Abstract

The worldwide exploration of the ethanolysis protocol (EP) has decreased despite the multifaceted benefits of ethanol, such as lower toxicity, higher oxygen content, higher renewability, and fewer emission tail compared to methanol, and the enhanced fuel properties with improved engine characteristics of multiple-oily feedstocks (MOFs) compared to single-oily feedstocks. The study first proposed a strategy for the optimisation of ethylic biodiesel synthesis from MOFs: neem, animal fat, and jatropha oil (NFJO) on a batch reactor. The project's goals were to ensure environmental benignity and encourage the use of totally biobased products. This was made possible by the introduction of novel population based algorithms such as Driving Training-Based Optimization (DTBO) and Election-Based Optimization (EBOA), which were compared with the widely used Grey Wolf Optimizer (GWO) combined with Response Surface Methodology (RSM). The yield of NFJO ethyl ester (NFJOEE) was predicted using the RSM technique, and the ideal transesterification conditions were determined using the DTBO, EBOA, and GWO algorithms. Reaction time showed a strong linear relationship with ethylic biodiesel yield, while ethanol-to-NFJO molar ratio, catalyst dosage, and reaction temperature showed nonlinear effects. Reaction time was the most significant contributor to NFJOEE yield.The important fundamental characteristics of the fuel categories were investigated using the ASTM test procedures. The maximum NFJOEE yield (86.3%) was obtained at an ethanol/NFJO molar ratio of 5.99, KOH content of 0.915 wt.%, ethylic duration of 67.43 min, and reaction temperature of 61.55 °C. EBOA outperforms DTBO and GWO regarding iteration and computation time, converging towards a global fitness value equal to 7 for 4 s, 20 for 5 s and 985 for 34 s. The key fuel properties conformed to the standards outlined by ASTMD6751 and EN 14,214 specifications. The NFJOEE fuel processing cost is 0.9328 USD, and is comparatively lesser than that of conventional diesel. The new postulated population based algorithm models can be a prospective approach for enhancing biodiesel production from numerous MOFs and ensuring a balanced ecosystem and fulfilling enviromental benignity when adopted.

Details

Title
RSM integrated GWO, Driving Training, and Election-Based Algorithms for optimising ethylic biodiesel from ternary oil of neem, animal fat, and jatropha
Author
Samuel, Olusegun D. 1 ; Patel, G. C. Manjunath 2 ; Thomas, Likewin 3 ; Chandran, Davannendran 4 ; Paramasivam, Prabhu 5 ; Enweremadu, Christopher C. 6 

 Federal University of Petroleum Resources, Department of Mechanical Engineering, Effurun, Nigeria (GRID:grid.442533.7) (ISNI:0000 0004 0418 7888); University of South Africa, Department of Mechanical, Bioresources and Biomedical Engineering, Science Campus, Florida, South Africa (GRID:grid.412801.e) (ISNI:0000 0004 0610 3238) 
 Visvesvaraya Technological University, Department of Mechanical Engineering, PES Institute of Technology and Management, Shivamogga, India (GRID:grid.444321.4) (ISNI:0000 0004 0501 2828) 
 Visvesvaraya Technological University, Department of Artificial Intelligence and Machine Learning, PES Institute of Technology and Management, Shivamogga, India (GRID:grid.444321.4) (ISNI:0000 0004 0501 2828) 
 Universiti Teknologi PETRONAS, Department of Mechanical Engineering, Perak, Malaysia (GRID:grid.444487.f) (ISNI:0000 0004 0634 0540) 
 SIMATS, Department of Research and Innovation, Saveetha School of Engineering, Chennai, India (GRID:grid.412431.1) (ISNI:0000 0004 0444 045X); Mattu University, Department of Mechanical Engineering, College of Engineering and Technology, Mettu, Ethiopia (GRID:grid.412431.1) (ISNI:0000 0004 8496 1254) 
 University of South Africa, Department of Mechanical, Bioresources and Biomedical Engineering, Science Campus, Florida, South Africa (GRID:grid.412801.e) (ISNI:0000 0004 0610 3238) 
Pages
21289
Publication year
2024
Publication date
2024
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3103678692
Copyright
© The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.