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<p>The growing demand for sustainable energy and stringent emission regulations necessitate the development of cleaner alternative fuels for diesel engines. This study investigates the performance and emission characteristics of a binary biodiesel blend derived from <i>Garcinia gummi-gutta</i> (GGG) and <i>Garcinia indica</i> (GI) doped with CaO·Al₂O₃ nanoparticles (NPs) in a compression-ignition engine. Biodiesel is produced via microwave-assisted transesterification, with a yield of 98.9%, and is characterized according to ASTM standards. A Central Composite Design (CCD) is employed to examine the effects of blend ratio (10–30%), nanoparticle concentration (60–180&#xa0;ppm), compression ratio (14–18), and engine load (40–100%) on brake thermal efficiency (BTE), brake specific fuel consumption (BSFC), and emissions (CO, UHC, and NOx). A hybrid multi-response optimization framework integrating the Desirability Function Approach (DFA), the Grey Wolf Optimizer (GWO), and the Starfish Optimization Algorithm (SFOA) is implemented to determine optimal engine conditions. Results indicate that nanoparticle doping enhances combustion efficiency by improving catalytic activity and oxygen availability. The optimal single-objective conditions yield a maximum BTE of 34.8%, minimum BSFC of 0.172&#xa0;kg/kW·h, and reduced emissions (CO: 0.031 vol.%, UHC: 13.82&#xa0;ppm, NOx: 92&#xa0;ppm). Multiobjective optimization yields a composite desirability value of 0.938, and experimental validation confirms its predictive accuracy, with an average absolute deviation of 5.85%. The addition of CaO.Al₂O₃ NPs increases the BTE by 11.86% and reduce BSFC, CO, and UHC by 1.72%, 38.89%, and 26.80%, respectively. However, NOx emissions increase slightly by 7.49%, attributed to improved combustion behaviour resulting from higher oxygen content, air–fuel ratio, and calorific value. The study demonstrates that GGG-GI biodiesel blends doped with CaO·Al₂O₃ nanoparticles, combined with a hybrid statistical-AI optimization framework, offer a viable pathway to enhance diesel engine efficiency and reduce emissions, supporting sustainable biofuel deployment.</p>

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Enhancing diesel engine performance and emissions with CaO.Al₂O₃-doped Garcinia biodiesel blends using a hybrid statistical-AI framework

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月22日

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  • Ajith BS,
  • Manjunath Patel GC,
  • Selçuk Sarıkoç,
  • Olusegun D. Samuel,
  • Manjunath Shettar

摘要

The growing demand for sustainable energy and stringent emission regulations necessitate the development of cleaner alternative fuels for diesel engines. This study investigates the performance and emission characteristics of a binary biodiesel blend derived from Garcinia gummi-gutta (GGG) and Garcinia indica (GI) doped with CaO·Al₂O₃ nanoparticles (NPs) in a compression-ignition engine. Biodiesel is produced via microwave-assisted transesterification, with a yield of 98.9%, and is characterized according to ASTM standards. A Central Composite Design (CCD) is employed to examine the effects of blend ratio (10–30%), nanoparticle concentration (60–180 ppm), compression ratio (14–18), and engine load (40–100%) on brake thermal efficiency (BTE), brake specific fuel consumption (BSFC), and emissions (CO, UHC, and NOx). A hybrid multi-response optimization framework integrating the Desirability Function Approach (DFA), the Grey Wolf Optimizer (GWO), and the Starfish Optimization Algorithm (SFOA) is implemented to determine optimal engine conditions. Results indicate that nanoparticle doping enhances combustion efficiency by improving catalytic activity and oxygen availability. The optimal single-objective conditions yield a maximum BTE of 34.8%, minimum BSFC of 0.172 kg/kW·h, and reduced emissions (CO: 0.031 vol.%, UHC: 13.82 ppm, NOx: 92 ppm). Multiobjective optimization yields a composite desirability value of 0.938, and experimental validation confirms its predictive accuracy, with an average absolute deviation of 5.85%. The addition of CaO.Al₂O₃ NPs increases the BTE by 11.86% and reduce BSFC, CO, and UHC by 1.72%, 38.89%, and 26.80%, respectively. However, NOx emissions increase slightly by 7.49%, attributed to improved combustion behaviour resulting from higher oxygen content, air–fuel ratio, and calorific value. The study demonstrates that GGG-GI biodiesel blends doped with CaO·Al₂O₃ nanoparticles, combined with a hybrid statistical-AI optimization framework, offer a viable pathway to enhance diesel engine efficiency and reduce emissions, supporting sustainable biofuel deployment.