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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.