A Component Prediction Method for Flue Gas of Natural Gas Combustion Based on Nonlinear Partial Least Squares Method

Quantitative analysis for the flue gas of natural gas-fired generator is significant for energy conservation and emission reduction. The traditional partial least squares method may not deal with the nonlinear problems effectively. In the paper, a nonlinear partial least squares method with extended...

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Bibliographic Details
Published in:TheScientificWorld 2014-01, Vol.2014 (2014), p.1-5
Main Authors: Zhou, Yan, Wang, Yanxia, Li, Yaojiang, Yan, Xingyu, Cao, Hui, Yang, Sanchun
Format: Article
Language:English
Subjects:
Algorithms
Carbon dioxide
Carbon monoxide
Chemical properties
Combustion
Emissions control
Energy conservation
Flue gas
Flue gases
Identification and classification
Least squares
Least squares method
Least-Squares Analysis
Models, Chemical
Natural gas
Natural Gas - analysis
Neural networks
Power plants
Prediction models
Spectrum analysis
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Summary:Quantitative analysis for the flue gas of natural gas-fired generator is significant for energy conservation and emission reduction. The traditional partial least squares method may not deal with the nonlinear problems effectively. In the paper, a nonlinear partial least squares method with extended input based on radial basis function neural network (RBFNN) is used for components prediction of flue gas. For the proposed method, the original independent input matrix is the input of RBFNN and the outputs of hidden layer nodes of RBFNN are the extension term of the original independent input matrix. Then, the partial least squares regression is performed on the extended input matrix and the output matrix to establish the components prediction model of flue gas. A near-infrared spectral dataset of flue gas of natural gas combustion is used for estimating the effectiveness of the proposed method compared with PLS. The experiments results show that the root-mean-square errors of prediction values of the proposed method for methane, carbon monoxide, and carbon dioxide are, respectively, reduced by 4.74%, 21.76%, and 5.32% compared to those of PLS. Hence, the proposed method has higher predictive capabilities and better robustness.
ISSN:2356-6140
1537-744X
1537-744X
DOI:10.1155/2014/418674