Biodiesel Production from Waste Cooking Oil Using Extracted Catalyst from Plantain Banana Stem via RSM and ANN Optimization for Sustainable Development

Gulzar Ahmad, Shahid Imran, Muhammad Farooq (), Asad Naeem Shah, Zahid Anwar, Ateekh Ur Rehman and Muhammad Imran
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Gulzar Ahmad: Department of Mechanical Engineering, University of Engineering and Tech, Lahore 54890, Pakistan
Shahid Imran: Department of Mechanical Engineering, University of Engineering and Tech, Lahore 54890, Pakistan
Muhammad Farooq: Department of Mechanical Engineering, University of Engineering and Tech, Lahore 54890, Pakistan
Asad Naeem Shah: Department of Mechanical Engineering, University of Engineering and Tech, Lahore 54890, Pakistan
Zahid Anwar: Department of Mechanical Engineering, University of Engineering and Tech, Lahore 54890, Pakistan
Ateekh Ur Rehman: Department of Industrial Engineering, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
Muhammad Imran: Department of Mechanical, Biomedical and Design Engineering, College of Engineering and Physical Sciences, Aston University, Birmingham B4 7ET, UK

Sustainability, 2023, vol. 15, issue 18, 1-17

Abstract: Biodiesel is a promising sector worldwide and is experiencing significant and rapid growth. Several studies have been undertaken to utilize homogeneous base catalysts in the form of KOH to develop biodiesel in order to establish a commercially viable and sustainable biodiesel industry. This research centers around extracting potassium hydroxide (KOH) from banana trunks and employing it in the transesterification reaction to generate biodiesel from waste cooking oil (WCO). Various operational factors were analyzed for their relative impact on biodiesel output, and after optimizing the reaction parameters, a conversion rate of 95.33% was achieved while maintaining a reaction period of 2.5 h, a methanol-to-oil molar ratio of 15:1, and a catalyst quantity of 5 wt%. Response surface methodology (RSM) and artificial neural network (ANN) models were implemented to improve and optimize these reaction parameters for the purpose of obtaining the maximum biodiesel output. Consequently, remarkably higher yields of 95.33% and 95.53% were achieved by RSM and ANN, respectively, with a quite little margin of error of 0.0003%. This study showcases immense promise for the large-scale commercial production of biodiesel.

Keywords: biodiesel; plantain banana stem; sustainable development; waste management; artificial neural network; circular economy; response surface methodology (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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