Using hybridized ANN-GA prediction method for DOE performed drying experiments

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dc.contributor.authorAkkoyunlu, Mehmet Cabir
dc.contributor.authorPekel, Engin
dc.contributor.authorAkkoyunlu, Mustafa Tahir
dc.contributor.authorPusat, Saban
dc.date.accessioned2024-02-23T14:20:15Z
dc.date.available2024-02-23T14:20:15Z
dc.date.issued2020
dc.departmentNEÜen_US
dc.description.abstractCoal is an important component in the energy industry and plays a key role in energy-producing facilities. Moisture is a common condition that has a considerable impact on coal. Coal drying has long been a question of great interest in a wide range of fields. Defining parameters in the coal drying is obtained by experiments. High costs, time constraints, and repetition of an experiment are one of the most frequently stated problems with experimental works. Using qualitative methods with experiments can be more useful for identifying and characterizing the coal drying process. The purpose of this article is finding the effective parameters in the coal drying process by using a hybridized prediction method. Genetic Algorithm (GA) and Artificial Neural Network (ANN) are hybridized with each other to identify and characterize the coal drying process. GA-ANN algorithm is applied to the coal drying process to predict the moisture of coal, but it does not provide a decent result at first. Later, the Design of Experiment (DoE) methodology is performed to determine the main effects of six parameters. Two scenarios are generated because two parameters are not statistically significant. The first scenario excludes the air relative humidity parameter, and the second scenario excludes the air relative humidity and the velocity of air parameters. Following the application of the DoE method, GA-ANN reaches decent results in scenario-2.en_US
dc.identifier.doi10.1080/07373937.2020.1750027
dc.identifier.endpage1399en_US
dc.identifier.issn0737-3937
dc.identifier.issn1532-2300
dc.identifier.issue11en_US
dc.identifier.scopus2-s2.0-85083901460en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1393en_US
dc.identifier.urihttps://doi.org/10.1080/07373937.2020.1750027
dc.identifier.urihttps://hdl.handle.net/20.500.12452/13081
dc.identifier.volume38en_US
dc.identifier.wosWOS:000526510500001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTaylor & Francis Incen_US
dc.relation.ispartofDrying Technologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectLow-Rank Coalen_US
dc.subjectDryingen_US
dc.subjectMoistureen_US
dc.subjectGenetic Algorithmen_US
dc.subjectArtificial Neural Networken_US
dc.subjectDesign Of Experimenten_US
dc.titleUsing hybridized ANN-GA prediction method for DOE performed drying experimentsen_US
dc.typeArticleen_US

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