Full Text:   <3670>

CLC number: TK421.24

On-line Access: 

Received: 2003-10-08

Revision Accepted: 2003-12-18

Crosschecked: 0000-00-00

Cited: 8

Clicked: 7442

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
1. Reference List
Open peer comments

Journal of Zhejiang University SCIENCE A 2004 Vol.5 No.8 P.960-965

http://doi.org/10.1631/jzus.2004.0960


Study of CNG/diesel dual fuel engine's emissions by means of RBF neural network


Author(s):  LIU Zhen-tao, FEI Shao-mei

Affiliation(s):  College of Mechanical and Energy Engineering, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   liuzt@zju.edu.cn

Key Words:  Dual fuel engine, Emission performance, RBF neural network


Share this article to: More

LIU Zhen-tao, FEI Shao-mei. Study of CNG/diesel dual fuel engine's emissions by means of RBF neural network[J]. Journal of Zhejiang University Science A, 2004, 5(8): 960-965.

@article{title="Study of CNG/diesel dual fuel engine's emissions by means of RBF neural network",
author="LIU Zhen-tao, FEI Shao-mei",
journal="Journal of Zhejiang University Science A",
volume="5",
number="8",
pages="960-965",
year="2004",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2004.0960"
}

%0 Journal Article
%T Study of CNG/diesel dual fuel engine's emissions by means of RBF neural network
%A LIU Zhen-tao
%A FEI Shao-mei
%J Journal of Zhejiang University SCIENCE A
%V 5
%N 8
%P 960-965
%@ 1869-1951
%D 2004
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2004.0960

TY - JOUR
T1 - Study of CNG/diesel dual fuel engine's emissions by means of RBF neural network
A1 - LIU Zhen-tao
A1 - FEI Shao-mei
J0 - Journal of Zhejiang University Science A
VL - 5
IS - 8
SP - 960
EP - 965
%@ 1869-1951
Y1 - 2004
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2004.0960


Abstract: 
Great efforts have been made to resolve the serious environmental pollution and inevitable declining of energy resources. A review of Chinese fuel reserves and engine technology showed that compressed natural gas (CNG)/diesel dual fuel engine (DFE) was one of the best solutions for the above problems at present. In order to study and improve the emission per-formance of CNG/diesel DFE, an emission model for DFE based on radial basis function (RBF) neural network was developed which was a black-box input-output training data model not require priori knowledge. The RBF centers and the connected weights could be selected automatically according to the distribution of the training data in input-output space and the given approximating error. Studies showed that the predicted results accorded well with the experimental data over a large range of operating conditions from low load to high load. The developed emissions model based on the RBF neural network could be used to successfully predict and optimize the emissions performance of DFE. And the effect of the DFE main performance parameters, such as rotation speed, load, pilot quantity and injection timing, were also predicted by means of this model. In resumé, an emission prediction model for CNG/diesel DFE based on RBF neural network was built for analyzing the effect of the main performance parameters on the CO, NOx emissions of DFE. The predicted results agreed quite well with the traditional emissions model, which indicated that the model had certain application value, although it still has some limitations, because of its high dependence on the quantity of the experimental sample data.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1] Fei, S.M., Liu, Z.T., Yan, Z.D., 2003. Knock prediction for dual fuel engines by using a simplified combustion model. Journal of Zhejiang University SCIENCE, 4(5):591-594.

[2] Ilkivová, M.R., Ilkiá, B.R., Neuschl, T., 2002. Comparison of a linear and nonlinear approach to engine misfire detection. Control Engineering Practice, 10:1141-1146.

[3] Kaoru, I., Youji, I., Hajime, M., 2003. Image restoration using the RBF network with variable regularization parameters. Neurocomputing, 50:177-191.

[4] Korres, D.M., Anastopoulos, G., Lois, E., Alexandridis, A., Sarimveis, H., Bafas, D., 2002. A neural network approach to the prediction of diesel fuel lubricity. Fuel, 81:1243-1250.

[5] Liu, Z.T., 2000. A Study of Combustion and Control Model of Natural Gas-Diesel Fuel Engine by Means of Neural Network. Ph.D thesis, Zhejiang University (in Chinese).

[6] Omatu, S., Khalid, M., 1996. Neuro-control and its Applications. Springer-Verlag, London.

[7] Yan, Z.D., Kriam, G.A., 1992. A predictive model for dual fuel D. I diesel engine performance and emission. ASME, 27:33-39.

[8] Yan, Z.D., Zhou, C.G., SU, S.C., Liu, Z.T., Wang, X.Z., 2003. Application of neural network in the study of combustion rate of natural gas/diesel dual fuel engine. Journal of Zhejiang University SCIENCE, 4(2):170-174.

[9] Zhou, B., Tan, D.M., Wei, D.Y., 2001. Prediction of the Emissions from internal combustion engine using a neural network. CSICE, 4(2):361-364.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Nurgemar Satheesh@Canara Engineering College<satheeshnurgemar@gmail.com>

2017-06-14 15:20:16

A good paper to study on RBFNN modeling.

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE