Hadi Mabadi Rahimi - Department of Mechanical Engineering, Ayatollah Amoli Branch, Islamic Azad University, Amol, Iran
Seyed Ali Jazayeri - Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran
Mojtaba Ebrahimi - Department of Mechanical Engineering, Ayatollah Amoli Branch, Islamic Azad University, Amol, Iran

The present study seeks to conduct the optimization of a heavy-duty diesel engine under RCCI combustion fueled with diesel fuel and natural gas enriched with hydrogen. Since NOx emission is one of the most important concerns of using hydrogen as a sole fuel or an additive to hydrocarbon fuels in an internal combustion engine like RCCI engine, thus, the main goals of this study are to overcome the NOx challenge, enhance the RCCI combustion characteristics, and reduce the fuel consumption when the conventional hydrocarbon fuels are substituted with hydrogen. In order to conduct the optimization process, an artificial neural network coupled with the design of the experiment concept was employed to identify the RCCI combustion mathematical model and provide the required population for two optimization algorithms, namely genetic algorithm, and particle swarm optimization algorithm. The results from the optimization process show that by advancing the diesel fuel injection along with the appropriate amount of exhaust gas recirculation and nitrogen as diluents, the level of EURO VI for NOx can be met. However, the losses in the RCCI engine output power is less than ۵% meanwhile the gross indicated efficiency is over ۵۰% and the reduction in hydrocarbon fuels consumption is about ۴۰%.

RCCI combustion, Heavy-Duty Diesel Engine, Hydrogen, artificial neural network, Optimization