Identification of working conditions and prediction of FeO content in sintering process of iron ore fines  

作　　者：Xiao-ming Li Bao-rong Wang Zhi-heng Yu Xiang-dong Xing 

机构地区：[1]School of Metallurgical Engineering,Xi’an University of Architecture and Technology,Xi’an,710055,Shaanxi,China

出　　处：《Journal of Iron and Steel Research International》2024年第9期2090-2100,共11页钢铁研究学报（英文版）

基　　金：the National Natural Science Foundation of China(52174325);the Key Research and Development Program of Shaanxi(Grant Nos.2020GY-166 and 2020GY-247);the Shaanxi Provincial Innovation Capacity Support Plan(Grant No.2023-CX-TD-53).

摘　　要：The iron oxide(FeO)content had a significant impact on both the metallurgical properties of sintered ores and the economic indicators of the sintering process.Precisely predicting FeO content possessed substantial potential for enhancing the quality of sintered ore and optimizing the sintering process.A multi-model integrated prediction framework for FeO content during the iron ore sintering process was presented.By applying the affinity propagation clustering algorithm,different working conditions were efficiently classified and the support vector machine algorithm was utilized to identify these conditions.Comparison of several models under different working conditions was carried out.The regression prediction model characterized by high precision and robust stability was selected.The model was integrated into the comprehensive multi-model framework.The precision,reliability and credibility of the model were validated through actual production data,yielding an impressive accuracy of 94.57%and a minimal absolute error of 0.13 in FeO content prediction.The real-time prediction of FeO content provided excellent guidance for on-site sinter production.

关 键 词：Iron ore sintering Condition identification FeO prediction Multi-model integrated prediction model Feature engineering 

分 类 号：TF046[冶金工程—冶金物理化学]