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Application Research of Electric Arc Furnace in Metallurgy
In the mid-1980s, a global wave of research and application of artificial neural networks began. Early efforts in the U.S., Japan, and other countries applied neural networks to metallurgical processes, solving critical technical issues and achieving remarkable results. For instance, Nippon Steel’s Oita Plant used a BP network to predict gas flow patterns within the furnace, combined with an expert system to forecast furnace conditions and guide operations, achieving over 90% accuracy. The U.S.-developed Intelligent Arc Furnace (IAF) utilized neural networks for real-time electrode position adjustments, resulting in a 30.89% energy saving and improved productivity. This system has been successfully implemented by companies like Guangzhou Iron and Steel in China. The integration of AI and neural networks in electric arc furnace steelmaking holds significant value for enhancing quality, efficiency, safety, and energy conservation.
Neural networks have been extensively applied in electric arc furnace steelmaking, with systems using three models: furnace simulators, regulators, and a controller with over 200 neurons employing an extended Bar-adaptive algorithm. These networks learn to adjust electrode positions to meet setpoints, minimizing grid flicker disturbances. Composite elbows, made with steel pipes and high-chromium cast iron linings, offer wear resistance, corrosion resistance, and impact strength, resolving long-standing issues in conveyor systems. Numerical simulation during development helps identify design flaws, reducing costs and development time.
The foundry technology network adjusts current and power factor every 15 seconds, allowing continuous improvement in furnace prediction and adaptation to changing conditions. This ensures better handling of scrap loading, voltage, electrode length, and system impedance. Modern electric arc furnace processes involve complex power supply and refining equipment, making the establishment of a comprehensive model a key research focus.
While expert control systems excel in power balance and power factor, they often lag in response, causing fluctuations and harmonic distortion. To address this, a neural network prediction system was introduced, estimating future furnace states and optimizing expert system outputs. This improves stability, reduces reactive power, and minimizes grid damage. In electrode control, neural networks help decouple three-phase systems, leading to more efficient operation.
Neural networks are widely used in electric arc furnace steelmaking due to their ability to adapt to changing load characteristics. The BP network is popular for its simplicity, adaptability, and generalization capabilities, though it suffers from slow convergence. Techniques like variable step sizes and momentum items are employed to improve performance. There is no standard theory for selecting models or algorithms, which are typically determined through experimentation.
Combining neural networks with mathematical models, expert systems, and fuzzy logic enhances problem-solving in metallurgy. Key research areas include optimizing expert systems, developing three-phase electrode control methods, adaptive online adjustments, power curve optimization, intelligent endpoint prediction, and integrated automation systems. These advancements aim to improve efficiency, reduce energy consumption, and enhance overall production quality in electric arc furnace steelmaking.