No abstract

A B S T R A C TThe global water sector faces challenges to maintain safe, healthy, and adequate water supply to its consumers. Control of water quality and quantity by real-time monitoring (RTM) plays an important role in the management of water distribution systems (WDSs) and protection of consumers' health. RTM could be used for monitoring and analyses of water quality parameters to ensure its suitability for drinking. Additionally, RTM system warns operators to stop water supply to save water and minimize risks when needed. WDS of Antalya City is monitored and controlled by an advanced RTM and Supervisory Control and Data Acquisition (SCADA) system. Integrated RTM-SCADA system monitors and controls both hydraulic and water quality parameters to improve the WDS's operational efficiency. The system automatically controls pumps and valves and it has security alarms if any of the monitored water quality parameters fail to comply with the drinking water quality standards. This feature helps to protect WDS from the adverse impacts of an intentional or unintentional contamination event. Furthermore, RTM system is very helpful to detect water losses. Integrated RTM-SCADA system in WDSs provides many operational benefits (improved water quality, decreased operational costs, reduced customer complaints, reduction in water losses, and modeling capability) but it requires a good management system to assess huge amount of collected data.

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.

No abstract