DEVELOPMENT OF MARINE ENGINE PERFORMANCE PREDICTION MODEL THROUGH NEURAL NETWORK APPROACH - AZAMIN, EZZATUL; NOOR, MOHD (Universiti Malaysia Terengganu Journal of Undergraduate Research, 2021)
The prediction and control of marine diesel engine performance and emission rates is not an easy task in real time. Comprehensive engine performance testing for entire operating conditions is extremely costly and time consuming. Therefore, the option of using a computer model can be used to determine those parameters. This work is concerned with the modeling of artificial neural networks in predicting the performance parameters of marine diesel engines such as torque, power, fuel consumption, efficiency and exhaust emission gases. Input data were obtained from engine tests in the laboratory operated with palm biodiesel and running at various speeds and loads. The predicted results have been validated by comparing the output values of the model with the experimental data. The results show that the prediction model using neural network gives good agreement to the experimental results which yield higher correlation coefficient of 0.98194 and lower mean square error of 0.0026809. This study proves that a trained neural network model is capable to determine the performance of marine diesel engines in the accepted range.