An experimental-based artificial neural network performance study of common rail direct injection engine run on plastic pyrolysis oil

Abstract

ABSTRACT Artificial neural network model was constructed to analyse and evaluate the engine performance. The experiments were conducted on a diesel engine with the blend of plastic pyrolysis oil with diesel and ethanol. Three input layer with two hidden layers and five output layers were used in artificial neural network modelling. The learning algorithm called feed-forward back-propagation was applied for the hidden layer. To train the neural network, 70% of the complete data from the experimentation was selected and 30% in predicting from the neural network. The model developed for prediction has excellent agreement as observed from the correlation coefficient (R) within the range of 0.964–0.9816. Statistical analysis shows that the ANN predicted and experimental results are in close agreement with each other. Overall, it could be concluded that it is a mean to predict the virtual sensing in studying the real time with established artificial neural network architecture. In addition, common rail direct injection engine operation could give complete freedom from diesel and thereby provides energy security and sustainable of a nation.