Maximizing biodiesel yield of a non-edible chinaberry seed oil via microwave assisted transesterification process using response surface methodology and artificial neural network techniques

Rehman Akhtar; Ameer Hamza; Luqman Razzaq; Fayaz Hussain; Saad Nawaz; Umer Nawaz; Zara Mukaddas; Tahir Abbas Jauhar; A S Silitonga; C Ahamed Saleel

doi:10.1016/j.heliyon.2023.e22031

Maximizing biodiesel yield of a non-edible chinaberry seed oil via microwave assisted transesterification process using response surface methodology and artificial neural network techniques

Heliyon. 2023 Nov 8;9(11):e22031. doi: 10.1016/j.heliyon.2023.e22031. eCollection 2023 Nov.

Authors

Affiliations

¹ Department of Mechanical Engineering Technology, University of Gujrat, 50700, Pakistan.
² Modeling Evolutionary Algorithms Simulation and Artificial Intelligence, Faculty of Electrical & Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Viet Nam.
³ Department of Mechanical, Mechatronic and Manufacturing Engineering, University of Engineering & Technology, Lahore (New Campus), KSK, Sheikhupura, 39350, Pakistan.
⁴ Department of Chemistry, University of Gujrat, 50700, Pakistan.
⁵ Centre for Technology in Water and Wastewater, School of Civil and Environmental Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney, NSW, 2007, Australia.
⁶ Center of Renewable Energy, Department of Mechanical Engineering, Politeknik Negeri Medan, 20155, Medan, Indonesia.
⁷ Department of Mechanical Engineering, College of Engineering, King Khalid University, Asir, Abha, 61421, Saudi Arabia.

Abstract

In this study, the non-edible Chinaberry Seed Oil (CBO) is converted into biodiesel using microwave assisted transesterification. The objective of this effort is to maximize the biodiesel yield by optimizing the operating parameters, such as catalyst concentration, methanol-oil ratio, reaction speed, and reaction time. The designed setup provides a controlled and effective approach for turning CBO into biodiesel, resulting in encouraging yields and reduced reaction times. The experimental findings reveal the optimal parameters for the highest biodiesel yield (95 %) are a catalyst concentration of 1.5 w/w, a methanol-oil ratio of 6:1 v/v, a reaction speed of 400 RPM, and a reaction period of 3 min. The interaction of the several operating parameters on biodiesel yield has been investigated using two methodologies: Response Surface Methodology (RSM) and Artificial Neural Network (ANN). RSM provides better modeling of parameter interaction, while ANN exhibits lower comparative error when predicting biodiesel yield based on the reaction parameters. The percentage improvement in prediction of biodiesel yield by ANN is found to be 12 % as compared to RSM. This study emphasizes the merits of both the approaches for biodiesel yield optimization. Furthermore, the scaling up this microwave-assisted transesterification system for industrial biodiesel production has been proposes with focus on its economic viability and environmental effects.

Keywords: Artificial neural network; Biodiesel; Chinaberry seed oil; Response surface methodology.