Electric Arc Furnace Based on Fuzzy Neural Network Control System and MATLAB Simulation

Abstract:

EAF steelmaking is a complex physical and chemical reactions，has Highly nonlinearity, time variability and strong coupling. As the electrode's contact with the charging circuit, charging the collapse of the gasification furnace components and liquid metal boiling and other factors, often result in dramatic fluctuations in arc current. At this point we need to quickly adjust the location of electrodes, so that arc current stable base on this within a certain range. This article in the fuzzy control and neural network’s foundation,will study a kind based on three-phase consciousness electric arc furnace predictive control new method, Using the fuzzy neural network control adjusting electrode, further enhancing the effectiveness of electric arc furnace’synthesis movement.

• Periodical: Applied Mechanics and Materials (Volumes 48-49)
• Pages: 491-495
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.48-49.491
• Online since: February 2011
• Authors: Qiang Li, Peng Hua Wu
• Keywords: Arc Furnace Steel-Making, Electrode Regulation System, Fuzzy Neural Network Control, MATLAB Simulation, Three-Phase Unified Consciousness

References

[1] Gushu Sheng, Wang Jianhui, Wang Zhenlei. Fuzzy neural network theory and its application in complex systems research, (2002).

[2] Sun Zengqi and so on. Intelligent Control Theory and Technology [M]. Beijing: Tsinghua University Press, 2002: 125-237.

[3] Li Qiang, Pan Yongxiang, Yu Jianming, Liang Li. Integrated intelligent control strategies in the control of electric arc furnace [J]. Electrotechnical Society, 2003, 18 (1) : 100-104.

[4] Zhang Zhiyuan, Wan Pei Lin, Tu Qiaoling. Complete fuzzy neural network fuzzy controller design and simulation [A]. Chongqing Institute of Technology, (2000).

[5] Li Qiang, Liang Li, LIU Qing-feng and so on. Neural network arc furnace - refining furnace control system [J]. Special steel, 2001, 22 (2) : 34-37.

[6] Wu Shiqian , Joo Meng , Gao Yang. A Fast Approach for Automatic Gen2 eration of Fuzzy Rules by Generalized Dynamic Fuzzy Neural Network[J ] . IEEE Trans. on Fuzzy Systems , 2001 , 9 (4) : 578 - 594.

DOI: 10.1109/91.940970

[7] Takagi H. Fusion technology of fuzzy theory and neural networks-survey and future directions [A] . Pro. Int . Conf . on Fuzzy Logic and Neural Networks[C] . 1990. 13～26.

[8] Simpson P K. Fuzzy min2max neural networks - Part 1 : Classification[J ]. IEEE Trans. NeuralNetworks , 1992 , 3(5) : 776～786.