NUKLEONIKA 2009, 54(4):239-245

 


GENETIC ALGORITHMS AND NEURAL NETWORKS FOR SOLVING WATER QUALITY MODEL OF THE EGYPTIAN RESEARCH REACTOR



Mohamed El-Sayed Wahed1, Wesam Z. Ibrahim2, Ahmed M. Effat2

1 Faculty of Science, Mathematics Department, Zagazic University, Egypt
2 Atomic Energy Authority, 54-amr bin yasser-11351-Helioples, Cairo, Egypt

The second Egyptian research reactor ETRR-2 became critical on 27th November, 1997. The National Center of Nuclear Safety and Radiation Control (NCNSRC) has the responsibility for the evaluation and assessment of safety of this reactor. Modern managements of water distribution system (WDS) need water quality models that are able to accurately predict the dynamics of water quality variations within the distribution system environment. Before water quality models can be applied to solve system problems, they should be calibrated. The purpose of this paper is to present an approach which combines both macro and detailed models to optimize the water quality parameters. For an efficient search through the solution space, we use a multi-objective genetic algorithm which allows us to identify a set of Pareto optimal solutions providing the decision maker with a complete spectrum of optimal solutions with respect to the various targets. This new combinative algorithm uses the radial basis function (RBF) metamodeling as a surrogate to be optimized for the purpose of decreasing the times of time-consuming water quality simulation and can realize rapidly the calibration of pipe wall reaction coefficients of chlorine model of large-scaled WDS.

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