Optimising annealing process on hot dip galvanising line based on robust predictive models adjusted with genetic algorithms

  1. Martínez-De-Pisón, F.J. 1
  2. Celorrio, L. 1
  3. Pérez-De-La-Parte, M. 1
  4. Castejón, M. 2
  1. 1 Universidad de La Rioja
    info

    Universidad de La Rioja

    Logroño, España

    ROR https://ror.org/0553yr311

  2. 2 Universidad de León
    info

    Universidad de León

    León, España

    ROR https://ror.org/02tzt0b78

Revista:
Ironmaking and Steelmaking

ISSN: 0301-9233

Año de publicación: 2011

Volumen: 38

Número: 3

Páginas: 218-228

Tipo: Artículo

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DOI: 10.1179/1743281210Y.0000000001 SCOPUS: 2-s2.0-79952608160 WoS: WOS:000288320900010 GOOGLE SCHOLAR

Otras publicaciones en: Ironmaking and Steelmaking

Resumen

This paper describes the process for optimising the annealing cycle on a hot dip galvanising line based on a combination of the techniques of artificial intelligence and genetic algorithms for creating two types of regression models. The first model can predict the furnace operating temperature for each coil and is trained to learn from the experience of the plant operators when the process has been correctly adjusted in 'manual mode' and from the control system when it has been properly operated in 'automatic mode'. Once the scheduling has been optimised, and using the two predictive models, a computer simulation is made of the galvanising process in order to optimise the target settings when there are sudden transitions in the steel strip. This substantially improves the thermal treatment, as these sudden transitions may occur when there are two welded coils differing in size and type of steel, whereby a drastic change in strip specifications leads to irregular thermal treatments that may affect the steel's coating or properties in that part of the coil. © 2011 Institute of Materials, Minerals and Mining.