A prediction model of artificial neural networks in development of thermoelectric materials with innovative approaches

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dc.contributor.authorKokyay, Seyma
dc.contributor.authorKilinc, Enes
dc.contributor.authorUysal, Fatih
dc.contributor.authorKurt, Huseyin
dc.contributor.authorCelik, Erdal
dc.contributor.authorDugenci, Muharrem
dc.date.accessioned2024-02-23T14:12:48Z
dc.date.available2024-02-23T14:12:48Z
dc.date.issued2020
dc.departmentNEÜen_US
dc.description.abstractThe fact that the properties of thermoelectric materials are to be estimated with Artificial Neural Networks without production and measurement will help researchers in terms of time and cost. For this purpose, figure of merit, which is the performance value of thermoelectric materials, is estimated by Artificial Neural Networks without an experimental study. P-and n-type thermoelectric bulk samples were obtained in 19 different compositions by doping different elements into Ca2.7Ag0.3Co4O9- and Zn0.98Al0.02O-based oxide thermoelectric materials. The Seebeck coefficient, electrical resistivity and thermal diffusivity values of the bulk samples were measured from 200 degrees C to 800 degrees C with an increase rate of 100 degrees C, and figure of merit values were calculated. 7 different Artificial Neural Network models were created using 123 measured results of experimental data and the molar masses of the doping elements. In this system aiming to predict the electrical resistivity, thermal diffusivity and figure of merit values of thermoelectric materials, the average R value and accuracy rate of these values were estimated to be 94% and 80%, respectively. (c) 2020 Karabuk University. Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).en_US
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK) [115M579]; Scientific Research Projects Coordinatorship of Karabuk University [KBU-BAP-16/1-DR-078]en_US
dc.description.sponsorshipThis study was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Grant No. 115M579 and by the Scientific Research Projects Coordinatorship of Karabuk University under Grant No. KBU-BAP-16/1-DR-078.en_US
dc.identifier.doi10.1016/j.jestch.2020.04.007
dc.identifier.endpage1485en_US
dc.identifier.issn2215-0986
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-85085080079en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1476en_US
dc.identifier.urihttps://doi.org/10.1016/j.jestch.2020.04.007
dc.identifier.urihttps://hdl.handle.net/20.500.12452/12191
dc.identifier.volume23en_US
dc.identifier.wosWOS:000595090200004en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier - Division Reed Elsevier India Pvt Ltden_US
dc.relation.ispartofEngineering Science And Technology-An International Journal-Jestechen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectThermoelectric Materialen_US
dc.subjectFigure Of Meriten_US
dc.subjectArtificial Neural Networken_US
dc.subjectPrediction Modelen_US
dc.titleA prediction model of artificial neural networks in development of thermoelectric materials with innovative approachesen_US
dc.typeArticleen_US

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