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Science and Technology of Nuclear Installations
Volume 2014 (2014), Article ID 854569, 8 pages
Research Article

Identification of Industrial Furnace Temperature for Sintering Process in Nuclear Fuel Fabrication Using NARX Neural Networks

1Department of Electrical Engineering, University of Indonesia, Kampus Baru UI, Depok 16424, Indonesia
2Center for Nuclear Fuel Technology, National Nuclear Energy Agency, Kawasan PUSPIPTEK, Tangerang 15314, Indonesia

Received 30 August 2013; Accepted 5 January 2014; Published 3 April 2014

Academic Editor: Alejandro Clausse

Copyright © 2014 Dede Sutarya and Benyamin Kusumoputro. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Nonlinear system identification is becoming an important tool which can be used to improve control performance and achieve robust fault-tolerant behavior. Among the different nonlinear identification techniques, methods based on neural network model are gradually becoming established not only in the academia, but also in industrial application. An identification scheme of nonlinear systems for sintering furnace temperature in nuclear fuel fabrication using neural network autoregressive with exogenous inputs (NNARX) model investigated in this paper. The main contribution of this paper is to identify the appropriate model and structure to be applied in control temperature in the sintering process in nuclear fuel fabrication, that is, a nonlinear dynamical system. Satisfactory agreement between identified and experimental data is found with normalized sum square error 1 for heating step and for soaking step. That result shows the model successfully predict the evolution of the temperature in the furnace.