General Regression Neural Networks for Modeling Disinfection Residual in Water Distribution Systems

Water treatment plant (WTP) operators set disinfectant levels such that a balance is m aintained between achieving adequate disinfection and minimising the undesirable effects of excessive disinfection residuals. Control sy stems for the optimal mai ntenance of disi nfection 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 t he devel opment of contr ol-oriented models. An advantage of ANNs is their ability to de scribe non-linear systems with great er accuracy than linear empirical models that are tr aditionally 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 Sout h Australia. The results indicate the superiority of the GRNN model and support further investigation of WDS control systems that incorporate ANN identification models.