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Smart optimisation and sensitivity analysis in water distribution systems

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dc.contributor.author Page, Philip R
dc.date.accessioned 2017-09-04T12:17:17Z
dc.date.available 2017-09-04T12:17:17Z
dc.date.issued 2015-12
dc.identifier.citation Page, P.R. 2015. Smart optimisation and sensitivity analysis in water distribution systems. Proceedings of Smart and Sustainable Built Environments (SASBE) 2015, 9-11 December 2015, University of Pretoria, South Africa, 9pp. en_US
dc.identifier.uri http://sasbe2015.com/wp-content/uploads/2015/09/SASBE201522_Page.pdf
dc.identifier.uri http://www.irbnet.de/daten/iconda/CIB_DC28839.pdf
dc.identifier.uri http://hdl.handle.net/10204/9524
dc.description Copyright: 2015. University of Pretoria. Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, kindly consult the publisher's website. The item may also be downloaded free of charge from the following website: http://www.irbnet.de/daten/iconda/CIB_DC28839.pdf en_US
dc.description.abstract Parameter uncertainty in water pipe network models are studied using newly developed simplified mathematical notions. These enable studies to be done using public domain software, including EPANET. The results obtained can be easier to use and interpret than those obtained from more general mathematical notions. The general idea is to study how a flow- and pressure-related quantity varies as a set of state parameters are varied. The quantity considered here is the average pressure, enabling smart optimisation of a water distribution system by keeping the average pressure unchanged as water demands change, by changing the speed of the pumps. Another application area considered, using the same mathematical notions, is the study of the sensitivity of parameters. Two models are analysed as examples, showing how smart optimisation works, and what the sensitivity of various sets of parameters are. The various parameter categories have very different sensitivities to a given change in the average pressure that can be tolerated. The critical state parameters to determine accurately in the models depend on the network. For the combined schemes studied as examples, variation of the pressure with reservoir depths is only related to the reservoir depths, and the pressure does not vary with the tank diameters. There is a relationship between variations of the various pipe parameters for both the Hazen-Williams and Chezy-Manning pipe major friction loss formulas. It holds for any network where there are no pipe minor friction losses. Pipe diameters are the most sensitive, pipe roughness coefficients are medium sensitive, and pipe lengths are the least sensitive. en_US
dc.language.iso en en_US
dc.publisher University of Pretoria en_US
dc.relation.ispartofseries Worklist;16028
dc.subject Water pipe network models en_US
dc.subject Smart water distribution systems en_US
dc.subject Reservoir depths en_US
dc.subject Proceedings of Smart and Sustainable Built Environments (SASBE) 2015 en_US
dc.title Smart optimisation and sensitivity analysis in water distribution systems en_US
dc.type Conference Presentation en_US
dc.identifier.apacitation Page, P. R. (2015). Smart optimisation and sensitivity analysis in water distribution systems. University of Pretoria. http://hdl.handle.net/10204/9524 en_ZA
dc.identifier.chicagocitation Page, Philip R. "Smart optimisation and sensitivity analysis in water distribution systems." (2015): http://hdl.handle.net/10204/9524 en_ZA
dc.identifier.vancouvercitation Page PR, Smart optimisation and sensitivity analysis in water distribution systems; University of Pretoria; 2015. http://hdl.handle.net/10204/9524 . en_ZA
dc.identifier.ris TY - Conference Presentation AU - Page, Philip R AB - Parameter uncertainty in water pipe network models are studied using newly developed simplified mathematical notions. These enable studies to be done using public domain software, including EPANET. The results obtained can be easier to use and interpret than those obtained from more general mathematical notions. The general idea is to study how a flow- and pressure-related quantity varies as a set of state parameters are varied. The quantity considered here is the average pressure, enabling smart optimisation of a water distribution system by keeping the average pressure unchanged as water demands change, by changing the speed of the pumps. Another application area considered, using the same mathematical notions, is the study of the sensitivity of parameters. Two models are analysed as examples, showing how smart optimisation works, and what the sensitivity of various sets of parameters are. The various parameter categories have very different sensitivities to a given change in the average pressure that can be tolerated. The critical state parameters to determine accurately in the models depend on the network. For the combined schemes studied as examples, variation of the pressure with reservoir depths is only related to the reservoir depths, and the pressure does not vary with the tank diameters. There is a relationship between variations of the various pipe parameters for both the Hazen-Williams and Chezy-Manning pipe major friction loss formulas. It holds for any network where there are no pipe minor friction losses. Pipe diameters are the most sensitive, pipe roughness coefficients are medium sensitive, and pipe lengths are the least sensitive. DA - 2015-12 DB - ResearchSpace DP - CSIR KW - Water pipe network models KW - Smart water distribution systems KW - Reservoir depths KW - Proceedings of Smart and Sustainable Built Environments (SASBE) 2015 LK - https://researchspace.csir.co.za PY - 2015 T1 - Smart optimisation and sensitivity analysis in water distribution systems TI - Smart optimisation and sensitivity analysis in water distribution systems UR - http://hdl.handle.net/10204/9524 ER - en_ZA


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