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

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dc.authoridPusat, Saban / 0000-0001-5868-4503
dc.authoridAkkoyunlu, Mehmet Cabir / 0000-0002-9388-6554
dc.authoridPEKEL, Engin / 0000-0002-5295-8013
dc.authorwosidPusat, Saban / AAZ-5375-2020
dc.authorwosidakkoyunlu, mehmet cabir / AAK-4612-2021
dc.contributor.authorAkkoyunlu, Mehmet Cabir
dc.contributor.authorPekel, Engin
dc.contributor.authorAkkoyunlu, Mustafa Tahir
dc.contributor.authorPusat, Saban
dc.date.accessioned2021-11-01T15:05:12Z
dc.date.available2021-11-01T15:05:12Z
dc.date.issued2020
dc.department[Belirlenecek]
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.
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-s2.0-85083901460
dc.identifier.scopusqualityQ1
dc.identifier.startpage1393en_US
dc.identifier.urihttps://doi.org/10.1080/07373937.2020.1750027
dc.identifier.urihttps://hdl.handle.net/11491/7171
dc.identifier.volume38en_US
dc.identifier.wosWOS:000526510500001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthor[Belirlenecek]
dc.language.isoen
dc.publisherTaylor & Francis Inc
dc.relation.ispartofDrying Technology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
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 experiments
dc.typeArticle

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