Abstract:

With the advancement of the digital information era， the digital transformation of blast furnaces has begun. Steel enterprises have applied intelligent closed-loop control， digital twins， and AI-based predictive models to develop intelligent systems for smart blast furnace operation， blast furnace condition assessment， and quality optimization. Research on digital blast furnaces primarily focuses on variable prediction， state diagnosis， and blast furnace condition optimization， with these domains evolving from traditional approaches toward complex optimization modeling， multidimensional comprehensive evaluation， and multi-objective collaborative optimization， respectively. However， current predictive models require enhanced online self-updating and integration of data and mechanisms； evaluation systems need to emphasize multidimensional and fine-grained diagnostics， and blast furnace condition optimization has to overcome single-indicator limitations by focusing on low-risk， low-cost， and multi-objective coupled strategies. According to the actual needs of the blast furnace site， a physical system of blast furnace information was developed， where data， mechanisms， and experience were reasonably matched and called upon to form an integrated technology encompassing data governance， rule mining， intelligent prediction， comprehensive evaluation， multi-objective optimization， and decision feedback， which was identified as one of the key directions for future development of digital blast furnace ironmaking.

Key words: blast furnace, digital technology, intelligent prediction, state diagnosis, blast furnace condition optimization