Estimating Seebeck Coefficient of a p-Type High Temperature Thermoelectric Material Using Bee Algorithm Multi-layer Perception

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dc.authoridKilinc, Enes/0000-0002-9585-998X
dc.authoridUYSAL, Fatih/0000-0001-5883-9317
dc.contributor.authorUysal, Fatih
dc.contributor.authorKilinc, Enes
dc.contributor.authorKurt, Huseyin
dc.contributor.authorCelik, Erdal
dc.contributor.authorDugenci, Muharrem
dc.contributor.authorSagiroglu, Selami
dc.date.accessioned2024-09-29T15:51:28Z
dc.date.available2024-09-29T15:51:28Z
dc.date.issued2017
dc.departmentKarabük Üniversitesien_US
dc.description.abstractThermoelectric generators (TEGs) convert heat into electrical energy. These energy-conversion systems do not involve any moving parts and are made of thermoelectric (TE) elements connected electrically in a series and thermally in parallel; however, they are currently not suitable for use in regular operations due to their low efficiency levels. In order to produce high-efficiency TEGs, there is a need for highly heat-resistant thermoelectric materials (TEMs) with an improved figure of merit (ZT). Production and test methods used for TEMs today are highly expensive. This study attempts to estimate the Seebeck coefficient of TEMs by using the values of existing materials in the literature. The estimation is made within an artificial neural network (ANN) based on the amount of doping and production methods. Results of the estimations show that the Seebeck coefficient can approximate the real values with an average accuracy of 94.4%. In addition, ANN has detected that any change in production methods is followed by a change in the Seebeck coefficient.en_US
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK) [115M579]; Office of Scientific Research Projects in Karabuk University, Turkey [KBU-BAP-14/2-DR-016]en_US
dc.description.sponsorshipThis work was carried out as part of the Scientific and Technological Research Council of Turkey (TUBITAK) under Project No. 115M579. We would like to thank the Office of Scientific Research Projects in Karabuk University, Turkey as part of Project No. KBU-BAP-14/2-DR-016.en_US
dc.identifier.doi10.1007/s11664-017-5497-6
dc.identifier.endpage4938en_US
dc.identifier.issn0361-5235
dc.identifier.issn1543-186X
dc.identifier.issue8en_US
dc.identifier.scopus2-s2.0-85017226512en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage4931en_US
dc.identifier.urihttps://doi.org/10.1007/s11664-017-5497-6
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4095
dc.identifier.volume46en_US
dc.identifier.wosWOS:000404530900031en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofJournal of Electronic Materialsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectThermoelectric materialen_US
dc.subjectthermoelectric generatoren_US
dc.subjectSeebeck coefficienten_US
dc.subjectartificial neural networken_US
dc.subjectback propagationen_US
dc.subjectbee algorithmen_US
dc.titleEstimating Seebeck Coefficient of a p-Type High Temperature Thermoelectric Material Using Bee Algorithm Multi-layer Perceptionen_US
dc.typeArticleen_US

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