Response surface methodology and artificial neural network analysis of crude palm kernel oil biodiesel production

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dc.contributor.authorAyoola AA
dc.contributor.authorHymore FK
dc.contributor.authorOmonhinmin CA
dc.contributor.authorBabalola PO
dc.contributor.authorFayomi OS
dc.contributor.authorOlawole OC
dc.contributor.authorOlawepo AV
dc.contributor.authorBabalola A
dc.date.accessioned2022-07-26T12:18:23Z
dc.date.available2022-07-26T12:18:23Z
dc.date.issued2020
dc.descriptionChemical Data Collections
dc.description.abstractResponse surface methodology (RSM) and Artificial neural network (ANN) analysis of crude palm kernel oil (CPKO) biodiesel production, using KOH and NaOH catalysts, were carried out in this research work. The four process parameters considered during the production process and modelling stages were 6–12 mol ratio of methanol/oil, 0.7–1.7 wt/wt% catalyst concentration, 48–62 °C reaction temperature and 50–90 min reaction time. Log sigmoid function and Levenberg marquardt technique were adopted in ANN while Box-Benkhen method was utilised for RSM. The results revealed that KOH catalyst process produced higher yield of biodiesel (87 – 99%), compared to the yield obtained from NaOH catalysed process (79 – 91%). The regression coefficients for RSM models were 0.9324 for KOH catalysed process and 0.8935 for NaOH catalysed process, while the overall correlation coefficients for ANN models were 0.82921 for KOH catalysed process and 0.89396 for NaOH catalysed process, an implication that RSM is a better analytical tool (compare to ANN) in models formulation.
dc.identifier.citation10.1016/j.cdc.2020.100478
dc.identifier.issn2405-8300
dc.identifier.urihttps://nerd.ethesis.ng/handle/123456789/329
dc.language.isoen
dc.subjectAnn
dc.subjectBiodiesel
dc.subjectCrude palm kernel oil
dc.subjectTransesterification
dc.subjectRSM
dc.titleResponse surface methodology and artificial neural network analysis of crude palm kernel oil biodiesel production
dc.typeArticle
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