An Optimization Framework for Pressure Monitoring in Water Distribution Networks: Coupling Krill Herd Algorithm with Monte Carlo-Based Uncertainty Analysis.
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| Title: | An Optimization Framework for Pressure Monitoring in Water Distribution Networks: Coupling Krill Herd Algorithm with Monte Carlo-Based Uncertainty Analysis. |
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| Authors: | Feng, Peng1 (AUTHOR) fengpengccj@163.com, Dong, Shen1,2 (AUTHOR) dongshends@126.com, Li, Hang1 (AUTHOR) 18561688266@163.com, Lv, Mou1 (AUTHOR) qdlvmou@yeah.net |
| Source: | Water Resources Management. Jun2026, Vol. 40 Issue 8, p1-17. 17p. |
| Subject Terms: | *Optimization algorithms, *Monte Carlo method, *Pressure sensors, *Infrastructure (Economics), *Stochastic analysis |
| Abstract: | Optimal placement of pressure monitoring points in the water distribution network (WDN) is essential for ensuring its efficient and reliable operation. Strategic placement of pressure monitoring points enables the system to have effective surveillance and management. However, the problem of parameter equifinality persists in the optimal placement of pressure monitoring points using intelligent optimization algorithms. Recognizing the inherent uncertainty in nodal water demands, this study utilized the Monte Carlo simulation to analyze network behaviors modelled as a normal distribution. A nodal pressure fluctuation coefficient was introduced to quantify the impact of demand stochasticity on the WDN. This metric was then combined with nodal pressure sensitivity to resolve parameter equifinality in the configuration of pressure monitoring points. An optimization model was developed by integrating these factors, and the Krill Herd Algorithm (KHA) was applied to solve the model and determine the optimal configuration of pressure monitoring points. During the simulation, 20,000 random samples were generated based on the steady-state hydraulic model as benchmarks. By calculating and integrating the nodal pressure fluctuation coefficients and nodal sensitivities, the optimal scheme was determined to resolve the issue of parameter equifinality. Quantitative evaluations demonstrated that the preferred layout outperforms alternative candidate schemes by at least 10% in overall monitoring efficacy. This approach provides a robust framework for the strategic design of pressure monitoring point placement schemes for the WDN. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 193809595 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: An Optimization Framework for Pressure Monitoring in Water Distribution Networks: Coupling Krill Herd Algorithm with Monte Carlo-Based Uncertainty Analysis. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Feng%2C+Peng%22">Feng, Peng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> fengpengccj@163.com</i><br /><searchLink fieldCode="AR" term="%22Dong%2C+Shen%22">Dong, Shen</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> dongshends@126.com</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Hang%22">Li, Hang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 18561688266@163.com</i><br /><searchLink fieldCode="AR" term="%22Lv%2C+Mou%22">Lv, Mou</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> qdlvmou@yeah.net</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Water+Resources+Management%22">Water Resources Management</searchLink>. Jun2026, Vol. 40 Issue 8, p1-17. 17p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22Monte+Carlo+method%22">Monte Carlo method</searchLink><br />*<searchLink fieldCode="DE" term="%22Pressure+sensors%22">Pressure sensors</searchLink><br />*<searchLink fieldCode="DE" term="%22Infrastructure+%28Economics%29%22">Infrastructure (Economics)</searchLink><br />*<searchLink fieldCode="DE" term="%22Stochastic+analysis%22">Stochastic analysis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Optimal placement of pressure monitoring points in the water distribution network (WDN) is essential for ensuring its efficient and reliable operation. Strategic placement of pressure monitoring points enables the system to have effective surveillance and management. However, the problem of parameter equifinality persists in the optimal placement of pressure monitoring points using intelligent optimization algorithms. Recognizing the inherent uncertainty in nodal water demands, this study utilized the Monte Carlo simulation to analyze network behaviors modelled as a normal distribution. A nodal pressure fluctuation coefficient was introduced to quantify the impact of demand stochasticity on the WDN. This metric was then combined with nodal pressure sensitivity to resolve parameter equifinality in the configuration of pressure monitoring points. An optimization model was developed by integrating these factors, and the Krill Herd Algorithm (KHA) was applied to solve the model and determine the optimal configuration of pressure monitoring points. During the simulation, 20,000 random samples were generated based on the steady-state hydraulic model as benchmarks. By calculating and integrating the nodal pressure fluctuation coefficients and nodal sensitivities, the optimal scheme was determined to resolve the issue of parameter equifinality. Quantitative evaluations demonstrated that the preferred layout outperforms alternative candidate schemes by at least 10% in overall monitoring efficacy. This approach provides a robust framework for the strategic design of pressure monitoring point placement schemes for the WDN. [ABSTRACT FROM AUTHOR] |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11269-026-04689-x Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 1 Subjects: – SubjectFull: Optimization algorithms Type: general – SubjectFull: Monte Carlo method Type: general – SubjectFull: Pressure sensors Type: general – SubjectFull: Infrastructure (Economics) Type: general – SubjectFull: Stochastic analysis Type: general Titles: – TitleFull: An Optimization Framework for Pressure Monitoring in Water Distribution Networks: Coupling Krill Herd Algorithm with Monte Carlo-Based Uncertainty Analysis. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Feng, Peng – PersonEntity: Name: NameFull: Dong, Shen – PersonEntity: Name: NameFull: Li, Hang – PersonEntity: Name: NameFull: Lv, Mou IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 09204741 Numbering: – Type: volume Value: 40 – Type: issue Value: 8 Titles: – TitleFull: Water Resources Management Type: main |
| ResultId | 1 |
