Relevance vector machine with hybrid kernel-based soft sensor via data augmentation for incomplete output data in sintering process

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dc.authoridHu, Jie/0000-0002-1725-6366
dc.authoridCao, Wei-Hua/0000-0002-9677-9586
dc.authoridWu, Min/0000-0002-0668-8315
dc.contributor.authorHu, Jie
dc.contributor.authorLi, Hongxiang
dc.contributor.authorLi, Huihang
dc.contributor.authorWu, Min
dc.contributor.authorCao, Weihua
dc.contributor.authorPedrycz, Witold
dc.date.accessioned2024-05-19T14:46:40Z
dc.date.available2024-05-19T14:46:40Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractA ratio of CO and CO2 (CO/CO2) is a key indicator of sintering carbon consumption, which is difficult to be determined in real-time. Therefore, the establishment of its soft sensing model is of great practical significance. This paper proposes a novel CO/CO2 soft sensing model with incomplete output data based on relevance vector machine with hybrid kernel via data augmentation. First, a least absolute shrinkage and selection operator is employed for determining key input variables of the model, and an automatic fuzzy clustering framework is used to automatically identify multiple operating modes. Then, a relevance vector machine with hybrid kernel method is presented to model each operating mode separately. Meanwhile, considering the problem of incomplete input and output data, data augmentation is applied in modeling to enhance the model performance. Finally, the soft sensing model of CO/CO2 is formed. Experimental results and analyses using actual production data coming from the sintering production process demonstrate that the prediction performance and accuracy of the proposed model outperform some existing algorithms.en_US
dc.description.sponsorshipNational Natural Science Foundation of China [62303431]; The 111 project [B17040]; Hubei Provincial Natural Science Foundation of China [2021CFB145]; Key Program of Hubei Provincial Technical Innovation Project [2023BAB080]; 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 62303431, in part by the 111 project under Grant B17040, in part by the Hubei Provincial Natural Science Foundation of China under Grant 2021CFB145, in part by the Key Program of Hubei Provincial Technical Innovation Project under Grant No. 2023BAB080, and in part by the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) under Grant CUG2106210.en_US
dc.identifier.doi10.1016/j.conengprac.2024.105850
dc.identifier.issn0967-0661
dc.identifier.issn1873-6939
dc.identifier.scopus2-s2.0-85182513576en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1016/j.conengprac.2024.105850
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5570
dc.identifier.volume145en_US
dc.identifier.wosWOS:001164395500001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofControl Engineering Practiceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectSintering Processen_US
dc.subjectActual Production Dataen_US
dc.subjectCo/Co2 Soft Sensing Modelen_US
dc.subjectData Augmentationen_US
dc.subjectRelevance Vector Machineen_US
dc.titleRelevance vector machine with hybrid kernel-based soft sensor via data augmentation for incomplete output data in sintering processen_US
dc.typeArticleen_US

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