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Neural and Neural Gray-Box Modeling for Entry Temperature Prediction in a Hot Strip Mill

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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 available only after the bar has entered the mill, and therefore they have to be estimated. Estimation of process variables, particularly that of temperature, is of crucial importance for the bar front section to fulfill quality requirements, and the same must be performed in the shortest possible time to preserve heat. Currently, temperature estimation is performed by physical modeling; however, it 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 artificial neural networks and fuzzy logic have been proposed. In this article, neural network-based systems, including neural-based Gray-Box models, are applied to estimate scale breaker entry temperature, given its importance, and their performance is compared to that of the physical model used in plant. Several neural systems and several neural-based Gray-Box models are designed and tested with real data. Taking advantage of the flexibility of neural networks for input incorporation, several factors which are believed to have influence on the process are also tested. The systems proposed in this study were proven to have better performance indexes and hence better prediction capabilities than the physical models currently used in plant.

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Acknowledgments

This study was partially supported by PROMEP and CONACYT. The authors would like to thank Dr. Jorge Ramirez from TERNIUM-Hylsa for providing the necessary data and the support.

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Authors and Affiliations

  1. Universidad Autónoma de Nuevo León, Av. Universidad S/N, Cd. Universitaria, Apdo. Postal 115, C.P. 66450, San Nicolás de los Garza, NL, Mexico

    José Angel Barrios, Miguel Torres-Alvarado & Alberto Cavazos

  2. Ternium Mexico, Av. Los Ángeles 325, San Nicolás de los Garza, NL, Mexico

    Luis Leduc

Authors
  1. José Angel Barrios
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  2. Miguel Torres-Alvarado
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  3. Alberto Cavazos
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  4. Luis Leduc
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Correspondence to Alberto Cavazos.

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Barrios, J.A., Torres-Alvarado, M., Cavazos, A. et al. Neural and Neural Gray-Box Modeling for Entry Temperature Prediction in a Hot Strip Mill. J. of Materi Eng and Perform 20, 1128–1139 (2011). https://doi.org/10.1007/s11665-010-9759-1

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  • DOI: https://doi.org/10.1007/s11665-010-9759-1

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