Prediction of Performance and Smoke Emission Using Artificial Neural Network in a Diesel Engine

Yakup Sekmen,Perihan Erduranlı,Mustafa Gölcü,Yaşar Pancar

doi:10.3390/mca11020205

Abstract

The fuel injection pressure is one of the significant operating parameters affects atomization of fuel and mixture formation; therefore, it determines the performance and emissions of a diesel engine. Increasing the fuel injection pressure decrease the particle diameter and caused the diesel fuel spray to vaporize quickly. However, with decreasing fuel particles their inertia will also decrease and for this reason fuel can not penetrate deeply into the combustion chamber. In this study, artificial neural-networks (ANNs) are used to determine the effects of injection pressure on smoke emissions and engine performance in a diesel engine. Experimental studies were used to obtain training and test data. Injection pressure was changed from 100bar to 300bar in experiment (standard injection pressure of test engine is 150bar). Injection pressure and engine speed have been used as the input layer; smoke emission, engine torque and specific fuel consumption have been used as the output layer. Two different training algorithms were studied. The best results were obtained from Levenberg-Marquardt (LM) and Scaled Conjugate gradient (SCG) algorithms with 11 neurons. However, The LM algorithm is faster than the SCG algorithm, and its error values are smaller than those of the SCGs. For the torque with LM algorithm, fraction of variance (R2) and mean absolute percentage error (MAPE) were found to be 0.9927 and 7.2108%, respectively. Similarly, for the specific fuel consumption (SFC), R2 and MAPE were calculated as 0.9872 and 6.0261%, respectively. For the torque with SCG algorithm, R2 and MAPE were found to be 0.9879 and 9.0026%, respectively. Similarly, for the specific fuel consumption (SFC), R2 and MAPE were calculated as 0.9793 and 8.7974%, respectively. So, these ANN predicted results can be considered within acceptable limits and the results show good agreement between predicted and experimental values.

Highlights

There are several factors that the engine designer considers to provide both current and future low emission levels and high performance with a good fuel economy
The selected artificial neural-networks (ANNs) model consists of one hidden layer of logarithmic sigmoid function and output layer of purelin transfer function
Two back-propagation learning algorithms are used to predict of the torque, power, specific fuel consumption, and smoke emission of diesel engine using different injection pressure and engine speed

Summary

INTRODUCTION

There are several factors that the engine designer considers to provide both current and future low emission levels and high performance with a good fuel economy. The fuel injection pressure is one of the significant operating parameters affects atomization of fuel and mixture formation; it determines the performance and emissions of a diesel engine. On the other hand, decreasing the fuel injection pressure increase the particle diameter and caused the diesel fuel spray to vaporize needs more time. A number of studies have been conducted on injection pressure and atomization to improve combustion and engine performance and to reduce exhaust emissions in diesel engines [3, 9,10,11,12,13]. Two different training algorithms are used to predict the effects of injection pressure on smoke emissions and engine performance. To obtain the best prediction values, the number of neurons was increased step-by step from 8 to 15 in a single hidden-layer

ANN APPROACH

EXPERIMENTAL APPARATUS AND PROCEDURE

RESULTS AND DISCUSSION

CONCLUSION