Abstract

Injection of chlorine as a disinfectant and the correct prediction of the residual amount in water distribution networks are key points and important principles in the quality management of these networks. It is necessary to determine and measure some parameters to develop an effective model in this field. In this study, for the first time, the Simple Hybrid of Genetic Algorithm and Particle Swarm Optimization (SHGAPSO) method along with GA and PSO algorithms are used for qualitative calibration of a real-life water distribution network in Iran to minimize the difference between observed chlorine concentrations at measurement points and the concentrations simulated by the EPANET2.0 hydraulic-qualitative simulation model. The objective functions in this study are Mean Absolute Error (MAE) and Mean Square Error (MSE). The decision variables include bulk (k(b)) and wall (k(w)) chlorine decay coefficients are estimated by SHGAPSO, GA, and PSO methods during 4 scenarios. In this study, the amount of residual chlorine in the network was measured by a digital chlorine meter in different days of the year 2018. The results show that SHGAPSO method enhanced the performance of GA and PSO algorithms in the scenarios studied. In scenario 3, this difference was noticeable. Also, scenario 4 with the lowest values of objective functions is selected to estimate the coefficients k(w) and k(b) as the best scenario, and finally the qualitative calibration of the network.