Special column on intelligent control of ironmaking process
AN Jianqi, GUO Yunpeng, ZHANG Xinmin, DU Sheng, HUANG Yuanfeng, WU Min
With the advancement of carbon peak and carbon neutrality policy,higher demands have been placed on the blast furnace ironmaking process,which constitutes a primary energy consumption segment within the iron and steel industry. Achieving intelligent sensing of key indicators,diagnosing furnace conditions,and optimizing control of operational parameters in the blast furnace ironmaking process is of paramount significance for promoting its safe,green,and low-carbon development. Firstly,taking intelligent sensing and prediction of key state indicators in blast furnaces as a starting point,providing a comprehensive review of sensing and prediction methods for three critical indicators:gas utilization rate,molten iron silicon content,and permeability index. Secondly,an analysis of the current research status of blast furnace condition monitoring and diagnosis is conducted from two perspectives:expert system and data-driven approaches. Subsequently,advancements in optimization and control of blast furnace operation parameters are reviewed from three angles:expert system and expert experience extraction,multi-objective optimization,and data-driven predictive control. Finally, by analyzing the strengths and weaknesses of various models and algorithms,the current challenges and development directions for intelligent sensing,furnace condition diagnosis,and operation optimization of blast furnaces are proposed.