Neutronics power estimation of prototype fast breeder reactor using artificial neural network model

This paper discusses about the implementation of an Artificial Neural Network (ANN) based model for Neutronics power estimation of Prototype Fast Breeder Reactor (PFBR). ANN has been designed to predict the Neutronics power for various positions of control and safety rods. Simulation studies were carried out using the thermo-hydraulics code and the required data has been generated for training the ANN model. Here, three layer neural network architecture has been developed and trained with different learning algorithms to estimate Neutronics power and model the plant dynamics. The best performing algorithm in terms of faster convergence has been identified among the variants of back propagation network. ANN model has more advantages compared to conventional model namely increased speed, reduced complexity in addition to producing accurate results. Neural network being a data driven model, it is possible to derive the operating characteristics of nuclear reactor without exploring the intricacies of the complex subsystems.

[1]  K. Nabeshima,et al.  Nuclear reactor monitoring with the combination of neural network and expert system , 2002, Math. Comput. Simul..

[2]  Walter Bogaerts,et al.  SCC susceptibility analysis of stainless steels in nuclear reactor water: a neural network and expert system approach , 1995, Fuzzy Sets Syst..

[3]  Ramgopal Uppaluri,et al.  Treatment of oily wastewater using low cost ceramic membrane: Comparative assessment of pore blocking and artificial neural network models , 2010 .

[4]  Baldev Raj,et al.  Artificial neural network modelling for evaluating austenitic stainless steel and Zircaloy-2 welds , 2005 .

[5]  B. C. Hwang.  Intelligent control for a nuclear power plant using artificial neural networks , 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).

[6]  Z. Zainuddin,et al.  Improving the Convergence of the Backpropagation Algorithm Using Local Adaptive Techniques , 2007, International Conference on Computational Intelligence.

[7]  Hakim Mazrou,et al.  Application of artificial neural network for safety core parameters prediction in LWRRS , 2004 .

[8]  S. Venugopal,et al.  Artificial neural network modeling to evaluate and predict the deformation behavior of stainless steel type AISI 304L during hot torsion , 2009, Appl. Soft Comput..

[9]  Laurene V. Fausett,et al.  Fundamentals Of Neural Networks , 1994 .

[10]  Juan José Ortiz,et al.  Local power peaking factor estimation in nuclear fuel by artificial neural networks , 2009 .

[11]  S. Rajeswari,et al.  Artificial Neural Network Model for Intermediate Heat Exchanger of Nuclear Reactor , 2010 .