Prediction of Pollutant Emissions of Biomass Flames Through Digital Imaging, Contourlet Transform, and Support Vector Regression Modeling
Prediction of Pollutant Emissions of Biomass Flames Through Digital Imaging, Contourlet Transform, and Support Vector Regression Modeling
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DOI：
10.1109/tim.2015.2411999
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发表时间：
2015-03
影响因子：
5.6
通讯作者：
Nan Li;G. Lu;Xinli Li;Yong Yan
中科院分区：
文献类型：
--
作者：
Nan Li;G. Lu;Xinli Li;Yong Yan
本文提出了一种结合火焰径向成像、contourlet变换和Zernike矩（CTZM）以及最小二乘支持向量回归（LS-SVR）建模的生物质燃烧过程中NOx排放预测方法。提出了一种新的基于CTZM算法的特征提取技术。contourlet变换为火焰径向图像提供了多尺度分解，并设计了基于Zernike矩的选择算子，为图像提供了明确的结构。所得到的图像特征是一种变结构，它起源于CTZM。最后，定义了OH*、CN*、CH*和C*2四种火焰自由基图像的变量特征。通过径向基函数网络建模、SVR建模和LS-SVR建模建立自由基图像变量特征与NOx排放之间的关系。三种建模方法的比较表明，LS-SVR模型在均方根误差和平均相对误差准则方面优于其他两种方法。此外，图像特征的结构对预测模型的性能有显著影响。在生物质燃气燃烧试验台上获得的测试结果表明，所提出的技术方法对于预测NOx排放是有效的。
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.