Prediction Of Marine Diesel Engine Performance By Using Artificial Neural Network Model

Abstract

This study deals with an artificial neural network (ANN) modelling of a marine diesel engine to predict the output torque, brake power, brake specific fuel consumption and exhaust gas temperature. The input data for network training was gathered from engine laboratory testing running at various engine speeds and loads. An ANN prediction model was developed based on a standard back-propagation Levenberg–Marquardt training algorithm. The performance of the model was validated by comparing the prediction data sets with the measured experiment data and output from the mathematical model. The results showed that the ANN model provided good agreement with the experimental data with a coefficient of determination (R2) of 0.99. The prediction error of the ANN model is lower than the mathematical model. The present study reveals that the artificial neural network approach can be used to predict the performance of a marine diesel engine with high accuracy.