| Calibration can be defined as a process in which a number of WDS model parameters are adjusted until the model mimics behavior of the real WDS as closely as possible. The focus of this dissertation is the relative research about water quality model estimates, calibration and monitoring, besides booster chlorination setup.Firstly, chlorine bulk and wall decay coefficients have been estimated and calibrated. Based on EPANET2 and EPANET-MSX toolkits, the effect of individual parameters uncertainty, as well as the combined effect of the parameters uncertainty, on water quality in a distribution system was analyzed by Monte Carlo simulation (MCS). It concludes that chlorine bulk and wall decay coefficients have larger influence on model prediction comparing with other parameters. Then chlorine bulk coefficients in two-source water distribution system networks, two sources of which have different source bulk decay coefficients, have been estimated. The method can be used in multi-sources networks, which can provide a novel means to get the bulk chlorine coefficients instead of measuring in the lab. Besides, a general calibration model under multi-mode for identifying wall decay parameters is formulated, based on the simple first-order reaction of chlorine and the Lagrange time-based approach of dynamic water quality model. Multi-mode model was analyzed to collect more node residual chlorine for calibration. Max-min ant colony system algorithm was proposed to solve the calibration model that is coupled with hydraulic and water quality simulation models using EPANET2 Toolkits. It concludes that the methods can obtain the solution quickly and effectively.Secondly, based on D-optimization and FOSM theory, the uncertainties involved with estimated parameters and the model prediction uncertainties for critical demand conditions due to the parameter uncertainties are calculated. Then location optimization of sampling and measurement for water quality calibration has been proposed based on estimated parameters and the model prediction uncertainties, and has been resolved by multi-objective genetic algorithm NSGA-II. The number of measurement can be determined from the obtained pareto front solution. NSGA-II is very befitting for resolving such two-objective optimization problem.Thirdly, booster chlorination added to water distribution to maintain disinfectant residuals and avoid high dosages at water sources has been researched. The objective function which maximizes the certified water quantity to consumer, considering partial influence on nodes by booster chlorination, has been proposed. The mathematical model has been solved by particle swarm optimization algorithm; given a set number of boosters' stations. By considering the natural organic matter reacting with the chlorine and the disinfectant by-products concentration under allowed maximization as one of the restrictions, the chlorine injection rates of chlorine boosters have been optimized by minimizing the total chlorine mass to be injected to maintain required residuals, using genetic algorithms. Then though introducing performance evaluation system of networks service level, the level of service has been compared between no booster chlorination and re-chlorination. It concludes that booster chlorination not only increase the service level of networks, but can allow lower average chlorine and decrease the variability of chlorine residuals through a water distribution network, which lead to a lower dosage and may reduce the formation of disinfectant by-products.Finally, the methods of calibrating chlorine bulk and wall decay coefficients have been used in a realistic network. Then sampling design based on water quality model calibration has been optimized and booster chlorination stations have been displayed including the optimal location and chlorine injection rates in different time. It concludes that the model and method which have been proposed in this research can be applied to solve relative problems.
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