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Author Spotlight: Employing Green-Chemistry Principles for Safe and Sustainable Synthesis of Biodiesels

Author Spotlight: Employing Green-Chemistry Principles for Safe and Sustainable Synthesis of Biodiesels

Published on: April 19, 2024

Enhancing Engine Performance and Sustainability: Gold Nanoparticles and Machine Learning for Biodiesel Optimization

Amith Gadagi¹, Sneha Bandekar², Santhosh Paramasivam³

¹Department of Mechanical Engineering, KLE Technological University's Dr. M. S. Sheshgiri College of Engineering and Technology, Belagavi 590008, India.

|October 20, 2025

Summary

This study enhanced biodiesel with gold nanoparticles (AuNPs) for better engine efficiency. Machine learning accurately predicted performance, showing AuNPs improve fuel and reduce consumption in compression ignition engines.

Area of Science:

Energy Science
Materials Science
Computational Science

Background:

Nanotechnology and machine learning are revolutionizing energy systems.
Biodiesel optimization is key for sustainable compression ignition (CI) engines.
Advanced nanomaterials like gold nanoparticles (AuNPs) offer potential for performance enhancement.

Purpose of the Study:

To develop and assess a novel biodiesel blend from waste cooking and Simarouba oils, enhanced with AuNPs.
To utilize machine learning for predicting and optimizing CI engine performance with the novel biodiesel.
To investigate the impact of AuNPs on biodiesel properties and engine performance metrics.

Main Methods:

Synthesized AuNPs from plant extract and characterized using UV-Vis spectrophotometry.
Prepared and tested various biodiesel blends (B20-B80) in a single-cylinder CI engine.
Employed an Extreme Gradient Boosting (XGBoost) model for performance prediction.

Main Results:

AuNP-enhanced biodiesel blends significantly improved brake thermal efficiency (BTE) by up to 6.57% and reduced brake specific fuel consumption (BSFC) by up to 9.17%.
The XGBoost model accurately predicted BTE and BSFC with minimal errors (4.17% and 3.53%, respectively).
Optimized compression ratios and loads demonstrated enhanced engine performance with AuNP-biodiesel.

Conclusions:

AuNP-enhanced biodiesel blends offer a sustainable solution for improved CI engine performance and fuel efficiency.
Machine learning, specifically XGBoost, provides a reliable and cost-effective tool for predicting and optimizing biofuel performance.
This integrated approach accelerates the development of high-performance biofuels and advances AI applications in sustainable energy systems.

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