Intelligent prediction and soft-sensing of comprehensive production indicators for iron ore sintering: a review

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dc.authorscopusidWitold Pedrycz / 58861905800
dc.authorwosidWitold Pedrycz / HJZ-2779-2023
dc.contributor.authorDu, Sheng
dc.contributor.authorMa, Xian
dc.contributor.authorFan, Haipeng
dc.contributor.authorHu, Jie
dc.contributor.authorCao, Weihua
dc.contributor.authorWu, Min
dc.contributor.authorPedrycz, Witold
dc.date.accessioned2025-04-17T12:35:07Z
dc.date.available2025-04-17T12:35:07Z
dc.date.issued2025
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractIron ore sintering is a critical process in iron and steel production, with a substantial impact on overall energy consumption and the emission of various environmental pollutants. Enhancing the efficiency of this process is crucial for achieving sustainability in the iron and steel industry. Accurate prediction and real-time monitoring of comprehensive production indicators are essential for optimizing production and improving energy efficiency. This paper provides a systematic review of intelligent prediction and soft-sensing techniques applied to the iron ore sintering process. It details the mechanisms and operational principles of these technologies, with a focus on key indicators such as quality, thermal state, yield, and energy consumption. This paper explores the current state-of-the-art in four prediction methodologies: mechanism analysis-based methods, data feature analysis-based methods, multi-model fusion-based methods, and operating mode recognition-based methods. Finally, the challenges to the current comprehensive production indicator prediction of the sintering process are pointed out, including the difficulty of dealing with the changing operating mode, the incomplete analysis of image features, and the insufficient consideration of the differences in data distribution. In the future, operating mode recognition approaches, deep learning approaches, transfer learning approaches, and computer vision techniques will have a broad prospect in the comprehensive production indicator prediction of the sintering process.
dc.identifier.citationDu, S., Ma, X., Fan, H., Hu, J., Cao, W., Wu, M., & Pedrycz, W. (2025). Intelligent prediction and soft-sensing of comprehensive production indicators for iron ore sintering: A review. Computers in Industry, 165, 104215.
dc.identifier.doi10.1016/j.compind.2024.104215
dc.identifier.endpage12
dc.identifier.issn0166-3615
dc.identifier.issn1872-6194
dc.identifier.scopus2-s2.0-85211495453
dc.identifier.scopusqualityQ1
dc.identifier.startpage1
dc.identifier.urihttp://dx.doi.org/10.1016/j.compind.2024.104215
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6248
dc.identifier.volume165
dc.identifier.wosWOS:001385171200001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorPedrycz, Witold
dc.institutionauthoridWitold Pedrycz / 0000-0002-9335-9930
dc.language.isoen
dc.publisherElsevier b.v.
dc.relation.ispartofComputers in industry
dc.relation.publicationcategoryDiğer
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectData-Driven
dc.subjectIndicator Prediction
dc.subjectIntelligent Prediction
dc.subjectOperating Mode
dc.subjectSintering Process
dc.titleIntelligent prediction and soft-sensing of comprehensive production indicators for iron ore sintering: a review
dc.typeOther

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