Prediction of characteristic properties of crude oil blending with ANN

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dc.authoridYuceer, Mehmet/0000-0002-2648-3931
dc.authoridGoz, Eda/0000-0002-3111-9042
dc.authoridAKYAZI, HABIB/0000-0002-3512-0438
dc.authorwosidYuceer, Mehmet/E-5110-2012
dc.authorwosidGoz, Eda/AAH-3388-2020
dc.contributor.authorKaradurmus, Erdal
dc.contributor.authorAkyazi, Habib
dc.contributor.authorGoz, Eda
dc.contributor.authorYuceer, Mehmet
dc.date.accessioned2024-08-04T20:44:06Z
dc.date.available2024-08-04T20:44:06Z
dc.date.issued2018
dc.departmentİnönü Üniversitesien_US
dc.description.abstractMineral oil is one of the most important materials on earth and it is used widely for its several features. Mineral oils derived from petroleum products are commonly used to decrease the friction effects in machine parts and, thus, they both prevent wear/overheating and facilitate power transmission. In this study, various binary mixtures of various base oils (SN-80, SN-100, SN-150, SN-50, SN-500) were prepared at different volumetric ratios. Kinematic viscosity (at 40 degrees C and 100 degrees C), viscosity index, flash point, pour point, and density (at 20 degrees C) measurements were performed for characterization of the prepared mixtures. These values were modeled by an artificial neural network (ANN) and the model was tested with root mean squared error (RMSE), mean absolute percentage error (MAPE, %), and regression coefficient (R) values. A higher value of correlation coefficient and smaller values of MAPE and RMSE indicate that the model performs better. For predicting kinematic viscosity at 40 degrees C, correlation coefficients were calculated for training and testing the network as 0.9999 and 0.9995, respectively. Respective MAPE values were determined as 1.011% and 1.8771%. [GRAPHICS] .en_US
dc.identifier.doi10.1080/01932691.2017.1391702
dc.identifier.endpage1243en_US
dc.identifier.issn0193-2691
dc.identifier.issn1532-2351
dc.identifier.issue9en_US
dc.identifier.scopus2-s2.0-85034045408en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage1236en_US
dc.identifier.urihttps://doi.org/10.1080/01932691.2017.1391702
dc.identifier.urihttps://hdl.handle.net/11616/98028
dc.identifier.volume39en_US
dc.identifier.wosWOS:000441655600002en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTaylor & Francis Incen_US
dc.relation.ispartofJournal of Dispersion Science and Technologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectANNen_US
dc.subjectcrude oil blendingen_US
dc.titlePrediction of characteristic properties of crude oil blending with ANNen_US
dc.typeArticleen_US

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