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

Dede Sutarya,Benyamin Kusumoputro

doi:10.1155/2014/854569

Dede Sutarya, Benyamin Kusumoputro

Open Access

https://doi.org/10.1155/2014/854569

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Abstract

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 1.9e-03for heating step and6.3859e-08for soaking step. That result shows the model successfully predict the evolution of the temperature in the furnace.

Highlights

Temperature controllers must set the temperature very accurately in order to meet the needs of technological processes
Most dynamical systems can be better represented by nonlinear models, which are able to describe the global behavior of a system over the whole operating range
The behavior of most nonlinear dynamical systems has made the use of artificial neural networks (ANNs) for identification task

Summary

Introduction

Temperature controllers must set the temperature very accurately in order to meet the needs of technological processes. The behavior of most nonlinear dynamical systems has made the use of artificial neural networks (ANNs) for identification task. The application of ANNs to modeling and control nonlinear process has been intensively studied in recent years [1]. All numerous studies have shown that multilayer perceptrons (MLPs) neural network is very good choice for nonlinear system identification [2]. The neural network autoregressive model with exogenous input has been used for the identification of temperature control, such as modeling greenhouse temperature [4], modeling and identification of heat exchanger [5], and thermal dynamic identification of a pulsating heat pipe [6], and for the other system identification applications which are shown in the literature [7,8,9,10]

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