Prediction of emissions and exhaust temperature for direct injection diesel engine with emulsified fuel using ANN

Authors : Görkem KÖKKÜLÜNK, Erhan AKDOĞAN, Vezir AYHAN

Pages : 2141-2152

Doi:10.3906/elk-1202-24

View : 25 | Download : 15

Publication Date : 0000-00-00

Article Type : Research Paper

Abstract :Exhaust gases have many effects on human beings and the environment. Therefore, they must be kept under control. The International Convention for the Prevention of Pollution from Ships insert ignore into journalissuearticles values(MARPOL);, which is concerned with the prevention of marine pollution, limits the emissions according to the regulations. In Emission Control Area insert ignore into journalissuearticles values(ECA); regions, which are determined by MARPOL as ECAs, the emission rates should be controlled. Direct injection insert ignore into journalissuearticles values(DI); diesel engines are commonly used as a propulsion system on ships. The prediction and control of diesel engine emission rates is not an easy task in real time. Therefore, in this study, an artificial neural network insert ignore into journalissuearticles values(ANN); structure using the back propagation insert ignore into journalissuearticles values(BP); learning algorithm and radial basis function insert ignore into journalissuearticles values(RBF); has been developed to predict the emissions and exhaust temperature for DI diesel engines with emulsified fuel. In order to show the ANN performance, the network outputs and experimental results of the BP and RBF have been compared in this paper. The experimental results were obtained from a real diesel engine. The results showed that the emissions and exhaust temperature were estimated with a very high accuracy by means of the designed neural network structures and the RBF is more reliable than the BP.
Keywords : Neural networks, emulsified fuel, diesel engine emissions, back propagation, radial basis function