东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2003, Vol. 24 ›› Issue (8): 715-718.DOI: -

• 论著 •    下一篇

转炉炼钢动态过程预设定模型的混合建模与预报

王永富;李小平;柴天佑;谢书明   

  1. 东北大学自动化研究中心;东北大学自动化研究中心;东北大学自动化研究中心;沈阳工业大学 辽宁沈阳　110004
  • 收稿日期:2013-06-24 修回日期:2013-06-24 发布日期:2013-06-24
  • 通讯作者: Wang, Y.-F.
  • 作者简介:-
  • 基金资助:
    国家自然科学基金资助项目(60074019)

Hybrid modeling and prediction of the dynamic BOF steelmaking process

Wang, Yong-Fu (1); Li, Xiao-Ping (1); Chai, Tian-You (1); Xie, Shu-Ming (2)   

  1. (1) Res. Ctr. of Automat., Northeastern Univ., Shenyang 110004, China; (2) Shenyang Univ. of Technol., Shenyang 110023, China
  • Received:2013-06-24 Revised:2013-06-24 Published:2013-06-24
  • Contact: Wang, Y.-F.
  • About author:-
  • Supported by:
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摘要： 准确预报转炉炼钢动态过程的补吹氧气用量和冷却剂添加量,对于提高终点命中率具有重要意义·采用机理模型及基于数据的自适应神经模糊推理系统混合建模方法建立了转炉炼钢动态过程预设定模型·用减法聚类,最小二乘法及梯度下降法辨识了T S模型并用该模型对机理模型进行补偿建模·对一座180t转炉的实测数据进行了仿真,仿真结果表明该方法是切实可行并有效的·

关键词: 转炉, 炼钢, 混合建模, 预设定模型, 自适应神经模糊系统, T-S模型, 减法聚类

Abstract: A new framework was presented for the accurate modeling and prediction of the reblown oxygen and the added coolant in dynamic basic-oxygen-furnace (EOF) steelmaking processes. The proposed method takes advantages of the modeling approach based on mechanism and uses adaptive neural-network-fuzzy-inference system (ANFIS) to compensate for the BOF modeling uncertainties based on mechanism. In the ANFIS compensating model, the first-order Takagi-Sugeno type fuzzy rules were employed and a hybrid algorithm combining the least square method (LSM) and the gradient descent method was adopted to obtain the model structure. The practical data of a 180 t converter were simulated. The simulated results are close to the practical values. The method is practicable and effective.

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