Combining genetic algorithms and the finite element method to improve steel industrial processes

  1. Sanz-García, A. 1
  2. Pernía-Espinoza, A.V. 1
  3. Fernández-Martínez, R. 1
  4. Martínez-De-Pisón-Ascacíbar, F.J. 1
  1. 1 Universidad de La Rioja
    info

    Universidad de La Rioja

    Logroño, España

    ROR https://ror.org/0553yr311

Revista:
Journal of Applied Logic

ISSN: 1570-8683

Año de publicación: 2012

Volumen: 10

Número: 4

Páginas: 298-308

Tipo: Artículo

DOI: 10.1016/J.JAL.2012.07.006 SCOPUS: 2-s2.0-84869503640 WoS: WOS:000312521800005 GOOGLE SCHOLAR

Otras publicaciones en: Journal of Applied Logic

Repositorio institucional: lock_openAcceso abierto Editor

Resumen

Most of the times the optimal control of steel industrial processes is a very complicated task because of the elevated number of parameters to adjust. For that reason, in steel plants, engineers must estimate the best values of the operational parameters of processes, and sometimes, it is also necessary to obtain the appropriate model for steel material behaviour. This article deals with three successful experiences gained from genetic algorithms and the finite element method in order to solve engineering optimisation problems. On one hand, a fully automated method for determining the best material behaviour laws is described, and on the other hand we present a common methodology to find the most appropriate settings for two cases of improvement in steel industrial processes. The study of the three reported cases allowed us to show the reliability and effectiveness of combining both techniques. © 2012 Elsevier B.V.