TY - GEN
T1 - Optimal sensors location using contamination detailed chemistry reactions
AU - Ohar, Ziv
AU - Lahav, Ori
AU - Ostfeld, Avi
N1 - Publisher Copyright: © 2015 ASCE.
PY - 2015
Y1 - 2015
N2 - Intrusion of contaminants into a water distribution system (WDS), deliberately or accidentally, is a potential risk facing water authorities and utilities. Deployment of methodologies for water quality sensor placements in water distribution systems is an approach for mitigation of such risks. This study incorporates a methodology for optimal placement of water quality sensors, through including contaminants detailed chemistry reactions. This is performed through using multispecies water quality model (EPANET-MSX) coupled with a statistical dose-response model. At the first stage, a series of contamination events are simulated, and the number of incident consumers' are evaluated. For each contamination event three decision variables are selected: injection location, contaminant type, and injection time. At the second stage, a genetic algorithm (GA) is invoked for selecting the optimal sensor placements through minimizing the average expected incidents. The method is demonstrated on an example application and two methods for events generation are compared and discussed.
AB - Intrusion of contaminants into a water distribution system (WDS), deliberately or accidentally, is a potential risk facing water authorities and utilities. Deployment of methodologies for water quality sensor placements in water distribution systems is an approach for mitigation of such risks. This study incorporates a methodology for optimal placement of water quality sensors, through including contaminants detailed chemistry reactions. This is performed through using multispecies water quality model (EPANET-MSX) coupled with a statistical dose-response model. At the first stage, a series of contamination events are simulated, and the number of incident consumers' are evaluated. For each contamination event three decision variables are selected: injection location, contaminant type, and injection time. At the second stage, a genetic algorithm (GA) is invoked for selecting the optimal sensor placements through minimizing the average expected incidents. The method is demonstrated on an example application and two methods for events generation are compared and discussed.
UR - http://www.scopus.com/inward/record.url?scp=84935089640&partnerID=8YFLogxK
U2 - https://doi.org/10.1061/9780784479162.076
DO - https://doi.org/10.1061/9780784479162.076
M3 - منشور من مؤتمر
T3 - World Environmental and Water Resources Congress 2015: Floods, Droughts, and Ecosystems - Proceedings of the 2015 World Environmental and Water Resources Congress
SP - 820
EP - 828
BT - World Environmental and Water Resources Congress 2015
A2 - Webster, Veronica L.
A2 - Karvazy, Karen
T2 - World Environmental and Water Resources Congress 2015: Floods, Droughts, and Ecosystems
Y2 - 17 May 2015 through 21 May 2015
ER -