基于收得率动态库的合金加料优化模型

Alloy charging optimization model based on the yield dynamic libraries

  • 摘要: 为了获得准确实时的合金元素收得率,采用历史加料数据自学习的方法,利用计算机技术建立了合金元素收得率动态库,并运用两阶段单纯形算法为天津钢管公司第一炼钢厂建立了合金加料优化模型.通过模型在线运行,得出了不同钢种的合金收得率,从而提高了不同钢种炉次合金加料的准确度.通过优化合金配料,不同钢种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.吨钢合金成本降低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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