Methanolysis of Africa Pear Seed Oil via Thermally Activated Empty Palm Fruit Bunch Ash (TAEPFBA) Catalyst, ANN Prediction and Sensitivity Analysis

Umeagukwu, O. E. and Onukwuli, O. D. and Ude, C. N. and Asadu, C. O. (2022) Methanolysis of Africa Pear Seed Oil via Thermally Activated Empty Palm Fruit Bunch Ash (TAEPFBA) Catalyst, ANN Prediction and Sensitivity Analysis. Journal of Engineering Research and Reports, 23 (12). pp. 87-103. ISSN 2582-2926

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Abstract

This study investigated the catalytic effect of thermal modified empty palm fruit bunch ash (TAEPFBA) on the transesterification reaction of Africa pear seed oil (APSO) with optimization of the process variable, and relative contribution of each variable ascertained. The TAEPFBA was synthesized from empty palm fruit bunches while the APSO was extracted via solvent extraction. The oil and catalyst were characterized using American Standard for Testing of Material (ASTM) and analytical tools. The biodiesel was produced at optimal condition and the yield of biodiesel was predicted with artificial neural network (ANN). The methyl ester produced was characterized and compared with standard properties for its application. The results showed that the oil and catalyst had comparable properties with other oils and catalysts that have been used for transesterification. The optimal conditions for transesterification reaction are temperature of 60oC, reaction time of 3hrs, 10:1 methanol/oil molar ratio, 3wt % catalyst dosage and agitation speed of 300rpm with biodiesel yield of 75%. The biodiesel produced was within the acceptable standard. The ANN predicted the yield of biodiesel with a mean square error (MSE) value of 0.0214 less than 0.05 level of significant. The contributive impact of each process variable shows that the temperature has the highest relative impact on the yield of biodiesel while the methanol/oil molar ratio has the least contribution The overall result shows that the catalyst (TAEPFBA) has the potential to convert the APSO to alkyl ester and the yield can be predicted with ANN tool.

Item Type: Article
Subjects: Opene Prints > Engineering
Depositing User: Managing Editor
Date Deposited: 07 Feb 2023 08:10
Last Modified: 29 Jul 2024 10:09
URI: http://geographical.go2journals.com/id/eprint/1182

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