonstrate the efficacy of the real time optimization scheme, we apply it to determine fast control quantities for a real world WDS in Vietnam and one WDS benchmark for optimal pressure management. The results have demonstrated that, by applying such the optimization scheme, the obtained control profile gains acceptable accuracy as compared with the fully accurate one while the performance of control system is significantly enhanced.

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Advanced transformer model for simultaneous leakage aperture recognition and localization in gas pipelines

Li,

Wang,

Jiang

et al. 2024

Reliability Engineering & System Safety

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Pressure-driven Background Leakage Models and their Application for Leak Localization Using a Multi-population Genetic Algorithm

Cited by 6 publications

References 27 publications

Enhancing the EPANET Hydraulic Model through Genetic Algorithm Optimization of Pipe Roughness Coefficients

Enhancing the EPANET Hydraulic Model through Genetic Algorithm Optimization of Pipe Roughness Coefficients

A Real Time Optimization Based Sequential Convex Program for Pressure Management in Water Distribution Systems

Advanced transformer model for simultaneous leakage aperture recognition and localization in gas pipelines

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