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

Ayoola, A. A. and Hymore, F. K. and Omonhinmin, Conrad A. and Babalola, P.O. and Fayomi, O. S. I and Olawole, C. Olukunle and Olawepo, A.V and Babalola, A (2020) Response surface methodology and artificial neural network analysis of crude palm kernel oil biodiesel production. Chemical Data Collections, 28.

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Abstract

Response 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 produc- tion 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 pro- duced 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 correla- tion 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

Item Type:	Article
Uncontrolled Keywords:	Ann Biodiesel Crude palm kernel oil Transesterification RSM
Subjects:	T Technology > T Technology (General) T Technology > TJ Mechanical engineering and machinery
Divisions:	Faculty of Engineering, Science and Mathematics > School of Engineering Sciences
Depositing User:	Mrs Hannah Akinwumi
Date Deposited:	21 Oct 2020 14:59
Last Modified:	21 Oct 2020 14:59
URI:	http://eprints.covenantuniversity.edu.ng/id/eprint/13664

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