Artificial Neural Networks for Prediction and Improvement of Efficiency and Exhaust Temperature in a CNG/Diesel Dual Fuel Engine

During the last few years a great deal of effort has been made for the reduction of pollutant emissions from direct injection diesel engines. Various solutions have been proposed, one of which is the use of gaseous fuels as a supplement for liquid diesel fuel. However, the combustion process in a dual fuel engine tends to display a complex combination of features of both compression and spark ignition engine operation. Therefore, the objective of this work is to investigate the ability of an artificial neural network model, using a back propagation learning algorithm, to predict specific fuel consumption, thermal efficiency and exhaust gas temperature of a dual fuel engine for various engine speeds and loads. The model predicted values are compared with corresponding experimental results. The comparison showed that the consistency between experimental and neural network results is achieved by a mean absolute relative error less than 2%.

Keywords:

Neural networks- CNG/Diesel engine , Dual Fuel Engine (DFE) , Specific fuel consumption , Thermal efficiency , Exhaust gas temperature