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.
Original language | English |
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Article number | 13599 |
Number of pages | 1 |
Journal | Sustainability |
Volume | 15 |
Issue number | 18 |
DOIs | |
Publication status | Published - 12 Sept 2023 |
Bibliographical note
© 2023 by the authors.Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/)
Funding: The authors extend their appreciation to the Researchers supporting Project
number (RSP2023R701), King Saud University, Riyadh, Saudi Arabia
Keywords
- artificial neural network
- biodiesel
- circular economy
- plantain banana stem
- response surface methodology
- sustainable development
- waste management