Proposing a machine learning approach to analyze and predict basic high-temperature properties of iron ore fines and its factors
作者机构：Institute of Artificial IntelligenceUniversity of Science and Technology BeijingBeijing100083China School of Intelligence Science and TechnologyUniversity of Science and Technology BeijingBeijing100083China School of Metallurgical and Ecological EngineeringUniversity of Science and Technology BeijingBeijing100083China
出 版 物：《Journal of Iron and Steel Research International》 (国际钢铁研究杂志)
年 卷 期：2024年第31卷第5期
页 面：1082-1094页
核心收录：
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：the National Natural Science Foundation of China(52204335) the Cross-disciplinary Research Project for Young Teachers of the University of Science and Technology Beijing(FRF-IDRY-22-004)
主 题：Iron ore Basic high-temperature property Machine learning Random forest Genetic algorithm
摘 要：The basic high-temperature properties of iron ore play a crucial role in optimizing sintering and ore blending,but the testing process for these properties is complex and has significant lag time,which cannot meet the actual needs of ore blending.A prediction model for the basic high-temperature properties of iron ore fines was thus proposed based on a combination of machine learning algorithms and genetic ***,the prediction accuracy of different machine learning models for the basic high-temperature properties of iron ore fines was ***,a random forest model optimized by genetic algorithms was built,further improving the prediction accuracy of the *** test results show that the random forest model optimized by genetic algorithms has the highest prediction accuracy for the lowest assimilation temperature and liquid phase fluidity of iron ore,with a determination coefficient of 0.903 for the lowest assimilation temperature and 0.927 for the liquid phase fluidity after *** trained model meets the fluctuation requirements of on-site testing and has been successfully applied to actual production on site.
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