Multiple Neural Network Modeling Method for Carbon and Temperature Estimation in Basic Oxygen Furnace

  • Xin Wang
  • Zhong-Jie Wang
  • Jun Tao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)


In this paper, a novel multiple Neural Network (NN) models including forecasting model, presetting model, adjusting model and judgment model for Basic Oxygen Furnace (BOF) steelmaking dynamic process is introduced. The control system is composed of the preset model of the dynamic requirement for oxygen blowing and coolant adding, bath [C] and temperature prediction model, and judgment model for blowing-stop. In this method, NN technology is used to construct these models above; Fuzzy Inference (FI) is adopted to derive the control law. The control method of BOF steelmaking process has been successfully applied in some steelmaking plants to improve the bath Hit Ratio (HR) significantly.


Radial Basis Function Basic Oxygen Furnace Dynamic Oxygen Lance Height Steel Bath 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xin Wang
    • 1
  • Zhong-Jie Wang
    • 2
  • Jun Tao
    • 3
  1. 1.Institute of Information & Control Technology, Center of Electrical & Electronic TechnologyShanghai Jiao Tong UniversityShanghaiP.R. China
  2. 2.Department of Control Science & EngineeringTongji UniversityShanghai
  3. 3.Baosight Software CorporationShanghaiChina

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