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Integrated Machine Learning Framework-Based Optimization of Performance and Emissions of Nanomaterial—Integrated Biofuel Engine

Author

Listed:
  • Sooraj Mohan

    (Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India)

  • K. Ashwini

    (Department of Artificial Intelligence and Machine Learning, Shri Madhwa Vadiraja Institute of Technology and Management, Bantakal, Udupi 574115, India)

  • Ranjan Kumar Ghadai

    (Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India)

  • Akash Nag

    (Faculty of Mechanical Engineering, VŠB—Technical University of Ostrava, 70800 Ostrava, Czech Republic)

  • Jana Petrů

    (Faculty of Mechanical Engineering, VŠB—Technical University of Ostrava, 70800 Ostrava, Czech Republic)

  • P. Dinesha

    (Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India)

Abstract

This study examines the effects of injection timing and cerium oxide (CeO 2 ) nanoparticle (NP) size on NOx emissions and brake thermal efficiency (BTE) in a compression ignition engine, contributing to Sustainable Development Goals 7 and 13. Experiments were conducted at four load conditions (25–100%) using NP sizes of 10 nm, 30 nm, and 80 nm. An artificial neural network integrated with multi-objective particle swarm optimization (ANN-PSO) was employed to identify optimal operating parameters. The optimized configurations improved BTE and reduced NOx emissions across all loads; for example, at 75% load, BTE increased from 30.38% (average) to 32.13% (optimum), while simultaneously reducing the NOx emissions from 1322 ppm (average) to 1272 ppm (optimum). Analysis of variance (ANOVA) confirmed load as the most significant factor ( p < 0.001), followed by injection timing and NP size. The model predictions closely matched experimental results, validating the optimization approach. The optimization suggests an interpolated optimal NP size of approximately 45 nm, highlighting the potential for further exploration. This integrated experimental and computational approach offers a promising framework for improving combustion efficiency and reducing emissions, thereby advancing cleaner and more sustainable fuel technologies.

Suggested Citation

  • Sooraj Mohan & K. Ashwini & Ranjan Kumar Ghadai & Akash Nag & Jana Petrů & P. Dinesha, 2025. "Integrated Machine Learning Framework-Based Optimization of Performance and Emissions of Nanomaterial—Integrated Biofuel Engine," Sustainability, MDPI, vol. 17(20), pages 1-16, October.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:20:p:9004-:d:1768822
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