Prediction of Pollutant Emissions of Biomass Flames Through Digital Imaging, Contourlet Transform, and Support Vector Regression Modeling

Li, Nan and Lu, Gang and Li, Xinli and Yan, Yong (2015) Prediction of Pollutant Emissions of Biomass Flames Through Digital Imaging, Contourlet Transform, and Support Vector Regression Modeling. IEEE Transactions on Instrumentation and Measurement, 64 (9). pp. 2409-2416. ISSN 0018-9456. (doi:https://doi.org/10.1109/TIM.2015.2411999) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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Official URL
http://doi.org/10.1109/TIM.2015.2411999

Abstract

This paper presents a method for the prediction of NOx emissions in a biomass combustion process through the combination of flame radical imaging, contourlet transform and Zernike moment (CTZM), and least squares support vector regression (LS-SVR) modeling. A novel feature extraction technique based on the CTZM algorithm is developed. The contourlet transform provides the multiscale decomposition for flame radical images and the selected operator based on Zernike moments is designed to provide the well-defined structure for the images. The resulted image features are a variable structure, which is originated from the CTZM. Finally, the variable features of the images of four flame radicals (OH*, CN*, CH*, and C*2) are defined. The relationship between the variable features of radical images and NOx emissions is established through radial basis function network modeling, SVR modeling, and the LS-SVR modeling. A comparison between the three modeling approaches shows that the LS-SVR model outperforms the other two methods in terms of root-mean-square error and mean relative error criteria. In addition, the structure of the image features has a significant impact on the performance of the prediction models. The test results obtained on a biomass-gas fired test rig show the effectiveness of the proposed technical approach for the prediction of NOx emissions.

Item Type: Article
Subjects: T Technology
Divisions: Faculties > Sciences > School of Engineering and Digital Arts
Faculties > Sciences > School of Engineering and Digital Arts > Instrumentation, Control and Embedded Systems
Depositing User: Tina Thompson
Date Deposited: 07 Oct 2015 11:15 UTC
Last Modified: 07 Oct 2015 11:15 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/50801 (The current URI for this page, for reference purposes)
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