Biodiesel production from Calophyllum inophyllum-Ceiba pentandra oil mixture: Optimization and characterization

Ong, Hwai Chyuan and Milano, Jassinnee and Silitonga, Arridina Susan and Hassan, Masjuki Haji and Shamsuddin, Abd Halim and Wang, Chin-Tsan and Mahlia, Teuku Meurah Indra and Siswantoro, Joko and Kusumo, Fitranto and Sutrisno, Joko (2019) Biodiesel production from Calophyllum inophyllum-Ceiba pentandra oil mixture: Optimization and characterization. Journal of Cleaner Production, 219. pp. 183-198. ISSN 0959-6526

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Abstract

In this study, a novel modeling approach (artificial neural networks (ANN) and ant colony optimization (ACO)) was used to optimize the process variables for alkaline-catalyzed transesterification of CI40CP60 oil mixture (40 wt% of Calophyllum inophyllum oil mixed with 60 wt% of Ceiba pentandra oil) in order to maximize the biodiesel yield. The optimum values of the methanol-to-oil molar ratio, potassium hydroxide catalyst concentration, and reaction time predicted by the ANN-ACO model are 37%, 0.78 wt%, and 153 min, respectively, at a constant reaction temperature and stirring speed of 60 °C and 1000 rpm, respectively. The ANN-ACO model was validated by performing independent experiments to produce the CI40CP60 methyl ester (CICPME) using the optimum transesterification process variables predicted by the ANN-ACO model. There is very good agreement between the average CICPME yield determined from experiments (95.18%) and the maximum CICPME yield predicted by the ANN-ACO model (95.87%) for the same optimum values of process variables, which corresponds to a difference of 0.69%. Even though the ANN-ACO model is only implemented to optimize the transesterification of process variables in this study. It is believed that the model can be used to optimize other biodiesel production processes such as seed oil extraction and acid-catalyzed esterification for various types of biodiesels and biodiesel blends.

Item Type:	Article
Uncontrolled Keywords:	Biodiesel; Alternative fuel; Artificial neural networks; Ant colony optimization; Kinetics study; Renewable energy
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TP Chemical technology
Divisions:	Faculty of Engineering > Department of Information System
Depositing User:	Joko Siswantoro
Date Deposited:	05 Jan 2022 08:28
Last Modified:	27 Jan 2022 04:04
URI:	http://repository.ubaya.ac.id/id/eprint/40907

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