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電弧爐煉鋼爐渣成分實時預報模型

Real-time prediction model of slag composition in electric arc furnace steelmaking

  • 摘要: 為了實現冶煉過程中對爐渣成分的實時預測,給電弧爐煉鋼過程中加料等工藝操作提供幫助,對影響爐內爐渣成分的因素(爐內反應、加料與流渣)進行了研究,構建了電弧爐煉鋼爐渣成分實時預報模型。結果顯示,該模型能夠實時預測爐內爐渣質量和成分變化,預報爐內鐵元素氧化狀況,可為冶煉過程中添加輔料與流渣等工藝操作提供指導作用。通過與現場爐渣取樣檢測結果進行對比,得到爐渣中CaO、SiO2和FeO實測成分與模型預測成分的平均相對誤差分別為12.66%、11.17%和19.16%。

     

    Abstract: To realize the real-time prediction of slag composition in the smelting process and provide the assistance to the operations in electric arc furnace (EAF) steelmaking process such as charging, the influence factors on the slag composition in the furnace (furnace reaction, charging, and slag overflowing) were studied, and the real-time prediction model of slag composition in EAF steelmaking process was established. In the results, the model could predict the slag quality, the slag composition, and the oxidation status of Fe element in the furnace in real time, providing the guidance for the auxiliary material charging and the slag flowing in the smelting process. Compared with the slag sampling results, the average relative errors of CaO, SiO2, and FeO content in the slag between the actual measurement and the model predicted values were 12.66%, 11.17%, and 19.16%, respectively.

     

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