Optimization model for location and operation schedule of chlorine booster stations in water distribution networks

In South Korea, various sensors and smart meters have recently been installed in water distribution networks as a consequence of the Fourth Industrial Revolution and the water supply system modernization project. This study identified consumers' actual water use patterns using hourly automatic meter reading (AMR) data. A genetic algorithm-based model was developed to optimize locations and operation schedules of chlorine booster stations, by minimizing residual chlorine concentration spatiotemporal variation within a water distribution network, and deriving a water quality management plan enabling economical disinfection. The model was applied to one water distribution district of the J water purification plant, and under the worst water quality conditions, three optimal chlorine booster stations locations could satisfy the target residual chlorine concentration of 0.1-0.5 mg/L, at a total cost of 110,991 KRW/d. Moreover, chlorination costs were compared before and after optimizing the chlorine booster stations' operation schedule. Chlorination costs were reduced from 2,554 to 1,576 KRW/d on Day 1, and from 2,232 to 1,319 KRW/d on Day 2, while maintaining 0.5 mg/L residual chlorine concentration. Residual chlorine concentration could be maintained in the range of 0.1-0.5 mg/L at every demand node.