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<p>This study investigates the impact of incorporating aluminum oxide (Al<sub>2</sub>O<sub>3</sub>) nanoparticles into biodiesel blends derived from dairy scum methyl ester (DSME) on the performance and emission characteristics of a diesel engine. A total of five fuel blends were prepared for experimental evaluation: neat diesel, DSMEB10 (10% DSME + 90% diesel), DSMEB20, and two nano-additized variants of DSMEB10 and DSMEB20 with 50 ppm and 60 ppm concentrations of Al<sub>2</sub>O<sub>3</sub> nanoparticles, respectively. The nanoparticles were dispersed using ultrasonication to ensure homogeneous mixing. Experimental findings indicate that the blend DSMEB10 + 50 ppm Al<sub>2</sub>O<sub>3</sub> achieved the highest brake thermal efficiency (BTE) of 29.1%, which is nearly equivalent to that of conventional diesel (30%). This blend also exhibited the lowest specific fuel consumption (SFC) at 0.31&#xa0;kg/kWh, owing to its calorific value being comparable to diesel, thereby enhancing fuel utilization. In terms of emissions, DSMEB100 + 50 ppm demonstrated a 40% reduction in overall pollutants compared to diesel + 50 ppm, attributed to enhanced oxygen content and improved combustion kinetics. Carbon dioxide emissions were significantly lower for DSMEB10 + 50 ppm (5.5% by volume) compared to diesel + 50 ppm (7.5%), indicating a 36.3% reduction. Furthermore, nitrogen oxide (NO<sub>x</sub>) emissions were minimized at 850 ppm for DSMEB10 + 50 ppm due to accelerated combustion and reduced high-temperature residence time, facilitated by the oxygen-rich biodiesel composition. Additionally, machine learning models were employed to predict and analyze the relationship between engine load, thermal efficiency, and emissions. Among the models tested, linear regression (LR) yielded a marginally lower mean squared error (MSE = 3.04) compared to Huber regression (MSE = 3.13), while the mean absolute error (MAE) for LR was 1.33 against 1.37 for Huber regression, suggesting LR’s slightly better predictive accuracy. These findings highlight the potential of nano-enhanced DSME biodiesel blends as a sustainable and cleaner alternative to conventional diesel, with favorable engine performance and emission profiles.</p>

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Machine learning and response surface optimization to enhance diesel engine performance using milk scum biodiesel with alumina nanoparticles

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月1日

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  • Veeranna Modi,
  • K. Sunil Kumar,
  • Bhavesh Kanabar,
  • Adarsh Rai,
  • Prasad B. Rampure,
  • Ali E. Anqi,
  • Ali A. Rajhi,
  • Sagr Alamri,
  • A. Bhowmik,
  • Jasmina Lozanović

摘要

This study investigates the impact of incorporating aluminum oxide (Al2O3) nanoparticles into biodiesel blends derived from dairy scum methyl ester (DSME) on the performance and emission characteristics of a diesel engine. A total of five fuel blends were prepared for experimental evaluation: neat diesel, DSMEB10 (10% DSME + 90% diesel), DSMEB20, and two nano-additized variants of DSMEB10 and DSMEB20 with 50 ppm and 60 ppm concentrations of Al2O3 nanoparticles, respectively. The nanoparticles were dispersed using ultrasonication to ensure homogeneous mixing. Experimental findings indicate that the blend DSMEB10 + 50 ppm Al2O3 achieved the highest brake thermal efficiency (BTE) of 29.1%, which is nearly equivalent to that of conventional diesel (30%). This blend also exhibited the lowest specific fuel consumption (SFC) at 0.31 kg/kWh, owing to its calorific value being comparable to diesel, thereby enhancing fuel utilization. In terms of emissions, DSMEB100 + 50 ppm demonstrated a 40% reduction in overall pollutants compared to diesel + 50 ppm, attributed to enhanced oxygen content and improved combustion kinetics. Carbon dioxide emissions were significantly lower for DSMEB10 + 50 ppm (5.5% by volume) compared to diesel + 50 ppm (7.5%), indicating a 36.3% reduction. Furthermore, nitrogen oxide (NOx) emissions were minimized at 850 ppm for DSMEB10 + 50 ppm due to accelerated combustion and reduced high-temperature residence time, facilitated by the oxygen-rich biodiesel composition. Additionally, machine learning models were employed to predict and analyze the relationship between engine load, thermal efficiency, and emissions. Among the models tested, linear regression (LR) yielded a marginally lower mean squared error (MSE = 3.04) compared to Huber regression (MSE = 3.13), while the mean absolute error (MAE) for LR was 1.33 against 1.37 for Huber regression, suggesting LR’s slightly better predictive accuracy. These findings highlight the potential of nano-enhanced DSME biodiesel blends as a sustainable and cleaner alternative to conventional diesel, with favorable engine performance and emission profiles.