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

dc.contributor.authorDos Santos Filho, Antonio Luiz
dc.contributor.authorRamirez-Fernandez, Francisco Javier [UNESP]
dc.contributor.institutionIF/SP - Cubatão Campus
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.date.accessioned2022-04-28T18:56:51Z
dc.date.available2022-04-28T18:56:51Z
dc.date.issued2010-12-01
dc.description.abstractIn 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. © 2010 IEEE.en
dc.description.affiliationIndustry Systems Department São Paulo Federal Institute of Education, Science and Technology IF/SP - Cubatão Campus, Cubatão, São Paulo, CEP 11533-160
dc.description.affiliationIntegrated Systems Laboratory São Paulo State University Polytechnic School (LME/USP), São Paulo, São Paulo, CEP 05508-010
dc.description.affiliationUnespIntegrated Systems Laboratory São Paulo State University Polytechnic School (LME/USP), São Paulo, São Paulo, CEP 05508-010
dc.identifierhttp://dx.doi.org/10.1109/IJCNN.2010.5596794
dc.identifier.citationProceedings of the International Joint Conference on Neural Networks.
dc.identifier.doi10.1109/IJCNN.2010.5596794
dc.identifier.scopus2-s2.0-79959395472
dc.identifier.urihttp://hdl.handle.net/11449/219671
dc.language.isoeng
dc.relation.ispartofProceedings of the International Joint Conference on Neural Networks
dc.sourceScopus
dc.titleNeural networks to improve mathematical model adaptation in the flat steel cold rolling processen
dc.typeTrabalho apresentado em evento

Arquivos

Coleções