Analysis of waste groundnut oil biodiesel production using response surface methodology and artificial neural network

Ayoola, A. A. and Hymore, F. K. and Omonhinmin, Conrad A. and Olawole, C. Olukunle and Fayomi, O. S. I and Babatunde, Damilola Elizabeth and Fagbiele, Omololu Oluwatobi (2019) Analysis of waste groundnut oil biodiesel production using response surface methodology and artificial neural network. Chemical Data Collections, 22.

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Abstract

Investigation on the use of KOH and NaOH catalysts for waste groundnut oil (WGO) biodiesel production, as well as the comparative adoption of response surface methodology (RSM) and artificial neural network (ANN) for the modelling of yield and process parame- ters was carried out in this research work. Box–Benkhen experimental design was adopted and the four process parameters considered were methanol-oil mole ratio (6–12), cata- lyst concentration (0.7–1.7 wt%), reaction temperature (48–62 °C) and reaction time (50–90 min). The results of this research work reveal that KOH catalyst produced higher yield of biodiesel, compared to the yield obtained from NaOH catalysed process. ANN model had 0.9241 regression coefficients ( R ) and 0.8539 correlation coefficients ( R 2 ) while the R and R 2 calculated from RSM were 0.9290 and 0.8516 for KOH catalysed transesterification pro- cess. Also, the overall regression coefficients R and correlation coefficient R 2 in the ANN model were 0.9629 and 0.9272, while the R and the correlation coefficient R 2 calculated from RSM were 0.9210 and 0.8791, for NaOH catalysed WGO biodiesel production. Hence, the results typify the robustness and superiority of ANN over RSM in predicting and solv- ing complex problems specifically in the transesterification of biodiesel, due to the larger values of R and R 2 as recorded.

Item Type:	Article
Uncontrolled Keywords:	ANN Biodiesel Transesterification Waste groundnut oil RS
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:	04 Jul 2019 10:55
Last Modified:	04 Jul 2019 10:55
URI:	http://eprints.covenantuniversity.edu.ng/id/eprint/12796

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