Water quality monitoring and search for environment friendly energy sources is becoming two of the most popular engineering research topics as we better understand the limits of our planet. In this thesis, first an optimal design methodology for water quality monitoring networks in river systems is developed. Next, a data interpretation approach is proposed to identify pollution source locations utilizing the water quality measurements supplied by the monitoring network. As the third topic, the thesis introduces an optimal design technique for energy recovery systems in water distribution networks.
In the first part of this thesis, an optimization algorithm is developed for the water quality monitoring system. In this process, the best monitoring locations are determined by utilizing the outcomes of a simulation model. The results of the simulation model is an essential component of this approach since they incorporate the unsteady and stochastic nature of hydrodynamics and the contaminant fate and transport processes in rivers into the optimization model. In this approach, the ideal monitoring locations are determined through a multi-objective optimization technique. One of the objectives of the monitoring system is specified as the early detection of the contaminants and the other as the reliability of the monitoring network. The methodology developed was first applied to a simple hypothetical river system to demonstrate the importance of the unsteady hydrological properties of the watershed on the optimal locations of the monitoring stations. Then, it is tested on a realistic river system. The results show that the design technique developed can be effectively used for the optimal design of monitoring networks in river systems.
In the second part of the study, a methodology for rapid identification of contaminant source locations is introduced. Since this is an ill posed problem which has non-unique solutions, a classification routine which correlates candidate spill locations with the measurements at the water quality monitoring stations is developed. For this purpose, the breakthrough curve of a contaminant measured at monitoring site is parameterized using its statistical moments. Then, a large number of spill scenarios are simulated for the training of the monitoring system. After the training process, the method is ready for sequential elimination of the candidate locations which leads to the identification of spill location for a breakthrough curve observed at the monitoring station. The model developed is applied to real river system and the results show that this technique can be a reliable starting point for the contaminant source investigation projects
The third part of the thesis is devoted to renewable energy production from water distribution systems. The main idea behind this study is to harvest as much available excess energy as possible by utilizing micro turbines. The energy production at these turbines is constrained by the minimum pressure limit set by the management. Moreover, the unsteady nature of the flow in the network results in variations in the available excess energy. These aspects of the water distribution systems necessitate operation schedules for the micro turbines. In this study, a simulation-optimization method is developed which maximizes the energy recovered at the micro turbine(s). This simulationoptimization model is based on Genetic Algorithms (GA). A smart seeding of the GA is introduced to lower the computational burden. The algorithm tests several energy recovery system configurations which has different turbine locations and turbine types. Then the best configuration which has the highest energy production is selected. The methodology is first applied to a real pump driven network. Then, this network is converted into a hypothetical gravity driven system and the optimization model is tested on this new system. The results show that the energy recovery systems in water distribution networks can provide significant economic and environmental benefits and the methodology introduced is not only an optimal design tool but also an effective means of assessing the renewable energy potential in water distribution systems.