Optimal Control of Electric Arc Furnace Steelmaking Process

Optimal Control of Electric Arc Furnace Steelmaking Process

1. Cost Control Optimization System  

In recent years, researchers from the University of Science and Technology Beijing have applied the spatiotemporal multiscale theory to the study of the electric arc furnace (EAF) steelmaking process, revealing that material transformation involves multiple scales—from microscopic and mesoscopic levels to unit‑operation and station‑level structures. Building upon existing process control models used in steel enterprises worldwide, a multiscale model integrating cost control and expert guidance for EAF steelmaking has been developed. This model enables cost monitoring and real‑time process optimization across EAF operations, ladle refining, and continuous casting.

The system has been successfully implemented in several steel plants, including Xinyu Xinliang Special Steel, Hengyang Steel Pipe, Malaysia Anyu Steel, Taiwan Yisheng Steel, Xining Special Steel, and Tianjin Steel. Practical results show average reductions of 2 Nm³ in oxygen consumption per ton of steel, 2 kWh in power consumption, and 10 kg in metallic material usage, leading to a cost saving of over 30 yuan per ton. The economic and social benefits are significant.

2. End‑Point Control in EAF Steelmaking  

With advances in intelligent algorithms, techniques such as artificial neural networks, support vector machines, and genetic algorithms have been introduced into EAF steelmaking. These data‑driven models have achieved promising results in endpoint prediction. However, purely data‑based “black‑box” models often lack integration with process mechanisms. Consequently, hybrid endpoint prediction models that combine reaction kinetics with intelligent algorithms are being developed.

Looking forward, the integration of more effective real‑time monitoring technologies with high‑reliability intelligent models is expected to be a key research direction, enhancing the accuracy and robustness of endpoint control in EAF steelmaking.

3. Holistic Intelligent Control of the Smelting Process  

Advances in sensing technology and computing have enabled intelligent control to extend beyond isolated process segments. A holistic approach—analyzing real‑time smelting data in conjunction with fundamental process mechanisms—allows for integrated decision‑making and control, aiming at overall optimization of the EAF steelmaking process.

Real‑time integrated control systems significantly improve energy efficiency, productivity, and operational safety. By adopting advanced detection technologies and condition‑monitoring strategies, these systems maximize production efficiency, optimize energy conversion, and minimize overall costs.

The development of fully intelligent EAF process control relies on the maturity of control technologies at each sub‑process level. Research in this area is still emerging, and continued refinement of monitoring methods and control models will further drive the evolution toward comprehensive intelligent control in electric arc furnace steelmaking.

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