Dissertation
Dissertation > Industrial Technology > Electrotechnical > Electrification,electrical energy application > Electric heating > Electric arc furnace

Research on Electro-hydraulic Integration Technology for Electrode Lifting System of Electric Arc Furnace

Author YuYongFeng
Tutor GuLiChen
School Xi'an University of Architecture and Technology
Course Mechanical and Electronic Engineering
Keywords EAF Hydraulic system Neural Network Control Hydromechatronics
CLC TM924.4
Type Master's thesis
Year 2009
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Three-phase AC arc furnace electrode lifting system is a complex nonlinear multivariable control system . Wherein the control electrode of the hydraulic lifting system is particularly important . Electrode lifting systems and metallurgical electrode regulator EAF control is the core component , and its performance has a direct impact on the efficiency of the electric arc furnace and the electrical consumption, electrode wear and others have a great impact. This project combines a steel company 160 t electric arc furnace steelmaking projects on domestic and international research and development of electric arc dynamics control for electric arc furnace hydraulic lift raised related to the transformation , failure analysis and BP neural network based control EAF methods . Thesis, mainly the following three points: First, the analysis of the structural design of hydraulic lift and electric arc fault ; lifting system for electric arc furnace and column column connections were made, starting from the real problems of the electrodes and the lid of the non- normal lifting analyzed, proposed a method of handling the fault . Secondly, a transfer function of the hydraulic power system , the system parameters (such as: open- loop natural frequency amplification factor K ω, damping ratio ζ) coupling between the analysis of the circumstances , for the study of rapidity , accuracy, stability, providing theoretical basis ; based on MATLAB , using PID control technology, by analyzing various properties of hydraulic drive system , determine the PID controller parameters ; snorkels through the electric arc furnace modeling and frequency response , Bode diagram , stability, error analysis , presented a series of characteristic indicators and optimization methods . Finally , the establishment of neural network model, using MATLAB 6.5 neural network toolbox BP neural network simulation ; and estimate the response of the system , will be estimated and actual values ??for comparing the calculated and actual control of the amount of correction . Simulation results show that this model significantly improves the co-ordination of the three-phase arc furnace electrode , making the overall relationship between each electrode is greatly improved ; presents a reliable lift system for electric arc furnace electrode control methods.

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