【摘要】 传统电弧炉炼钢面临自动化程度低、炉况复杂度高、冶炼节奏波动性大、终点控制不稳定等问题，主要依赖人工经验操作。近年来随着人工智能、工业互联网迅速发展，电弧炉炼钢向控制智能化升级已成大势所趋。系统性介绍了电弧炉炼钢智能指导模型的构建与应用情况，在配料、供电、供氧、合金加料、成分预报等方面深度融合数据驱动与炼钢机理，突破多元物质-能量流协同调控、终点动态预测等关键技术，优化电弧炉炼钢控制技术，为电弧炉炼钢绿色洁净化、高效生产化升级提供理论支撑与实践典型案例，助力电弧炉资源利用率提升与智能转型。更多还原

【Abstract】 Traditional electric arc furnace(EAF) steelmaking faces challenges such as low automation level, complex furnace conditions, fluctuating smelting rhythms, and unstable endpoint control, predominantly relying on manual empirical operations.With the rapid advancement of artificial intelligence and industrial internet technologies, the intelligent upgrading of EAF steelmaking process control has become an inevitable trend.It systematically presents the development and application of intelligent guidance models for EAF steelmaking.By deeply integrating data-driven approaches with metallurgical mechanisms in critical areas including charge optimization, power supply regulation, oxygen injection control, alloy addition, and composition prediction, the proposed models address key technical bottlenecks in coordinated control of multi-component mass-energy flows and dynamic endpoint prediction.The optimized control framework enhances EAF steelmaking technology, providing theoretical foundations and engineering paradigms for green and clean production, high-efficiency process upgrading, and intelligent transformation.These advancements significantly improve resource utilization efficiency and promote the digitalization of EAF steelmaking.更多还原