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The engine performance (brake thermal efficiency: BTE and brake-specific fuel consumption: BSFC) and emission characteristics (carbon monoxide: CO, nitrogen oxide: NOx, unburnt hydrocarbon: UHC) were reliant on engine parameters such as engine load (EL), blend type (BT), injection pressure (IP), and compression ratio (CR). Optimizing these parameters helps improve engine efficiency and reduce pollutants released into the atmosphere. Optimization is often complex due to conflicting requirements such as maximizing BTE and minimizing BSFC, CO, NOx, and UHC. Empirical equations with a better coefficient of determination were used as an objective function for optimization. No universal algorithm has been defined yet to produce the best-optimized conditions satisfying all applications. Six meta-heuristic search algorithms, including the JAYA Algorithm, Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), Teaching–Learning-Based Optimization (TLBO), Election Optimization Algorithm (EBOA), and Driving Training Optimization (DTBO), were used to conduct the optimization task. A parametric study was undertaken to tune algorithm-specific and common parameters before optimization. The algorithm performances were evaluated for global desirability value and computation time. Six cases were considered with equal weight fraction for five responses (1/5 = 0.2) corresponding to case 1, cases 2–6 assigned with maximum weight fraction (0.6) for individual output with the least weight for the rest (0.1) towards BTE, BSFC, CO, UHC, and NOx. DTBO and EBOA converged to a global fitness function value of 0.94, with DTBO being more computationally efficient than EBOA. The conditions determined by DTBO and EBOA correspond to case 2 (maximum weight fraction (i.e. 0.6) for BTE, with a minimum weight fraction of 0.1 maintained for BSFC, CO, UHC, and NOx) and are recommended as optimal ones that satisfy all outputs resulting in an average absolute percent deviation in prediction equal to 4.68% experimentally. Ag2O nanoparticles in biodiesel, fuelled by diesel engines, were found to increase by 2.77% for BTE and decrease by 5.41% for BSFC, 4.72% for CO, 2.53% for NOx, and 4.20% for UHC, respectively.

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Optimization of Engine Performance and Emission Characteristics

  • 章节

  • 发表日期：

    2025年

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  • Manjunath Patel G. C.,
  • Ajith B. S.,
  • Jagadish,
  • Arun Kumar Shettigar,
  • Olusegun David Samuel

摘要

The engine performance (brake thermal efficiency: BTE and brake-specific fuel consumption: BSFC) and emission characteristics (carbon monoxide: CO, nitrogen oxide: NOx, unburnt hydrocarbon: UHC) were reliant on engine parameters such as engine load (EL), blend type (BT), injection pressure (IP), and compression ratio (CR). Optimizing these parameters helps improve engine efficiency and reduce pollutants released into the atmosphere. Optimization is often complex due to conflicting requirements such as maximizing BTE and minimizing BSFC, CO, NOx, and UHC. Empirical equations with a better coefficient of determination were used as an objective function for optimization. No universal algorithm has been defined yet to produce the best-optimized conditions satisfying all applications. Six meta-heuristic search algorithms, including the JAYA Algorithm, Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), Teaching–Learning-Based Optimization (TLBO), Election Optimization Algorithm (EBOA), and Driving Training Optimization (DTBO), were used to conduct the optimization task. A parametric study was undertaken to tune algorithm-specific and common parameters before optimization. The algorithm performances were evaluated for global desirability value and computation time. Six cases were considered with equal weight fraction for five responses (1/5 = 0.2) corresponding to case 1, cases 2–6 assigned with maximum weight fraction (0.6) for individual output with the least weight for the rest (0.1) towards BTE, BSFC, CO, UHC, and NOx. DTBO and EBOA converged to a global fitness function value of 0.94, with DTBO being more computationally efficient than EBOA. The conditions determined by DTBO and EBOA correspond to case 2 (maximum weight fraction (i.e. 0.6) for BTE, with a minimum weight fraction of 0.1 maintained for BSFC, CO, UHC, and NOx) and are recommended as optimal ones that satisfy all outputs resulting in an average absolute percent deviation in prediction equal to 4.68% experimentally. Ag2O nanoparticles in biodiesel, fuelled by diesel engines, were found to increase by 2.77% for BTE and decrease by 5.41% for BSFC, 4.72% for CO, 2.53% for NOx, and 4.20% for UHC, respectively.