Dynamic Modeling Framework Based on Automatic Identification of Operating Conditions for Sintering Carbon Consumption Prediction

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dc.authoridCao, Wei-Hua/0000-0002-9677-9586
dc.authoridHu, Jie/0000-0002-1725-6366
dc.authoridWu, Min/0000-0002-0668-8315
dc.contributor.authorHu, Jie
dc.contributor.authorWu, Min
dc.contributor.authorCao, Weihua
dc.contributor.authorPedrycz, Witold
dc.date.accessioned2024-05-19T14:38:49Z
dc.date.available2024-05-19T14:38:49Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractIn iron and steel industry, the sintering process requires huge carbon consumption. Achieving accurate and dynamic prediction of carbon consumption in this process is of evident significance to protect the environment and raise the economic efficiency of iron and steel industry. This article develops an original dynamic modeling framework, including automatic identification of operating conditions, modeling in different conditions, and dynamic prediction model of sintering carbon consumption. First, an automatic kernel-based fuzzy C-means algorithm is presented for the automatic identification of operating conditions. Dynamic relationships between the inputs and output of the model are analyzed by copula entropy, and then, the relevant production data in each operating condition are determined by a just-in-time learning method. Next, broad learning models are established under different operating conditions. Furthermore, a dynamic prediction model of sintering carbon consumption is designed, and the prediction error is adopted to quantify the performance of the model and as one criterion to determine the update of production database. Finally, results of experiments using actual production data demonstrate the advantage and validity of the developed model compared with some advanced modeling methods. The developed model considers complex characteristics of the sintering process and presents better dynamic performance and information mining performance.en_US
dc.description.sponsorshipNational Natural Science Foundation of China [61210011]; 111 project [B17040]; Hubei Provincial Natural Science Foundation of China [2021CFB145, 2015CFA010]; Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) [CUG2106210]en_US
dc.description.sponsorshipThis work was supported in part by the National Natural Science Foundation of China under Grant 61210011, in part by the 111 project under Grant B17040, in part by the Hubei Provincial Natural Science Foundation of China under Grant 2021CFB145 and Grant 2015CFA010, and in part by the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) under Grant CUG2106210.en_US
dc.identifier.doi10.1109/TIE.2023.3270514
dc.identifier.endpage3141en_US
dc.identifier.issn0278-0046
dc.identifier.issn1557-9948
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85159829658en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage3133en_US
dc.identifier.urihttps://doi.org10.1109/TIE.2023.3270514
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4616
dc.identifier.volume71en_US
dc.identifier.wosWOS:001080899800091en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeee-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIeee Transactions on Industrial Electronicsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectActual Production Dataen_US
dc.subjectAutomatic Kernel-Based Fuzzy C-Means (Akfcm) Algorithmen_US
dc.subjectBroad Learning Model (Blm)en_US
dc.subjectCarbon Consumption Predictionen_US
dc.subjectSintering Process (Sp)en_US
dc.titleDynamic Modeling Framework Based on Automatic Identification of Operating Conditions for Sintering Carbon Consumption Predictionen_US
dc.typeArticleen_US

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