Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/4682
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dc.contributor.authorİnal, Fikret-
dc.contributor.authorTayfur, Gökmen-
dc.contributor.authorMelton, Tyler R.-
dc.contributor.authorSenkan, Selim M.-
dc.date.accessioned2016-05-30T11:03:10Z
dc.date.available2016-05-30T11:03:10Z
dc.date.issued2003-08
dc.identifier.citationİnal, F., Tayfur, G., Melton, T. R., and Senkan, S. M. (2003). Experimental and artificial neural network modeling study on soot formation in premixed hydrocarbon flames. Fuel, 82(12), 1477-1490. doi:10.1016/S0016-2361(03)00060-7en_US
dc.identifier.issn0016-2361
dc.identifier.issn0016-2361-
dc.identifier.urihttp://doi.org/10.1016/S0016-2361(03)00060-7
dc.identifier.urihttp://hdl.handle.net/11147/4682
dc.description.abstractThe formation of soot in premixed flames of methane, ethane, propane, and butane was studied at three different equivalence ratios. Soot particle sizes, number densities, and volume fractions were determined using classical light scattering measurement techniques. The experimental data revealed that the soot properties were sensitive to the fuel type and combustion parameter equivalence ratio. Increase in equivalence ratio increased the amount of soot formed for each fuel. In addition, methane flames showed larger particle diameters at higher distances above the burner surface and propane, ethane, and butane flames came after the methane flames, respectively. Three-layer, feed-forward type artificial neural networks having seven input neurons, one output neuron, and five hidden neurons for soot particle diameter predictions and seven hidden neurons for volume fraction predictions were used to model the soot properties. The network could not be trained and tested with sufficient accuracy to predict the number density due to a large data range and greater uncertainty in determination of this parameter. The number of complete data set used in the model was 156. There was a good agreement between the experimental and predicted values, and neural networks performed better when predicting output parameters (i.e. soot particle diameters and volume fractions) within the limits of the training data.en_US
dc.language.isoengen_US
dc.publisherElsevier Ltd.en_US
dc.relation.ispartofFuelen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectSooten_US
dc.subjectHydrocarbon flamesen_US
dc.subjectCombustionen_US
dc.subjectLight scatteringen_US
dc.subjectArtificial neural networksen_US
dc.titleExperimental and artificial neural network modeling study on soot formation in premixed hydrocarbon flamesen_US
dc.typearticleen_US
dc.authoridTR30587en_US
dc.authoridTR2054en_US
dc.contributor.institutionauthorİnal, Fikret-
dc.contributor.institutionauthorTayfur, Gökmen-
dc.contributor.departmentIzmir Institute of Technology. Chemical Engineeringen_US
dc.contributor.departmentIzmir Institute of Technology. Civil Engineeringen_US
dc.identifier.volume82en_US
dc.identifier.issue12en_US
dc.identifier.startpage1477en_US
dc.identifier.endpage1490en_US
dc.identifier.wosWOS:000183747700005
dc.identifier.scopusSCOPUS:2-s2.0-0038555595
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1016/S0016-2361(03)00060-7-
dc.relation.doi10.1016/S0016-2361(03)00060-7en_US
dc.coverage.doi10.1016/S0016-2361(03)00060-7en_US
item.openairetypearticle-
item.cerifentitytypePublications-
item.languageiso639-1en-
item.fulltextWith Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextopen-
crisitem.author.deptDepartment of Chemical Engineering-
crisitem.author.deptDepartment of Civil Engineering-
Appears in Collections:Chemical Engineering / Kimya Mühendisliği
Civil Engineering / İnşaat Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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