Research Progress on Optimal Blending of Iron Ore Powders for Sintering

Abstract

With the gradually accelerated steel industrial transition toward green and low-carbon production, optimizing ore blending in the sintering process as an essential step in reducing costs and improving efficiency faces dual challenges of the declining grade of iron ore resources and the increasing diversification of supply sources. This study first examines the physical and chemical properties of iron ore powder and discusses an ore blending strategy based on the complementarity of ambient-temperature and high-temperature characteristics to enhance sinter strength and reducibility. Subsequently, the role of the sintering cup test in verifying iron ore powder characteristics and the theoretical study of the sintering process are analyzed. While this method provides reliable data support, its high cost and long testing period limit large-scale industrial application. To address this issue, an optimized ore blending model incorporating theoretical analysis and intelligent algorithms is developed from a mathematical modeling perspective to enhance the accuracy and efficiency of intelligent ore blending. Finally, future research prospects in sintering optimization ore blending are discussed. Current challenges include the absence of dynamic modeling linking high-temperature characteristics to sinter quality, the strong reliance of existing models on offline data, and the lack of a systematic multi-objective optimization framework, particularly for carbon emission reduction. Future research should prioritize refining the standard system for high-temperature characteristics in iron ore sintering, advancing intelligent ore blending systems, and fostering the integration of artificial intelligence into the process.