TY - JOUR
TI - Short-term and long-term thermal prediction of a walking beam furnace using neuro-fuzzy techniques
AU - Banadaki Hamed Dehghan
AU - Nozari Hasan Abbasi
AU - Shoorehdeli Mahdi Aliyari
JN - Thermal Science
PY - 2015
VL - 19
IS - 2
SP - 703
EP - 721
PT - Article
AB - 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.