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 (R 2 ) 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.

Modelling A Single-Rotor Wankel Engine Performance With Artificial Neural Network At Middle Speed Range

“…R 2 is a calculation of how successful the regression line reflects the sets of actual data which differs between 0 and 1. An R 2 value close to 0 corresponds to unsuccessful prediction, while 1 indicates that the ANN model perfectly predicts the output [10]. The R 2 is represented as the eq 1 below:…”

“…Where is target and is predicted value of the ith output neuron, ̅ represents the target mean value while N is the overall data [10].…”

“…Mohd Noor et al modelled a marine diesel engine with using ANN predicting torque, power, fuel consumption and exhaust gas temperature. As a result of this study, they obtained %99 (very high) prediction performance compares to actual experimental data with a determination coefficient (R 2 ) of 0.99 [10]. Sinaga and Syahrullah investigated the ability of ANN feed-forward back propagation model to optimize and predict the performance of motorcycle fuel injection systems of gasoline.…”

“…This method is implemented to different type of internal combustion engines for analyzing various features in terms of performance, emissions etc. Therefore, ANN is used in a wide variety of studies such as modelling, prediction, optimization and calibration [9][10][11][12][13][14]. Mohd Noor et al modelled a marine diesel engine with using ANN predicting torque, power, fuel consumption and exhaust gas temperature.…”

The Application of Neural Networks for Prediction of Concentration of Harmful Components in the Exhaust Gases of Diesel Engines

“…Density, heat power and Cetane number are common for two papers [10,11]. The airflow, which is very important for the combustion process, is only considered as an input parameter in two papers [12,13]. The compression ratio is common for three studies [14][15][16].…”