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

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dc.creatorAyoola, A. A., Hymore, F. K., Omonhinmin, Conrad A., Babalola, P.O., Bolujo, E. O., Adeyemi, Gideon Adewale, Babalola, Rasheed, OLAFADEHAN, O. A.
dc.date2020
dc.date.accessioned2025-04-04T16:05:11Z
dc.descriptionThe 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 em- ployed 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 mod- els were revealed, with values of R-sq being 0.93 and 0.98 for RSM and ANN respectively.
dc.formatapplication/pdf
dc.identifierhttp://eprints.covenantuniversity.edu.ng/13391/
dc.identifier.urihttps://repository.covenantuniversity.edu.ng/handle/123456789/43504
dc.languageen
dc.publisherElsevier
dc.subjectQH301 Biology, TJ Mechanical engineering and machinery, TP Chemical technology
dc.titleData on artificial neural network and response surface methodology analysis of biodiesel production
dc.typeArticle

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