Research on Electro-hydraulic Integration Technology for Electrode Lifting System of Electric Arc Furnace
|School||Xi'an University of Architecture and Technology|
|Course||Mechanical and Electronic Engineering|
|Keywords||EAF Hydraulic system Neural Network Control Hydromechatronics|
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.