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A Model of time Variability of Characteristic Parameters of the Water Distribution System as a Base of Information and the Basis of Mathematical Modelling

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The subject of the paper is the Bialystok water supply network. The studies on the Bialystok water supply network are aimed at identifying new, more rational ways of operation (decreasing the pressure value) and defining the conditions for expansion and modernization of the water supply network, with the particular system analysis as a part of identifying the most dangerous places on the network. The increase of requirements concerning the quality and the time of realisation in the scope of the design studies requires the introduction of computer technology in calculations. The work presents the processes based on mapping and refinement of specific conservative states of the water distribution system (SDW), using mathematical modelling taking into account the latest available computer techniques.
Rocznik
Strony
46--51
Opis fizyczny
Bibliogr. 12 poz., rys.
Twórcy
  • Department of Technology and Systems of Environmental Engineering, Bialystok University of Technology, ul. Wiejska 45E, 15-351 Bialystok, Poland
Bibliografia
  • 1. Abe N., Peter B.C. 2010. Epanet Calibrator – An integrated computational tool to calibrate hydraulic models. Integrating Water Systems Boxall & Maksimovic.
  • 2. Boulos P.F., Lansey K.E., Kamey B.W. 2009. Comprehensive Water Distribution Systems Analysis Handbook for Engineers and Planners, Pasadena, MWH Soft, California.
  • 3. Darsono S., Labadie J.W 2007. Neural-optimal control algorithm for real-time regulation of in-line storage in combined sewer systems. Environmental Modelling & Software, 22, 1349–1361.
  • 4. Knapik K. 2000. Dynamiczne modele w badaniach sieci wodociągowych. Kraków.
  • 5. Machón I., López H., Rodriguez-Iglesias J., Marañón E.,Vázquez I. 2007. Simulation of a coke wastewater nitrification process using a feed-forward neuronal net. Environmental Modelling & Software, 22, 1382–1387.
  • 6. Mays L.W. 2005. The role of risk analysis in water resources engineering. Department of Civil and Environmental Engineering, Arizona State University. www.public.asu.edu/lwmays, 8–12.
  • 7. Michaud D., Apostolakis G.E. 2006. Methodology for ranking elements of water supply networks. Journal of Infrastructure Systems, 12(4), 230–242.
  • 8. Sadiq R., Saint-Martin E., Kleiner Y. 2008. Predicting risk of water quality failures in distribution networks under uncertainties Rusing fault-tree analysis. Urban Water, 5(4), 287–304.
  • 9. Studziński J. 2014. Some algorithms supporting the water network management by use of simulation of network hydraulic model. Industrial Simulation Conference (ISC), 11–13.06.2014, Hoegskolan, EUROSIS 2014, 33–37.
  • 10. Trębicka A. 2018. Dynamic model of the water distribution system as an analysis tool in the management of the Łapy water supply network. Economic and Environment, 3, 118–126.
  • 11. Trębicka A. 2018. Efficiency and optimum decisions in the modeling process of water distribution. Journal of Ecological Engineering, 19(6), 254–258.
  • 12. Walski T., Chase D.V., Sawicki D.A. 2011. Water distribution modeling. HaestadPress, Waterbury, CT, USA.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-fb605af9-39f6-4fb2-bb72-ffd0f0f9e693
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