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Neural networks to improve mathematical model adaptation in the flat steel cold rolling process

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2 Author(s)
Antonio Luiz dos Santos Filho ; Industry Systems Department of the São Paulo Federal Institute of Education, Science and Technology (IF/SP - Cubatão Campus), CEP 11533-160, Brazil ; Francisco Javier Ramirez-Fernandez

In the flat steel cold rolling process, real-time controllers get their reference values (setpoints) using a mathematical model. Such a model is carried out at the process optimization level of the plant automation architecture. Since not all variables needed by the model can be effectively measured, and since a very accurate modeling would be unsuitable for real-time application or unachievable at all, the mathematical model must have adaptive capabilities, that is, its key parameters must be continuously adjusted based on real process values. This work proposes the application of Artificial Neural Networks to improve the adaptation of two hardly modeled process variables: the material yield stress and the friction coefficient between the work rolls and the strip. The text describes the theoretical foundations, the development methodology and the preliminary results achieved by implementing the proposed system in a real tandem cold mill.

Published in:

The 2010 International Joint Conference on Neural Networks (IJCNN)

Date of Conference:

18-23 July 2010