DocumentCode :
2464236
Title :
Predication Emission of an Marine Two Stroke Diesel Engine Based on Modeling of Radial Basis Function Neural Networks
Author :
Wang, Mingyv ; Zhang, Shaojun ; Zhang, Jundong ; Ma, Qiang
Author_Institution :
Marine Eng. Coll., Dalian Maritime Univ., Dalian, China
Volume :
3
fYear :
2010
fDate :
16-17 Dec. 2010
Firstpage :
184
Lastpage :
188
Abstract :
As testing the marine large-scale low-speed two stroke engine to determine the engine performance map for different working conditions costs too much time and money. So the prediction of the marine engine exhaust emissions modelling is developed to define how the inputs affect the outputs. The marine engine exhaust emissions were measured for different engine loads conditions. Using Radial Basis Function Neural Networks (RBFNNs) model, the exhaust emissions of a marine diesel engine was predicted. According to the results, the network performance was sufficient for all emission outputs. In the network, engine speed (N), engine load (L), fuel flow rate (FFR), air mass flow rate (AMR), scavenge air pressure(SAR), maximum injection pressure (MIP), electronic parameters and environmental conditions were taken as the input parameters, and the values of emissions were used as the output parameters. The R2 values of the modeling were 0.984, and the mean % errors were smaller. However, filter smoke number (FSN) higher mean errors were obtained due to the complexity of the burning process and the measurement errors. The aim of this paper is to establish a new approach based on RBFNNs for prediction of the marine diesel engine emissios. The results showed that the values predicted by RBFNNs were parallel to the experiment.
Keywords :
combustion; diesel engines; marine vehicles; mechanical engineering computing; radial basis function networks; RBFNN; air mass flow rate; burning process; engine performance; fuel flow rate; marine engine exhaust emissions modelling; marine transportation; marine two stroke diesel engine; maximum injection pressure; predication emission; radial basis function neural networks; scavenge air pressure; Diesel engines; Fuels; Mathematical model; Rails; Temperature measurement; Training; RBF Neural Network; emission; high pressure common rail;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Systems (GCIS), 2010 Second WRI Global Congress on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-9247-3
Type :
conf
DOI :
10.1109/GCIS.2010.230
Filename :
5709352
Link To Document :
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