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An Integrated Methodology for Rule Extraction from ELM-Based Vacuum Tank Degasser Multiclassifier for Decision-Making

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  • Senhui Wang

    (School of Metallurgy, Northeastern University, Shenyang 110819, China)

  • Haifeng Li

    (School of Metallurgy, Northeastern University, Shenyang 110819, China
    Key Laboratory of Ecological Utilization of Multi-metallic Mineral of Education Ministry, Northeastern University, Shenyang 110819, China)

  • Yongjie Zhang

    (School of Metallurgy, Northeastern University, Shenyang 110819, China)

  • Zongshu Zou

    (School of Metallurgy, Northeastern University, Shenyang 110819, China
    Key Laboratory of Ecological Utilization of Multi-metallic Mineral of Education Ministry, Northeastern University, Shenyang 110819, China)

Abstract

The present work proposes an integrated methodology for rule extraction in a vacuum tank degasser (VTD) for decision-making purposes. An extreme learning machine (ELM) algorithm is established for a three-class classification problem according to an end temperature of liquid steel that is higher than its operating restriction, within the operation restriction and lower than the operating restriction. Based on these black-box model results, an integrated three-step approach for rule extraction is constructed to interpret the understandability of the proposed ELM classifier. First, the irrelevant attributes are pruned without decreasing the classification accuracy. Second, fuzzy rules are generated in the form of discrete input attributes and the target classification. Last but not the least, the rules are refined by generating rules with continuous attributes. The novelty of the proposed rule extraction approach lies in the generation of rules using the discrete and continuous attributes at different stages. The proposed method is analyzed and validated on actual production data derived from a No.2 steelmaking workshop in Baosteel. The experimental results revealed that the extracted rules are effective for the VTD system in classifying the end temperature of liquid steel into high, normal, and low ranges. In addition, much fewer input attributes are needed to implement the rules for the manufacturing process of VTD. The extracted rules serve explicit instructions for decision-making for the VTD operators.

Suggested Citation

  • Senhui Wang & Haifeng Li & Yongjie Zhang & Zongshu Zou, 2019. "An Integrated Methodology for Rule Extraction from ELM-Based Vacuum Tank Degasser Multiclassifier for Decision-Making," Energies, MDPI, vol. 12(18), pages 1-15, September.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:18:p:3535-:d:267442
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    References listed on IDEAS

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    1. Gajic, Dragoljub & Savic-Gajic, Ivana & Savic, Ivan & Georgieva, Olga & Di Gennaro, Stefano, 2016. "Modelling of electrical energy consumption in an electric arc furnace using artificial neural networks," Energy, Elsevier, vol. 108(C), pages 132-139.
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    Cited by:

    1. Denis Sidorov & Fang Liu & Yonghui Sun, 2020. "Machine Learning for Energy Systems," Energies, MDPI, vol. 13(18), pages 1-6, September.

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