A BP neural network predictor model for desulfurizing molten iron

  • Authors:

  • Zhijun Rong;Binbin Dan;Jiangang Yi

  • Affiliations:

  • Department of Industrial Engineering, Wuhan University of Science and Technology, Wuhan, China;Department of Industrial Engineering, Wuhan University of Science and Technology, Wuhan, China;Department of Industrial Engineering, Wuhan University of Science and Technology, Wuhan, China

  • Venue:
  • ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
  • Year:
  • 2005

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Abstract

Desulfurization of molten iron is one of the stages of steel production process. A back-propagation (BP) artificial neural network (ANN) model is developed to predict the operation parameters for desulfurization process in this paper. The primary objective of the BP neural network predictor model is to assign the operation parameters on the basis of intelligent algorithm instead of the experience of operators. This paper presents a mathematical model and development methodology for predicting the three main operation parameters and optimizing the consumption of desulfurizer. Furthermore, a software package is developed based on this BP ANN predictor model. Finally, the feasibility of using neural networks to model the complex relationship between the parameters is been investigated.