Research progress on multi-physics field coupling and optimization control technology in the electric arc furnace steelmaking process

Electric Arc Furnace (EAF) steelmaking technology has become a crucial technical pathway for the iron and steel industry to achieve green and low-carbon transformation, owing to its significant carbon emission reduction advantages and resource recycling characteristics. However, the EAF steelmaking process involves strong nonlinear coupling interactions among electromagnetic, temperature, flow, and chemical reaction fields. The composition and morphology of scrap steel feedstock vary greatly, and operational conditions change rapidly and are complex. These characteristics pose severe challenges to the precise modeling and optimal control of the process. This paper systematically reviews research progress in the EAF field regarding the analysis of multi-physics field coupling mechanisms, the development of numerical simulation methods, and the application of intelligent control technologies. At the level of mechanism research, it delves into the thermodynamic driving mechanisms and kinetic evolution laws of the scrap melting process, clarifying the influence mechanism of alloying elements on phase transformation temperature, the dominant role of arc radiation heat transfer, the enhancing effect of forced convection in the molten pool on mass transfer processes, and the contribution of chemical reactions to energy balance. Particularly, the multi-component multiphase reaction model developed based on the double-film theory represents an important shift from equilibrium assumptions to kinetic descriptions, providing a new theoretical framework for understanding the mechanisms of the refining process. In terms of numerical simulation, it systematically summarizes the technological evolution from empirical correlations, lumped parameter models, and distributed parameter models to Computational Fluid Dynamics (CFD)-based multi-physics field coupling simulations. It focuses on the unique advantages of coupled electro-thermal-flow-chemical models in revealing complex transport phenomena and reaction kinetic characteristics within the furnace, as well as key advancements in significantly improving computational efficiency through techniques like mesh optimization, parallel computing, and reduced-order modeling. Regarding control and optimization, it reviews the development trend from classical feedback control to modern intelligent control, analyzes the application characteristics of advanced algorithms such as Model Predictive Control (MPC), fuzzy logic control, neural networks, and reinforcement learning in handling multivariate coupling, nonlinear dynamics, and uncertain disturbances, and summarizes the significant effects of deep integration of mechanistic models and data-driven methods in improving prediction accuracy and control performance. Industrial practices show that the integrated application of advanced monitoring technologies and intelligent control systems can significantly reduce energy consumption, raw material consumption, shorten the smelting cycle, and improve product quality stability. This paper further identifies key scientific issues and technical bottlenecks in current research, including understanding multi-scale coupling mechanisms, characterizing physical property parameters under extreme conditions, real-time simulation capabilities, coordinated control across multiple time scales, and adaptability to complex conditions. From perspectives such as developing advanced in-situ detection technologies, constructing high-fidelity digital twin systems, developing physics-informed machine learning models, designing hierarchical control architectures, and exploring new low-carbon metallurgical processes, it prospects future research directions in adaptive control, multi-modal data fusion, and intelligent decision system development, providing theoretical reference for promoting innovation in EAF intelligent technology and the green, low-carbon development of the iron and steel industry.