Biodiesel production from Calophyllum inophyllum-Ceiba pentandra oil mixture: Optimization and characterization. (10th May 2019)

• Record Type: Journal Article
• Authors: Ong, Hwai Chyuan; Milano, Jassinnee; Silitonga, Arridina Susan; Hassan, Masjuki Haji; Shamsuddin, Abd Halim; Wang, Chin-Tsan; Indra Mahlia, Teuku Meurah; Siswantoro, Joko; Kusumo, Fitranto; Sutrisno, Joko
• Is Part Of: Journal of cleaner production. Volume 219(2019)
• Page Start: 183
• Page End: 198
• Publication Date: 2019-05-10
• Subjects: Biodiesel -- Alternative fuel -- Artificial neural networks -- Ant colony optimization -- Kinetics study -- Renewable energy; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2019.02.048
• Languages: English
• ISSNs: 0959-6526

Abstract

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. Graphical abstract: Image 1 Highlights: CICPO is a potential feedstock for biodiesel production. ANN-ACO is a reliable tool to optimize the transesterification process variables. Methanol-to-oil molar ratio has the most significant effect on the CICPME yield. The optimum CICPME yield predicted by the ANN-ACO model is 95.87%. The optimum CICPME yield obtained from independent experiments is 95.18%.