Modelling and optimizing effect of start of injection and blend of fuels on dual fuel diesel engine performance parameters by ANN and NSGA II

The dual fuel diesel engine (DDF) is a one of the various IC engine that can used alternative fuel for power generation. Emission and fuel consumption reduction are some of the properties that use a combination of fuel mixture in dual fuel diesel engine (gas-diesel). We focused in this research to study the effect of start of injection (SOI) and blend of fuel variation in OM355 EU2 dual fuel diesel engine at two various speeds with the help of the computational fluids dynamic. The modeling of the artificial neural network (ANN) has been used to study the interaction effects of SOI and blend of fuel on the operational parameters. The non-sorted genetic algorithm (NSGA II) has been used for determining the optimized levels of the variables. The results of this study show that the RBFNN has acceptable predictions about the outputs’ variables (R^2=0.99 ,RMSE=0.01), and the optimized range of the engine function in the specific speeds has been attained with the help of the responding surface of the neural network. Besides, the optimizing capabilities of the NSGA II have provided the optimized levels of the input and output variables in the various speeds.