Application of improved local models of large scale databasebased online modeling to prediction of molten iron temperature of blast furnace

Norio Kaneko*, Shinroku Matsuzaki, Masahiro Ito, Haruhisa Oogai, Kenko Uchida

*この研究の対応する著者

    研究成果: Article査読

    11 被引用数 (Scopus)

    抄録

    The large scale database-based online modeling called LOM is the one of local modeling method. This method has been developed to apply the just-in-time modeling for the blast furnace by us. In this paper, we propose two new types of local models in LOM to improve the prediction performance. One is used weighted multiple regression model as a linear local model of LOM. The other is used on-line Bayesian learning model as a nonlinear local model of LOM. In order to compare the prediction performance of the two types of local models in LOM, we evaluate the prediction performance by using the real process data of the blast furnace.

    本文言語English
    ページ(範囲)939-945
    ページ数7
    ジャーナルISIJ International
    50
    7
    DOI
    出版ステータスPublished - 2010

    ASJC Scopus subject areas

    • 機械工学
    • 材料力学
    • 材料化学
    • 金属および合金

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