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Article Citation - WoS: 22Citation - Scopus: 23On the Usage of Artificial Neural Networks in Chlorine Control Applications for Water Distribution Networks With High Quality Water(Iwa Publishing, 2011) Soyupak, S.; Kilic, H.; Karadirek, I. E.; Muhammetoglu, H.Artificial neural network (ANN) methodology has found some recent applications as efficient control tools for satisfying free residual chlorine (FRC) levels at critical locations of water distribution systems. This particular research was started to critically investigate the potential and applicability of the ANN approach as a tool for controlling FRC levels for complex water distribution systems supplied by high quality waters with low chlorine demands. Konyaalti Water Distribution System, operated by Antalya Water and Wastewater Administration, Turkey, has been selected as a pilot. The selected system is complex in structure and supplied with raw water which has high quality and low decay rate of chlorine. The study has shown that ANN models with high predictive power and precision can be developed for such water distribution systems, and that these models can be utilized for forecasting purposes. The data for model building should be collected properly if the developed ANN models are to be utilized as control instruments for FRC levels within water distribution systems.Article Citation - WoS: 2Constraint Programming for Optimal Design of Architectures for Water Distribution Tanks and Reservoirs: a Case Study(Univ Osijek, Tech Fac, 2014) Soto, Ricardo; Crawford, Broderick; Misra, Sanjay; Monfroy, Eric; Palma, Wenceslao; Castro, Carlos; Paredes, FernandoA water distribution system is an essential component of any urban infrastructure system. Its design is commonly a hard task mainly due to the presence of several complex interrelated parameters. Among others, some parameters to study are the water demand, pressure requirements, topography, location of resources, system reliability, and energy uses. In this paper, we focus on a real case of water distribution system in order to minimize installation costs by satisfying the given system requirements. We solve the problem by using state-of-the-art Constraint Programming techniques combined with Interval Analysis for rigorously handling continuous decision variables. Experimental results demonstrate the feasibility of the proposed approach, where the global optimum is reached in all instances and in reasonable runtime.
