Estimation of Water Demand in Water Distribution Systems Using Particle Swarm Optimization
| Autor: | Yskandar Hamam, Adnan M. Abu-Mahfouz, Lawrence K. Letting |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Mathematical optimization
lcsh:Hydraulic engineering Discretization Underdetermined system underdetermined model Computation 0208 environmental biotechnology Geography Planning and Development Evolutionary algorithm water demand estimation demand multipliers uncertain measurements particle swarm optimization 02 engineering and technology 010501 environmental sciences Aquatic Science 01 natural sciences Biochemistry lcsh:Water supply for domestic and industrial purposes lcsh:TC1-978 Econometrics Economics Process simulation 0105 earth and related environmental sciences Water Science and Technology lcsh:TD201-500 Particle swarm optimization 020801 environmental engineering Water demand Multiplier (economics) |
| Zdroj: | Water; Volume 9; Issue 8; Pages: 593 Water, Vol 9, Iss 8, p 593 (2017) |
| ISSN: | 2073-4441 |
| DOI: | 10.3390/w9080593 |
| Popis: | Demand estimation in a water distribution network provides crucial data for monitoring and controlling systems. Because of budgetary and physical constraints, there is a need to estimate water demand from a limited number of sensor measurements. The demand estimation problem is underdetermined because of the limited sensor data and the implicit relationships between nodal demands and pressure heads. A simulation optimization technique using the water distribution network hydraulic model and an evolutionary algorithm is a potential solution to the demand estimation problem. This paper presents a detailed process simulation model for water demand estimation using the particle swarm optimization (PSO) algorithm. Nodal water demands and pipe flows are estimated when the number of estimated parameters is more than the number of measured values. The water demand at each node is determined by using the PSO algorithm to identify a corresponding demand multiplier. The demand multipliers are encoded with varying step sizes and the optimization algorithm particles are also discretized in order to improve the computation time. The sensitivity of the estimated water demand to uncertainty in demand multiplier discrete values and uncertainty in measured parameters is investigated. The sensor placement locations are selected using an analysis of the sensitivity of measured nodal heads and pipe flows to the change in the water demand. The results show that nodal demands and pipe flows can be accurately determined from a limited number of sensors. |
| Databáze: | OpenAIRE |
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