Optimization of Performance, Emissions, and Vibration in a Hydrogen-Diesel Dual-Fuel Engine Using Response Surface Methodology

Abstract

Background: Dual-fuel diesel engines using hydrogen as a secondary fuel source are a promising technology for reducing emissions while maintaining engine performance. However, optimizing these engines for all three aspects (performance, emissions, and vibration) simultaneously presents a challenge. Objective: This study aimed to address this challenge by employing Response Surface Methodology, a statistical technique used to optimize multi-variable processes. The goal was to find the ideal combination of engine load, hydrogen flow rate, and compression ratio that would maximize Brake Thermal Efficiency while minimizing Brake-Specific Fuel Consumption, Nitrogen Oxide emissions, and engine vibration. Method: A Box-Behnken design, a specific type of design optimization with three factors and three levels, was employed. The experiment evaluated the impact of three key factors: engine load (ranging from 0 - 12 kg), hydrogen flow rate (0-15 L/min), and compression ratio (16 to 18:1). The effects of these factors on performance, emissions, and vibration were measured. Results: The results revealed a trade-off between achieving optimal performance and minimizing emissions. The highest Brake Thermal Efficiency and lowest Brake-Specific Fuel Consumption were achieved at a high compression ratio (18:1), maximum hydrogen flow rate (15 L/min), and under full engine load (12 kg), corresponding to a brake power of 3.5 kW. However, these conditions also resulted in higher NOx emissions and vibration levels. Conversely, minimizing NOx and vibration occurred at a lower compression ratio (16:1), with the same maximum hydrogen flow rate (15 L/min), but at a significantly reduced engine load (3 kg), resulting in a much lower brake power of 0.875 kW. Conclusion: These findings highlight the complex relationship between performance, emissions, and vibration in a hydrogen-diesel dual-fuel engine optimized using Response Surface Methodology. While optimal conditions were identified for specific goals, achieving all desired characteristics simultaneously across the entire operating range remains a challenge.