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This study evaluates biodiesel blends from waste cooking oil (WCO) and diesel, enhanced with cerium oxide (CeO2) and zinc oxide (ZnO) nanoparticles, for reducing emissions and improving compression ignition engine performance. Biodiesel blends were prepared via transesterification and nanoparticle suspension using ultrasonic homogenization. Engine tests showed that zinc oxide and cerium oxide nanoparticles significantly reduce emissions and enhance performance. A linear regression model, predicting brake power based on waste cooking oil, zinc oxide, and cerium oxide concentrations, achieved an R2 score of 0.95, outperforming other machine learning algorithms. A linear regression model, predicting brake power based on waste cooking oil, zinc oxide, and cerium oxide concentrations, achieved an R2 score of 0.95, outperforming other machine learning algorithms. This model effectively correlates fuel composition with engine performance, providing a reliable tool for optimizing biodiesel blends. The optimal blend contained 10% zinc oxide, indicating the prospect of nanoparticle-enhanced biodiesel for better combustion ignition engine performance and lower emissions. The study demonstrates that biodiesel blends from waste cooking oil, enhanced with cerium oxide and zinc oxide nanoparticles, can significantly reduce emissions and improve performance in compression ignition engines. The linear regression model used to predict brake power proved highly accurate, outperforming other machine learning algorithms. Notably, a biodiesel blend with 10% zinc oxide nanoparticles showed optimal results, highlighting its potential as an effective fuel alternative. This suggests further research into various nanoparticle compositions could yield even better performance and emission reduction.

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Experimental Implementation of Combustion Ignition Metrics Using Machine Learning Algorithms

  • 会议论文

  • 发表日期：

    2026年

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  • T. Charandeep Reddy,
  • Sindhu Chandra Sekharan,
  • Mokkapati Shiv Prasad Sahil,
  • Gnaneswarudu Kuna,
  • V. Praveena

摘要

This study evaluates biodiesel blends from waste cooking oil (WCO) and diesel, enhanced with cerium oxide (CeO2) and zinc oxide (ZnO) nanoparticles, for reducing emissions and improving compression ignition engine performance. Biodiesel blends were prepared via transesterification and nanoparticle suspension using ultrasonic homogenization. Engine tests showed that zinc oxide and cerium oxide nanoparticles significantly reduce emissions and enhance performance. A linear regression model, predicting brake power based on waste cooking oil, zinc oxide, and cerium oxide concentrations, achieved an R2 score of 0.95, outperforming other machine learning algorithms. A linear regression model, predicting brake power based on waste cooking oil, zinc oxide, and cerium oxide concentrations, achieved an R2 score of 0.95, outperforming other machine learning algorithms. This model effectively correlates fuel composition with engine performance, providing a reliable tool for optimizing biodiesel blends. The optimal blend contained 10% zinc oxide, indicating the prospect of nanoparticle-enhanced biodiesel for better combustion ignition engine performance and lower emissions. The study demonstrates that biodiesel blends from waste cooking oil, enhanced with cerium oxide and zinc oxide nanoparticles, can significantly reduce emissions and improve performance in compression ignition engines. The linear regression model used to predict brake power proved highly accurate, outperforming other machine learning algorithms. Notably, a biodiesel blend with 10% zinc oxide nanoparticles showed optimal results, highlighting its potential as an effective fuel alternative. This suggests further research into various nanoparticle compositions could yield even better performance and emission reduction.