Prediction of Kinematic Viscosities of Biodiesels Derived from Edible and Non-edible Vegetable Oils by Using Artificial Neural Networks

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dc.contributor.authorEryilmaz, Tanzer
dc.contributor.authorYesilyurt, Murat Kadir
dc.contributor.authorTaner, Alper
dc.contributor.authorCelik, Sadiye Ayse
dc.date.accessioned2020-03-26T19:06:42Z
dc.date.available2020-03-26T19:06:42Z
dc.date.issued2015
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractIn the present study, the seeds named as wild mustard (Sinapis arvensis L.) and safflower (Carthamus tinctorius L.) were used as feedstocks for production of biodiesels. In order to obtain wild mustard seed oil (WMO) and safflower seed oil (SO), screw press apparatus was used. wild mustard seed oil biodiesel (WMOB) and safflower seed oil biodiesel (SOB) were produced using methanol and NaOH by transesterification process. Various properties of these biodiesels such as density (883.62-886.35 ), specific gravity (0.88442-0.88709), kinematic viscosity (5.75-4.11 ), calorific value (40.63-38.97 ), flash point (171-), water content (328.19-412.15 ), color (2.0-1.8), cloud point [5.8-, pour point [(-3.1)-(-13.1), cold filter plugging point [(-2.0)-], copper strip corrosion (1a-1a) and pH (7.831-7.037) were determined. Furthermore, kinematic viscosities of biodiesels and euro-diesel (ED) were measured at 298.15-373.15 K intervals with 1 K increments. Four different equations were used to predict the viscosities of fuels. Regression analyses were done in MATLAB program, and , correlation constants and root-mean-square error were determined. 1-7-7-3 artificial neural network (ANN) model with a back propagation learning algorithm was developed to predict the viscosities of fuels. The performance of neural network-based model was compared with the performance of viscosity prediction models using same observed data. It was found that ANN model consistently gave better predictions (0.9999 values for all fuels) compared to these models. ANN model was showed 0.34 % maximum errors. Based on the results of this study, ANNs appear to be a promising technique for predicting viscosities of biodiesels.en_US
dc.identifier.doi10.1007/s13369-015-1831-6en_US
dc.identifier.endpage3758en_US
dc.identifier.issn2193-567Xen_US
dc.identifier.issn2191-4281en_US
dc.identifier.issue12en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage3745en_US
dc.identifier.urihttps://dx.doi.org/10.1007/s13369-015-1831-6
dc.identifier.urihttps://hdl.handle.net/20.500.12395/32447
dc.identifier.volume40en_US
dc.identifier.wosWOS:000364971200030en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSPRINGER HEIDELBERGen_US
dc.relation.ispartofARABIAN JOURNAL FOR SCIENCE AND ENGINEERINGen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectWild mustard (Sinapis arvensis L.)en_US
dc.subjectSafflower (Carthamus tinctorius L.)en_US
dc.subjectFuel propertyen_US
dc.subjectKinematic viscosityen_US
dc.subjectPredictionen_US
dc.subjectArtificial neural network (ANN)en_US
dc.titlePrediction of Kinematic Viscosities of Biodiesels Derived from Edible and Non-edible Vegetable Oils by Using Artificial Neural Networksen_US
dc.typeArticleen_US

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