Calibration of water quality model for distribution networks using genetic algorithm, particle swarm optimization, and hybrid methods

Abstract

Chlorine reacts with both organic and inorganic matters in water. That is why water quality modeling has received great attention in recent years. The serious issue in municipal water quality modeling is gathering the essential input parameters of the model, particularly bulk decay (kb) and wall decay (kw) coefficients as well as their calibrations. Therefore, this study first thoroughly formulates the problem in the form of a heuristic optimization and then utilizes Genetic Algorithm, Particle Swarm Optimization, and Hybrid GA-PSO as the model optimizers in order to best calibrate kw for minimizing the difference of residual chlorine concentrations that exist between the simulated and observed values. These three algorithms are linked to EPANET, the hydraulic and water quality simulator. The method is then applied to a real-world water distribution network. Here, kw is considered as a decision variable. The objective function is to minimize both the Sum of Square Error and Root Mean Square Error between the observed and simulated chlorine concentrations. According to the simulation results obtained, the optimal value of wall decay coefficient is 1.233 m/day during the calibration process while the minimum and maximum differences between the measured and simulated chlorine rates were 0 and 0.18, respectively. • The method presented in this article can be useful for managers of water and wastewater companies, water resources facilities and operators and operation manager of water distribution system to manage chlorine dosing rate. • Due to adverse health effect of disinfection by product and poor microbial water quality as results of inefficient chlorination, control chlorine concentration in water distribution networks and its consequence on human health effect is necessary. • Hybrid PSO and GA methods are used to cope with their falling in local optimum and requiring highly computational effort. Method name: Genetic Algorithm (GA), Hybrid of Genetic Algorithm and Particle Swarm Optimization (HGAPSO), Particle Swarm Optimization (PSO), Keywords: EPANET, GA, HGAPSO, PSO, Wall decay coefficient, Water distribution network