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

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  • Yihong Guan

    (Qingdao University of Technology)

  • Mou Lv

    (Qingdao University of Technology)

  • Shen Dong

    (Qingdao University of Technology)

Abstract

Model-based techniques can accurately locate the vicinity of leak localization. However, in the traditional hydraulic leakage model (THLM), the nodal flow is composed of the actual water consumption and the background leakage calculated in equal proportion, without considering the influence of pressure on the background leakage. Therefore, in this research, the parameter α is obtained through a pipe network experiment. According to the relationship between the background leakage and the pressure, the parameter β is calculated with a nonlinear genetic algorithm (NGA). The emission coefficient C is obtained based on the length of the pipeline, and then the pressure-driven background leakage model (PDBLM) is built to detect the leak localization using a multi-population genetic algorithm (MPGA). Through the simulation results under three working conditions, it is concluded that the PDBLM model is closer to the operation of the actual pipe network than the THLM model. Additionally, the PDBLM-based Inverse Problem Leak Localization Model can find the actual leakage point more accurately, improve leakage detection efficiency, and reduce water loss.

Suggested Citation

  • Yihong Guan & Mou Lv & Shen Dong, 2023. "Pressure-driven Background Leakage Models and their Application for Leak Localization Using a Multi-population Genetic Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(1), pages 359-373, January.
    • Handle: RePEc:spr:waterr:v:37:y:2023:i:1:d:10.1007_s11269-022-03377-w
    DOI: 10.1007/s11269-022-03377-w
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    References listed on IDEAS

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    1. Robles-Velasco, Alicia & Cortés, Pablo & Muñuzuri, Jesús & Onieva, Luis, 2020. "Prediction of pipe failures in water supply networks using logistic regression and support vector classification," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
    2. Mohanaprasad Kothandaraman & Zijian Law & Morris A. G. Ezra & Chang Hong Pua & Uma Rajasekaran, 2022. "Water Pipeline Leak Measurement Using Wavelet Packet-based Adaptive ICA," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(6), pages 1973-1989, April.
    3. Perpar, Matjaž & Rek, Zlatko, 2020. "Soil temperature gradient as a useful tool for small water leakage detection from district heating pipes in buried channels," Energy, Elsevier, vol. 201(C).
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    Cited by:

    1. Pham Duc Dai, 2023. "A Real Time Optimization Based Sequential Convex Program for Pressure Management in Water Distribution Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(12), pages 4751-4768, September.
    2. Chia-Cheng Shiu & Chih-Chung Chung & Tzuping Chiang, 2024. "Enhancing the EPANET Hydraulic Model through Genetic Algorithm Optimization of Pipe Roughness Coefficients," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(1), pages 323-341, January.

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