Drinking Water Engineering and Science Discussions www.drink-water-eng-sci-discuss.net 1996-9473 1996-9481 2 2 2009 10.5194/dwesd-2-279-2009 http://www.drink-water-eng-sci-discuss.net/2/279/2009/ http://www.drink-water-eng-sci-discuss.net/2/279/2009/dwesd-2-279-2009.html http://www.drink-water-eng-sci-discuss.net/2/279/2009/dwesd-2-279-2009.pdf 279 294 2009-12-23 Online modelling of water distribution systems: a UK case study J. Machell S. R. Mounce s.r.mounce@sheffield.ac.uk J. B. Boxall Pennine Water Group, Department of Civil and Structural Engineering, University of Sheffield, Sheffield, UK Hydraulic simulation models of water distribution networks are routinely used for operational investigations and network design purposes. However, their full potential is often never realised because, in the majority of cases, they have been calibrated with data collected manually from the field during a single historic time period and, as such, reflect the network operational conditions that were prevalent at that time, and they are then applied as part of a reactive, desktop investigation. In order to use a hydraulic model to assist proactive distribution network management its element asset information must be up to date and it should be able to access current network information to drive simulations. Historically this advance has been restricted by the high cost of collecting and transferring the necessary field measurements. However, recent innovation and cost reductions associated with data transfer is resulting in collection of data from increasing numbers of sensors in water supply systems, and automatic transfer of the data to point of use. This means engineers potentially have access to a constant stream of current network data that enables a new era of "online" modelling that can be used to continually assess standards of service compliance for pressure and reduce the impact of network events, such as mains bursts, on customers. A case study is presented here that shows how an online modelling system can give timely warning of changes from normal network operation, providing capacity to minimise customer impact. Bhattacharya, B., Lobbrecht, A. H., and Solomatine, D. P.: Neural networks and reinforcement learning in control of water systems, J. Water Res. Pl.-ASCE, 129(6), 458–465, 2003. Dandy, G. A., Simpson, A. R., and Murphy, L. J.: An improved genetic algorithm for pipe network optimisation, Water Res., 32(2), 449–458, 1996. Kapelan, Z., Savic, D. A., and Walters, G. A.: Inverse transient analysis in pipe networks for leakage detection and roughness calibration, Proceedings of Water Network Modelling for Optimal Design and Management, Exeter, UK, 143–159, 2000. Koppel, T. and Vassiljev, A.: Calibration of the Model of an Operational Water Distribution System, Proceedings of the IWA World Water Congress (WWC), Vienna, 2008. Mounce, S. R. and Boxall, J. B.: Implementation of an on-line Artificial Intelligence District Meter Area flow meter data analysis system for abnormality detection: a case study, Water Sci. Technol., in press, 2010. Orr, C., Bouulos, P., Stern, C., and Liu, P.: Developing real-time models of water distribution systems, Modeling and optimisation applications, Water engineering and management series, 3–4, edited by: Savic, D. and Walters, G., Vol 1, Research Studies Pr., 1999. Rao, Z. and Salomons, E.: Development of a real-time, near-optimal control process for water distribution networks, J. HydroInform., IWA Publishing, 9(1), 25–37, 2007. Skipworth, P. J., Saul, A., and Machell, J.: Practical applications of real time hydraulic modelling of water distribution networks, Int. J. COMADEM, 2, 1, 15–21, 1999.