Title: Identification of a research nuclear reactor using computational intelligence techniques

Authors: Erick Rojas-Ramirez, Jorge Samuel Benitez-Read, J. Armando Segovia-De-los-Rios, Luis C. Longoria-Gandara

Addresses: Toluca Institute of Technology (ITT), Av. Tecnologico s/n, Ex-Rancho La Virgen, Metepec, Estado de Mexico 52000, Mexico. ' National Nuclear Research Institute (ININ), Carretera Mexico-Toluca s/n, La Marquesa, Ocoyoacac, Estado de Mexico 52750, Mexico. ' National Nuclear Research Institute (ININ), Carretera Mexico-Toluca s/n, La Marquesa, Ocoyoacac, Estado de Mexico 52750, Mexico. ' National Nuclear Research Institute (ININ), Carretera Mexico-Toluca s/n, La Marquesa, Ocoyoacac, Estado de Mexico 52750, Mexico

Abstract: In the field of nuclear engineering, the development of methods to identify the non-linear dynamic behaviour of nuclear reactors is an important area of research. In this paper, the design and performance of a multi-input multi-output Takagi–Sugeno (TS) neuro-fuzzy network are presented. The aim of this network is to identify the temporal behaviour of the power, the fuel temperature and the core reactivity of a TRIGA Mark III type nuclear research reactor. The tuning of the parameters corresponding to the antecedent membership functions is carried out by means of descent gradient algorithms with stable training, whereas the consequent parameters are identified using a Kalman estimator. Genetic algorithms are used to define the best input selection in the model. The results of the simulations show that the identification system converges rapidly and with high accuracy to both the training data and the test data. Thus, in the absence of real reactor data, the identified system can be used for tuning purposes of reactor power control schemes.

Keywords: system identification; Takagi–Sugeno; TS neuro-fuzzy networks; nuclear reactors; reactor power; nuclear energy; nuclear power; computational intelligence; research reactors; neural networks; fuzzy logic; nonlinear dynamic behaviour; fuel temperature; core reactivity; genetic algorithms; simulation; tuning; reactor power control.

DOI: 10.1504/IJNKM.2011.040937

International Journal of Nuclear Knowledge Management, 2011 Vol.5 No.2, pp.112 - 125

Published online: 18 Feb 2015 *

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