{"title":"Identification of working conditions and prediction of FeO content in sintering process of iron ore fines","authors":"Xiao-ming Li, Bao-rong Wang, Zhi-heng Yu, Xiang-dong Xing","doi":"10.1007/s42243-024-01220-7","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":16151,"journal":{"name":"Journal of Iron and Steel Research International","volume":null,"pages":null},"PeriodicalIF":2.5000,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Iron and Steel Research International","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1007/s42243-024-01220-7","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
Publishes critically reviewed original research of archival significance
Covers hydrometallurgy, pyrometallurgy, electrometallurgy, transport phenomena, process control, physical chemistry, solidification, mechanical working, solid state reactions, materials processing, and more
Includes welding & joining, surface treatment, mathematical modeling, corrosion, wear and abrasion
Journal of Iron and Steel Research International publishes original papers and occasional invited reviews on aspects of research and technology in the process metallurgy and metallic materials. Coverage emphasizes the relationships among the processing, structure and properties of metals, including advanced steel materials, superalloy, intermetallics, metallic functional materials, powder metallurgy, structural titanium alloy, composite steel materials, high entropy alloy, amorphous alloys, metallic nanomaterials, etc..