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dc.contributor.authorChugh, Tinkle
dc.contributor.authorChakraborti, Nirupam
dc.contributor.authorSindhya, Karthik
dc.contributor.authorJin, Yaochu
dc.date.accessioned2017-07-31T12:03:27Z
dc.date.available2017-12-20T22:45:09Z
dc.date.issued2017
dc.identifier.citationChugh, T., Chakraborti, N., Sindhya, K., & Jin, Y. (2017). A Data-Driven Surrogate-Assisted Evolutionary Algorithm Applied to a Many-Objective Blast Furnace Optimization Problem. <i>Materials and Manufacturing Processes</i>, <i>32</i>(10), 1172-1178. <a href="https://doi.org/10.1080/10426914.2016.1269923" target="_blank">https://doi.org/10.1080/10426914.2016.1269923</a>
dc.identifier.otherCONVID_26405251
dc.identifier.otherTUTKAID_72217
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/54954
dc.description.abstractA new data-driven reference vector-guided evolutionary algorithm has been successfully implemented to construct surrogate models for various objectives pertinent to an industrial blast furnace. A total of eight objectives have been modeled using the operational data of the furnace using 12 process variables identified through a principal component analysis and optimized simultaneously. The capability of this algorithm to handle a large number of objectives, which has been lacking earlier, results in a more efficient setting of the operational parameters of the furnace, leading to a precisely optimized hot metal production process.
dc.language.isoeng
dc.publisherTaylor & Francis Inc.
dc.relation.ispartofseriesMaterials and Manufacturing Processes
dc.subject.otherblast furnace
dc.subject.otherironmaking
dc.subject.othermetamodeling
dc.subject.othermulti-objective optimization
dc.subject.othermodel management
dc.subject.otherdata-driven optimization
dc.subject.otherPareto optimality
dc.titleA Data-Driven Surrogate-Assisted Evolutionary Algorithm Applied to a Many-Objective Blast Furnace Optimization Problem
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-201707183318
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMathematical Information Technologyen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.date.updated2017-07-18T12:15:22Z
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.format.pagerange1172-1178
dc.relation.issn1042-6914
dc.relation.numberinseries10
dc.relation.volume32
dc.type.versionacceptedVersion
dc.rights.copyright© 2017 Taylor & Francis. This is a final draft version of an article whose final and definitive form has been published by Taylor & Francis. Published in this repository with the kind permission of the publisher.
dc.rights.accesslevelopenAccessfi
dc.subject.ysooptimointi
dc.subject.ysopareto-tehokkuus
dc.subject.ysorautateollisuus
jyx.subject.urihttp://www.yso.fi/onto/yso/p13477
jyx.subject.urihttp://www.yso.fi/onto/yso/p28039
jyx.subject.urihttp://www.yso.fi/onto/yso/p18241
dc.relation.doi10.1080/10426914.2016.1269923
dc.type.okmA1


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