Application of Genetic Algorithms for Booster Chlorination inWater Supply Networks

摘要

The chlorination of drinking water distribution networks is usually carried out at the supply source. However, chlorine may disappear in some portions or at distant points within a network. In this case, one or more booster chlorination stations must be built in the network in order to observe detectable chlorine residual at all levels of branching. Network hydraulic values, tank water levels and chlorine concentrations may vary over the course of one day because of changes in consumer demand. For this reason, the optimal location of a booster chlorination station, injection rates and scheduling must be considered together. In this research, the locations, injection rates and scheduling of chlorine booster stations were studied using genetic algorithms. The results indicate that booster disinfection can significantly increase the desired residual concentrations above the minimum limit while helping to reduce variability in nodal concentrations. The objectives were satisfied with a small increase in chlorine consumption compared to conventional disinfection only at the source. In order to find a hydraulic solution and chlorine concentration distribution in a network, EPANET software was used. In the solution phase, genetic algorithms and EPANET software were run interactively. The algorithm developed was used on an existing network given in the literature and solutions were compared with the current status of the network.