Data-driven time discrete models for dynamic prediction of the hot metal silicon content in the blast furnace—A review

Henrik Saxén, Chuanhou Gao, Zhiwei Gao

    Research output: Contribution to journalReview Article or Literature Reviewpeer-review

    61 Citations (Scopus)

    Abstract

    A review of black-box models for short-term time-discrete prediction of the silicon content of hot metal produced in blast furnaces is presented. The review is primarily focused on work presented in journal papers, but still includes some early conference papers (published before 1990) which have a clear contribution to the field. Linear and nonlinear models are treated separately, and within each group a rough subdivision according to the model type is made. Within each subsection the models are treated (almost) chronologically, presenting the principle behind the modeling approach, the signals used and the main findings in terms of accuracy and usefulness. Finally, in the final section the approaches are discussed and some potential lines of future research are proposed. In an Appendix, a list of commonly used input and output variables in the models is presented.
    Original languageUndefined/Unknown
    Pages (from-to)2213–2225
    Number of pages13
    JournalIEEE Transactions on Industrial Informatics
    Volume9
    Issue number4
    DOIs
    Publication statusPublished - 2013
    MoE publication typeA2 Review article in a scientific journal

    Keywords

    • blast furnace
    • dynamics
    • Hot metal silicon
    • prediction
    • time-series models

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