Evolutionary neural network modeling of blast furnace burden distribution

Frank Pettersson*, Jan Hinnelä, Henrik Saxén

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

20 Citations (Scopus)


A neural network-based model of the burden layer thickness in the blast furnace is presented. The model is based on layer thicknesses estimates from a single radar measurement of the burden (stock) level in the furnace and describes the dependence between the layer thickness and key charging variables. An evolutionary algorithm is applied to train the network weights and connectivity by optimizing the model structure and parameters simultaneously, tackling part of the parameter estimation by linear least squares. This enhances convergence and results in parsimonious and transparent network models with actions that can be explained. Finally, the networks are used in a hybrid model for analyzing novel charging programs and for studying the limits of the charging process.

Original languageEnglish
Pages (from-to)385-399
Number of pages15
JournalMaterials and Manufacturing Processes
Issue number3
Publication statusPublished - May 2003
MoE publication typeA1 Journal article-refereed


  • Blast furnace
  • Burden distribution
  • Burden layer thickness
  • Evolutionary training
  • Genetic algorithm
  • Network architecture selection
  • Neural network


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