Multiple neural network modeling method for carbon and temperature estimation in basic oxygen furnace

Authors:
Xin Wang;Zhong-Jie Wang;Jun Tao
Affiliations:
Institute of Information & Control Technology, Center of Electrical & Electronic Technology, Shanghai Jiao Tong University, Shanghai, P.R. China;Department of Control Science & Engineering, Tongji University, Shanghai;Baosight Software Corporation, Shanghai, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Year:
2006

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Abstract

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.