Data on artificial neural network and response surface methodology analysis of biodiesel production

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dc.contributor.authorAyoola AA
dc.contributor.authorHymore FK
dc.contributor.authorOmonhinmin CA
dc.contributor.authorBabalola PO
dc.contributor.authorBolujo EO
dc.contributor.authorAdeyemi GA
dc.contributor.authorBabalola R
dc.contributor.authorOlafadehan OA
dc.date.accessioned2022-07-26T12:18:19Z
dc.date.available2022-07-26T12:18:19Z
dc.date.issued2020
dc.descriptionData in Brief
dc.description.abstractThe biodiesel production from waste soybean oil (using NaOH and KOH catalysts independently) was investigated in this study. The use of optimization tools (artificial neural network, ANN, and response surface methodology, RSM) for the modelling of the relationship between biodiesel yield and process parameters was carried out. The variables employed in the experimental design of biodiesel yields were methanol-oil mole ratio (6 – 12), catalyst concentration (0.7 – 1.7 wt/wt%), reaction temperature (48 – 62°C) and reaction time (50 – 90 min). Also, the usefulness of both the RSM and ANN tools in the accurate prediction of the regression models were revealed, with values of R-sq being 0.93 and 0.98 for RSM and ANN respectively.
dc.identifier.citation10.1016/j.dib.2020.105726
dc.identifier.issn2352-3409
dc.identifier.urihttps://nerd.ethesis.ng/handle/123456789/297
dc.language.isoen
dc.subjectANN
dc.subjectBiodiesel
dc.subjectKOH
dc.subjectNaOH
dc.subjectRSM
dc.subjectWaste soybean oil
dc.titleData on artificial neural network and response surface methodology analysis of biodiesel production
dc.typeArticle
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