THERMAL SCIENCE

International Scientific Journal

SHORT-TERM AND LONG-TERM THERMAL PREDICTION OF A WALKING BEAM FURNACE USING NEURO-FUZZY TECHNIQUES

ABSTRACT
The walking beam furnace (WBF) is one of the most prominent process plants often met in an alloy steel production factory and characterized by high non-linearity, strong coupling, time delay, large time-constant and time variation in its parameter set and structure. From another viewpoint, the WBF is a distributed-parameter process in which the distribution of temperature is not uniform. Hence, this process plant has complicated non-linear dynamic equations that have not worked out yet. In this paper, we propose one-step non-linear predictive model for a real WBF using non-linear black-box sub-system identification based on locally linear neuro-fuzzy (LLNF) model. Furthermore, a multi-step predictive model with a precise long prediction horizon (i.e., ninety seconds ahead), developed with application of the sequential one-step predictive models, is also presented for the first time. The locally linear model tree (LOLIMOT) which is a progressive tree-based algorithm trains these models. Comparing the performance of the one-step LLNF predictive models with their associated models obtained through least squares error (LSE) solution proves that all operating zones of the WBF are of non-linear sub-systems. The recorded data from Iran Alloy Steel factory is utilized for identification and evaluation of the proposed neuro-fuzzy predictive models of the WBF process.
KEYWORDS
PAPER SUBMITTED: 2012-04-10
PAPER REVISED: 2012-05-10
PAPER ACCEPTED: 2012-10-14
DOI REFERENCE: https://doi.org/10.2298/TSCI120410210B
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2015, VOLUME 19, ISSUE Issue 2, PAGES [703 - 721]
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© 2024 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence