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Received: 2001-10-08

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Journal of Zhejiang University SCIENCE A 2002 Vol.3 No.5 P.543-548

http://doi.org/10.1631/jzus.2002.0543


A method for predicting in-cylinder compound combustion emissions


Author(s):  SU Shi-chuan, YAN Zhao-da, YUAN Guang-jie, CAO yun-hua, ZHOU Chong-guang

Affiliation(s):  The Institute Power Machinery and Vehicular Engineering, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   ssczju@hotmail.com

Key Words:  Back-propagation neural network (EBP), Compound fuel, Emissions, Prediction


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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.

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author="SU Shi-chuan, YAN Zhao-da, YUAN Guang-jie, CAO yun-hua, ZHOU Chong-guang",
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doi="10.1631/jzus.2002.0543"
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%A YAN Zhao-da
%A YUAN Guang-jie
%A CAO yun-hua
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%J Journal of Zhejiang University SCIENCE A
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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
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SP - 543
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PB - Zhejiang University Press & Springer
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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.

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

Reference

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[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.

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[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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