Methodology based on genetic optimisation to develop overall parsimony models for predicting temperature settings on annealing furnace

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

    Universidad de La Rioja

    Logroño, España

    ROR https://ror.org/0553yr311

Revista:
Ironmaking and Steelmaking

ISSN: 0301-9233

Año de publicación: 2014

Volumen: 41

Número: 2

Páginas: 87-98

Tipo: Artículo

DOI: 10.1179/1743281212Y.0000000094 SCOPUS: 2-s2.0-84894634231 WoS: WOS:000337063200002 GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Ironmaking and Steelmaking

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Resumen

Developing better prediction models is crucial for the steelmaking industry to improve the continuous hot dip galvanising line (HDGL). This paper presents a genetic based methodology whereby a wrapper based scheme is optimised to generate overall parsimony models for predicting temperature set points in a continuous annealing furnace on an HDGL. This optimisation includes a dynamic penalty function to control model complexity and an early stopping criterion during the optimisation phase. The resulting models (multilayer perceptron neural networks) were trained using a database obtained from an HDGL operating in the north of Spain. The number of neurons in the unique hidden layer, the inputs selected and the training parameters were adjusted to achieve the lowest validation and mean testing errors. Finally, a comparative evaluation is reported to highlight our proposal's range of applicability, developing models with lower prediction errors, higher generalisation capacity and less complexity than a standard method. © 2014 Institute of Materials, Minerals and Mining.