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基于收得率動態庫的合金加料優化模型

Alloy charging optimization model based on the yield dynamic libraries

  • 摘要: 為了獲得準確實時的合金元素收得率,采用歷史加料數據自學習的方法,利用計算機技術建立了合金元素收得率動態庫,并運用兩階段單純形算法為天津鋼管公司第一煉鋼廠建立了合金加料優化模型.通過模型在線運行,得出了不同鋼種的合金收得率,從而提高了不同鋼種爐次合金加料的準確度.通過優化合金配料,不同鋼種的合金加料平均成本最多降低54.96元·t-1,最小降低8.57元·t-1.噸鋼合金成本降低6.76%~11.40%,平均降低了9.74%.

     

    Abstract: Alloy yield dynamic libraries were established based on self-learning of historical data and computer technology,through which accurate real-time alloy yield was obtained. Alloy charging optimization model was established for the First Steel-Making Plant of Tianjin Pipe Co. Ltd using the two-stage simplex method. As the operation of steelmaking is online,the alloy yields of different steel grades are obtained,and the accuracy of dosage of the alloy is improved. By optimizing alloy charging,average cost of alloy charging for each steel grade has been reduced. The maximum of the cost reduced is 54.96 yuan·t-1,and the minimum is 8.57 yuan·t-1.The cost of alloy charging per ton steel was reduced by 6.76%-11.40%,and the average cost was reduced by 9.74%.

     

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