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Short-term and long-term thermal prediction of a walking beam furnace using neuro-fuzzy techniques

Banadaki Hamed Dehghan (Department of Electrical Engineering, Islamic Azad University, Ashkezar Branch, Yazd, Iran)
Nozari Hasan Abbasi (Department of Electrical Engineering, Islamic Azad University, Joybar Branch, Joybar, Iran)
Shoorehdeli Mahdi Aliyari (Department of Mechatronics, Faculty of Electrical Engineering, K. N. Toosi University, Tehran, Iran)

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: Non-linear prediction, Walking beam furnace, locally linear neuro-fuzzy, locally linear model tree, least-squares error