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|Title:||General regression neural networks for modeling disinfection residual in water distribution systems|
|Citation:||Critical transitions in water and environmental resources management [electronic resource] : proceedings of the World Water and Environmental Resources Congress : June 27-July 1, 2004, Salt Lake City, UT / sponsored by Environmental and Water Resources Institute (EWRI) of the American Society of Civil Engineers ; Gerald Sehlke, Donald F. Hayes, and David K. Stevens (eds.): CDROM, pp.1-8|
|Publisher:||American Society of Civil Engineers|
|Conference Name:||World Water and Environmental Resources Congress (2004 : Salt Lake City, Utah)|
|Abstract:||Water treatment plant (WTP) operators set disinfectant levels such that a balance is maintained between achieving adequate disinfection and minimizing the undesirable effects of excessive disinfection residuals. Control systems for the optimal maintenance of disinfection residuals are based upon a model that attempts to describe the non-linear dynamics of the water distribution system (WDS). A system identification approach, based on artificial neural networks (ANNs), offers an expedient methodology for the development of control-oriented models. An advantage of ANNs is their ability to describe non-linear systems with greater accuracy than linear empirical models that are traditionally used for system identification. In this paper, the parallel development of a general regression neural network (GRNN) model and an autoregressive model with exogenous inputs (ARX) is described for the Myponga WDS in South Australia. The results indicate the superiority of the GRNN model and support further investigation of WDS control systems that incorporate ANN identification models.|
|Description:||© 2004 ASCE|
|Appears in Collections:||Civil and Environmental Engineering publications|
Environment Institute publications
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