Developing correlations by extreme learning machine for calculating higher heating values of waste frying oils from their physical properties

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dc.authorid0000-0003-0710-0867en_US
dc.contributor.authorAltun, Şehmus
dc.contributor.authorErtuğrul, Ömer Faruk
dc.date.accessioned2019-06-21T09:17:18Z
dc.date.available2019-06-21T09:17:18Z
dc.date.issued2017-11-01en_US
dc.departmentBatman Üniversitesi Mühendislik - Mimarlık Fakültesi Makine Mühendisliği Bölümüen_US
dc.departmentBatman Üniversitesi Mühendislik - Mimarlık Fakültesi Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractIn this study, a novel approach was proposed based on extreme learning machine (ELM) for developing correlations in order to calculate higher heating values (HHVs, kj/kg) of waste frying oils from their physical properties such as density (ρ, kg/m 3 ) and kinematic viscosity (v, mm 2 /s) values. These values can easily be determined by using laboratory equipment. For developing the correlations, an experimental dataset from the literature covering 35 samples was collected to be employed in the training and validation steps. The obtained optimum parameters of artificial neural network in the training stage by ELM were employed to develop new correlations. The HHVs calculated by using density-based correlation (HHV = 50823.183 − 12.34095ρ) showed the mean absolute and relative errors of 145.8048 kJ/kg and 0.3695 %, respectively. In the case of the viscosity-based correlation (HHV = 40172.85 − 17.93615v), they were found as 129.04 kJ/kg and 0.327 %, respectively. Additionally, new correlations were performed better than those available in the literature and those obtained by other machine learning methods; therefore, it is highly suggested that the proposed approach can be used for developing new correlations.en_US
dc.identifier.citationErtuğrul, Ö F., Altun, Ş. (2016). Developing correlations by extreme learning machine for calculating higher heating values of waste frying oils from their physical properties. Neural Computing and Applications, 28(11), pp. 3145-3152. https://doi.org/10.1007/s00521-016-2233-8en_US
dc.identifier.endpage3152en_US
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.issue11en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage3145en_US
dc.identifier.urihttps://doi.org/10.1007/s00521-016-2233-8
dc.identifier.urihttps://hdl.handle.net/20.500.12402/2093
dc.identifier.volume28en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Natureen_US
dc.relation.isversionof10.1007/s00521-016-2233-8en_US
dc.relation.journalNeural Computing and Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.subjectExtreme Learning Machineen_US
dc.subjectHigher Heating Valueen_US
dc.subjectMathematical Modelingen_US
dc.subjectWaste Frying Oilsen_US
dc.titleDeveloping correlations by extreme learning machine for calculating higher heating values of waste frying oils from their physical propertiesen_US
dc.typeArticleen_US

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