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Explainable AI-assisted sustainable performance optimization of Nano-enhanced rapeseed oil blends in ci engines for optimized engine operation

Sumathy MuniamuthuDepartment of Mechanical Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, 600062, Tamil Nadu, IndiaK. Sunil KumarDepartment of Mechanical Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, 602105, Tamil Nadu, IndiaSundaravadivel. T.AMechanical Engineering Division, R. M. D Engineering College, 601206, Kavaraipettai, Tamil Nadu, IndiaIbrahim A. AlsayerDepartment of Chemical and Materials Engineering, College of Engineering, Northern Border University, Arar, Saudi ArabiaRamis M KDepartment of Mechanical Engineering, P. A. College of Engineering (Affiliated to Visvesvaraya Technological University, (Belagavi), Mangaluru, 574153, IndiaAbdul RazakDepartment of Mechanical Engineering, P. A. College of Engineering (Affiliated to Visvesvaraya Technological University, (Belagavi), Mangaluru, 574153, IndiaElyor BerdimurodovUniversity of Tashkent for Applied Sciences, Str. Gavhar 1 , 100149, Tashkent, UzbekistanTrmesgen EngidaComputational and Natural Sciences College, Dilla University, Dilla, Ethiopia
2026en
ABI

Annotatsiya

This study investigates the influence of advanced fuel injection timing on the performance, combustion, and emission characteristics of a compression ignition engine fueled with a rapeseed oil emulsion–pentanol blend (70D25GS5P) supplemented with 150 ppm TiO₂ nanoparticles. Experiments were conducted at injection timings of 21°, 23°, 25°, and 27° crank angle before top dead centre (CA bTDC). Among the tested conditions, 27° CA bTDC produced the highest brake thermal efficiency of 35% and the lowest brake-specific fuel consumption of 0.26 kg kWh⁻1. The corresponding peak cylinder pressure and heat release rate reached 80 bar and 50 kJ deg⁻1 CA, respectively. The lowest hydrocarbon and carbon monoxide emissions, 45 ppm and 0.07%, respectively, were obtained at 23° CA bTDC, whereas 21° CA bTDC resulted in the minimum NOx emission of 2000 ppm. To achieve an improved balance between engine efficiency and emissions, response surface methodology, analysis of variance, desirability-based optimization, and machine-learning approaches were integrated with the experimental analysis. ANOVA demonstrated the significant influence of injection timing on engine performance and emission responses. Among the evaluated machine-learning models, the Gradient Boosting Regressor showed excellent predictive capability, achieving R2 values of 0.999 for brake thermal efficiency and 0.996 for brake-specific fuel consumption. SHAP and Sobol sensitivity analyses further identified engine load as the most influential parameter governing overall engine behaviour, while injection timing exerted a dominant influence on NOx formation. The findings demonstrate that coordinated optimization of engine load and injection timing can improve the performance–emission trade-off of TiO₂-enhanced rapeseed oil emulsion–pentanol fuels, supporting more energy-efficient and sustainable compression ignition engine operation in alignment with Sustainable Development Goal 7.

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