Application of Optimization Algorithms for Leakage Identification for Data Sparse Old Town
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Numerous cities developed in the 20th century have a Water Distribution Network(WDN) with decade-old pipes. Aging pipelines can leak with lower the water revenue ratio, thus resulting in waste and economic losses. In this study, new methods for localizing leakage in WDNs are proposed and tested. Two meta-heuristic methods based on Harmony Search(HS) and Genetic Algorithm(GA) are developed to detect leakage of WDNs through an evaluation of emitter coefficients. The old town of G-Town in South Korea has a 50-year-old WDN and a low water revenue ratio. High quality field measurements in G-Town allowed detailed testing of optimization methods based on emitter coefficients for leakage detection. As a result, both GA and HS yielded comparable outputs. The HS method performed with slightly better accuracy in minimizing the objective function than GA after about 1,000 iterations. Leakage detection becomes fairly accurate after approximately 30 minutes of optimization and pipe networks with more than 100 pipes and 50 nodes require more iterations and calculation time.
Abstract
Numerous cities developed in the 20th century have a Water Distribution Network(WDN) with decade-old pipes. Aging pipelines can leak with lower the water revenue ratio, thus resulting in waste and economic losses. In this study, new methods for localizing leakage in WDNs are proposed and tested. Two meta-heuristic methods based on Harmony Search(HS) and Genetic Algorithm(GA) are developed to detect leakage of WDNs through an evaluation of emitter coefficients. The old town of G-Town in South Korea has a 50-year-old WDN and a low water revenue ratio. High quality field measurements in G-Town allowed detailed testing of optimization methods based on emitter coefficients for leakage detection. As a result, both GA and HS yielded comparable outputs. The HS method performed with slightly better accuracy in minimizing the objective function than GA after about 1,000 iterations. Leakage detection becomes fairly accurate after approximately 30 minutes of optimization and pipe networks with more than 100 pipes and 50 nodes require more iterations and calculation time.
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