Investigation of correlation between chemical composition and properties of biodiesel using principal component analysis (PCA) and artificial neural network (ANN)

Jahirul, M. I. and Rasul, M. G. and Brown, R. J. and Senadeera, W and Hosen, M. A. and Haque, R. and Saha, S. C. and Mahlia, T. M. I. (2021) Investigation of correlation between chemical composition and properties of biodiesel using principal component analysis (PCA) and artificial neural network (ANN). Renewable Energy, 168. pp. 632-646. ISSN 0960-1481


Abstract

Biodiesel will provide a significant renewable energy source for transportation in the near future. In the present study, principal component analysis (PCA) has been used to understand the relationship between important properties of biodiesel and its chemical composition. Finally, several artificial intelligence-based models were developed to predict specific biodiesel properties based on their chemical composition. The experimental study was conducted in order to generate training data for the artificial neural network (ANN). Available (experimental) data from the literature was also employed for this modeling strategy. The analytical part of this study found a complex multi-dimensional correlation between chemical composition and biodiesel properties. Average numbers of double bonds in the chemical structure (representing the unsaturated component in biodiesel) and the poly-unsaturated component in biodiesel had a great impact on biodiesel properties. The simulation result in this study demonstrated that ANN is a useful tool for investigating the fuel properties from its chemical composition which eventually can replace the time consuming and costly experimental test.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Mechanical and Electrical Engineering (1 Jul 2013 -)
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Mechanical and Electrical Engineering (1 Jul 2013 -)
Date Deposited: 07 Jan 2021 05:46
Last Modified: 07 Jan 2021 05:46
Uncontrolled Keywords: Biodiesel, Fuel Properties, Artificial neural network (ANN), Principle component analysis (PCA)
Fields of Research (2008): 09 Engineering > 0913 Mechanical Engineering > 091305 Energy Generation, Conversion and Storage Engineering
Fields of Research (2020): 40 ENGINEERING > 4017 Mechanical engineering > 401703 Energy generation, conversion and storage (excl. chemical and electrical)
Socio-Economic Objectives (2008): B Economic Development > 85 Energy > 8505 Renewable Energy > 850501 Biofuel (Biomass) Energy
Socio-Economic Objectives (2020): 17 ENERGY > 1708 Renewable energy > 170801 Biofuel energy
Identification Number or DOI: https://doi.org/10.1016/j.renene.2020.12.078
URI: http://eprints.usq.edu.au/id/eprint/40429

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