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CLC number: TK421+.5
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Received: 2001-10-08
Revision Accepted: 2002-01-20
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A method for predicting in-cylinder compound combustion emissions
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SU Shi-chuan, YAN Zhao-da, YUAN Guang-jie, CAO yun-hua, ZHOU Chong-guang. A method for predicting in-cylinder compound combustion emissions[J]. Journal of Zhejiang University Science A, 2002, 3(5): 543-548.
@article{title="A method for predicting in-cylinder compound combustion emissions",
author="SU Shi-chuan, YAN Zhao-da, YUAN Guang-jie, CAO yun-hua, ZHOU Chong-guang",
journal="Journal of Zhejiang University Science A",
volume="3",
number="5",
pages="543-548",
year="2002",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2002.0543"
}
%0 Journal Article
%T A method for predicting in-cylinder compound combustion emissions
%A SU Shi-chuan
%A YAN Zhao-da
%A YUAN Guang-jie
%A CAO yun-hua
%A ZHOU Chong-guang
%J Journal of Zhejiang University SCIENCE A
%V 3
%N 5
%P 543-548
%@ 1869-1951
%D 2002
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2002.0543
TY - JOUR
T1 - A method for predicting in-cylinder compound combustion emissions
A1 - SU Shi-chuan
A1 - YAN Zhao-da
A1 - YUAN Guang-jie
A1 - CAO yun-hua
A1 - ZHOU Chong-guang
J0 - Journal of Zhejiang University Science A
VL - 3
IS - 5
SP - 543
EP - 548
%@ 1869-1951
Y1 - 2002
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2002.0543
Abstract: This paper presents a method using a large steady-state engine operation data matrix to provide necessary information for successfully training a predictive network, while at the same time eliminating errors produced by the dispersive effects of the emissions measurement system. The steady-state training conditions of compound fuel allow for the correlation of time-averaged in-cylinder combustion variables to the engine-out NOx and HC emissions. The error back-propagation neural network (EBP) is then capable of learning the relationships between these variables and the measured gaseous emissions, and then interpolating between steady-state points in the matrix. This method for NOx and HC has been proved highly successful.
[1] Atkinson, C., Long, T., Hanzevack, E. 1998. Virtual sensing: an emissions prediction system for on-board diagnostics and engine control. SAE Paper 980516.
[2] Hafner, M., SchuK ler, M., Nelles, O., Isermann, R., 2000. Fast neural networks for diesel engine control design. Control Engineering Practice 8:1211-1221.
[3] Haykin, S., 1994. Neural Networks: AComprehensive Foundation.Macmillan College Publishing Company, Inc., Englewood Cliffs, New Jercy.
[4] Hornik, K., Stinchcombe, M., White, H., 1989, Neural networks. Int. J. Control, 2:359.
[5] Nelles, O., 1997. Orthonormal basis functions for nonlinear system identification with local linear model trees (LOLIMOT). Proceedings of the 11th IFAC Symposium on System Identification (SYSID). Fukuoka, Japan, p.667-672.
[6] Raina, P., 1994. Comparison of learning and generalization capabilities of the Kak and the backpropagation algorithms. Information Sciences, 81:261-274.
[7] Watanabe, S., Machida, K., Iijima, K., Tomisawa, N., 1996. A sophisticated engine control system using combustion pressure detection. SAE Paper: 960042.
[8] Widrow, B., Lehr, M.A.,1990. 30 years of adaptive neural networks: Perception, Madaline, and Back propagation, Proceedings of IEEE, 78(9):1415-1442.
[9] Yuan, G.J., 2000. Test Study the Performance of Emissions of Compound fuel Combustion in Diesel Engine. Thesis Requirement for the Degree of Master of Engineering, Zhejiang University.
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