Improvement of thermal insulation properties of polyester nonwoven and estimation of thermal conductivity coefficients using artificial neural network

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dc.contributor.authorEyüpoğlu, Can
dc.contributor.authorEyüpoğlu, Şeyda
dc.contributor.authorMerdan, Nigar
dc.date.accessioned2020-11-21T15:53:17Z
dc.date.available2020-11-21T15:53:17Z
dc.date.issued2019en_US
dc.departmentİstanbul Ticaret Üniversitesien_US
dc.description.abstractIn this study, polyester, i.e., Poly(ethylene terephthalate) (PET) nonwoven fabric, was coated with white tuff, perlite, and volcanic stone powder at rates of 10, 20, 30 and 40 % so as to increase the thermal insulation properties of PET nonwoven fabric. In order to apply white tuff, perlite, and volcanic stone powder to PET nonwoven fabric, polyurethane-based coating material was used as a cross-linking agent. The porosity and thermal conductivity coefficients of samples were then investigated as regards the type and concentration of stone powder. Furthermore, three-layer feed-forward artificial neural network (3FFNN) was used to estimate and verify the accuracy of the thermal conductivity coefficient of PET nonwovens coated with white tuff, perlite, and volcanic stone powder. The results showed that perlite stone powder provided higher thermal insulation compared to white tuff and volcanic stone powder. Moreover, thermal insulation coefficient of samples increased with the rise in concentration of white tuff, perlite, and volcanic stone powder. Besides, the accuracy of 3FFNN was 99 %. Artificial neural network (ANN)-based results showed that the thermal conductivity coefficients of samples with four different concentrations obtained from white tuff, perlite, and volcanic stone powder were almost the same for experimental and ANN-trained models. According to the results, it was seen that 3FFNN was correctly modeled, and the prediction of the thermal conductivity coefficients was successfully realized. Copyright © 2019 by ASTM International,en_US
dc.identifier.doi10.1520/JTE20180129en_US
dc.identifier.endpage1086en_US
dc.identifier.issn0090-3973
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85064931783en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage1075en_US
dc.identifier.urihttps://doi.org/10.1520/JTE20180129
dc.identifier.urihttps://hdl.handle.net/11467/3533
dc.identifier.volume47en_US
dc.identifier.wosWOS:000464850800024en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherASTM Internationalen_US
dc.relation.ispartofJournal of Testing and Evaluationen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial neural networken_US
dc.subjectCoatingen_US
dc.subjectNonwovenen_US
dc.subjectPorosityen_US
dc.subjectThermal insulationen_US
dc.titleImprovement of thermal insulation properties of polyester nonwoven and estimation of thermal conductivity coefficients using artificial neural networken_US
dc.typeArticleen_US

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