Fuzzy and Fuzzy Grey-Box Modelling for Entry Temperature Prediction in a Hot Strip Mill

Abstract

In hot strip mills, initial controller set points have to be calculated before the steel bar enters the mill. Calculations rely on the good knowledge of rolling variables. Measurements are only available once the bar has entered the mill; therefore, they have to be estimated. Estimation of process variables, particularly temperature, is of crucial importance for the bar front section to fulfil quality requirements and must be performed in the shortest possible time to keep heat. Variable estimation is highly affected by measurement uncertainties, variations in the incoming bar conditions, and final product changes. In order to overcome these problems, artificial intelligence techniques, such as fuzzy logic and artificial neural networks, have been proposed. In this article, fuzzy logic-based systems, including fuzzy-based Grey-Box models, are applied to estimate scale breaker entry temperature, given its importance, and its performance is compared against that of the physical model used in plant. Six fuzzy systems and six fuzzy-based Grey-Box models of the type Mamdani, Sugeno, and adaptive neuro-fuzzy inference systems are designed for two different sets of rules and tested with experimental data.

Keywords:

• Fuzzy logic
• Grey-Box modelling
• Hot rolling
• Hot strip mills
• Hybrid modelling
• Semiphysical modelling
• Temperature estimation

ACKNOWLEDGMENTS

This work was partially supported by PROMEP and CONACYT. The authors would like to thank TERNIUM-Hylsa for the data and the support provided. The authors are also grateful to Prof. Rafael Colás for his valuable advice.