A model to minimize the hot rolling time of a steel slab considering the steel's chemical composition

Carlos A. Carreón Hernández, Héctor J. Fraire-Huacuja, Karla Espriella Fernandez, Guadalupe Castilla-Valdez, Juana E. Mancilla Tolama

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper presents an optimization approach to deal with the problem of minimizing the hot rolling time of a steel slab. Unlike traditional approaches, this work also considers the chemical composition of the steel slab as a parameter, allowing the automatic setup for different steels of the hot rolling mill. To validate the approach discussed here, a six-stand rolling mill is modeled as an optimization constrained problem solving for six different steel types taken from real processes. The mathematical formulation and considerations for each presented case are fully described. The experimental evidence shows that the solution of the hot rolling scheduling problem requires a more efficient method than just a constrained nonlinear optimizer and that the proposed model properly simulates the hot rolling process.

Original languageEnglish
Title of host publicationInternational Symposium on Distributed Computing and Artificial Intelligence 2008 (DCAI 2008)
EditorsJuan Corchado, Sara Rodriguez, James Llinas, Jose Molina
Pages471-480
Number of pages10
DOIs
StatePublished - 2009

Publication series

NameAdvances in Soft Computing
Volume50
ISSN (Print)1615-3871
ISSN (Electronic)1860-0794

Keywords

  • Evolutionary computation
  • Genetic algorithms
  • Rolling pass schedule

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