Optimal reconstruction of historical water supply to a distribution system: B. Applications.
In a recently completed case-control epidemiological study, the New Jersey Department of Health and Senior Services (NJDHSS) with support from the Agency for Toxic Substances and Disease Registry (ATSDR) documented an association between prenatal exposure to a specific contaminated community water source and leukaemia in female children. An important and necessary step in the epidemiological study was the reconstruction of the historical water supply strategy of the water distribution system serving the Dover Township area, New Jersey. The sensitivity of solutions to: (1) pressure and pattern factor constraints, (2) allowable operational extremes of water levels in the storage tanks, and (3) the non-uniqueness of the water supply solution are analysed in detail. The computational results show that the proposed approach yields satisfactory results for the complete set of monthly simulations and sensitivity analyses, providing a consistent approach for identifying the historical water supply strategy of the water distribution system. Sensitivity analyses indicated that the alternative strategy obtained from the revised objective function and the variation of constraints did not yield significantly different water supply characteristics. The overall analysis demonstrates that the progressive optimality genetic algorithm (POGA) developed to solve the optimization problem is an effective and efficient algorithm for the reconstruction of water supply strategies in water distribution systems.
Aral
M M
MM
Multimedia Environmental Simulations Laboratory, School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA. maral@ce.gatech.edu
Guan
J
J
Maslia
M L
ML
Sautner
J B
JB
Gillig
R E
RE
Reyes
J J
JJ
Williams
R C
RC
D000465:Algorithms; D002648:Child; D005260:Female; D006801:Humans; D007938:Leukemia / Q000139:chemically induced / Q000453:epidemiology; D018811:Maternal Exposure / Q000009:adverse effects; D008957:Models, Genetic; D009515:New Jersey / Q000453:epidemiology; D018570:Risk Assessment / Q000379:methods*; D012498:Sanitary Engineering; D012680:Sensitivity and Specificity; D013997:Time Factors; D014874:Water Pollutants, Chemical / Q000032:analysis / Q000633:toxicity; D014881:Water Supply / Q000032:analysis / Q000706:statistics & numerical data*
D014874:Water Pollutants, Chemical