Prediction of NOx emissions throughflame radical imaging and neural network based soft computing
Prediction of NOx emissions throughflame radical imaging and neural network based soft computing
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DOI：
10.1109/ist.2012.6295594
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发表时间：
2012-07
期刊：
影响因子：
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通讯作者：
Xinli Li;Duo Sun;G. Lu;J. Krabicka;Yong Yan
中科院分区：
文献类型：
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作者：
Xinli Li;Duo Sun;G. Lu;J. Krabicka;Yong Yan
火焰中反应自由基的特性对于深入了解燃烧排放物的形成过程至关重要。本文提出了一种基于火焰自由基成像、火焰温度监测和神经网络技术的烟气NOx （NO和NO2）排放预测算法。利用增强多波长成像系统捕获了OH*、CN*、CH*和C2*等火焰自由基的辐射图像。火焰温度是用光谱仪和双色热分析法测定的。在此基础上提取火焰自由基的特征值。这些特征值与火焰温度一起用于预测NOx排放。实验室规模的燃气燃烧装置的实验结果表明，所提出的方法对于预测NOx排放是有效的。
The characteristics of reacting radicals in a flame are crucial for an in-depth understanding of the formation process of combustion emissions. This paper presents an algorithm for the prediction of NOx (NO and NO2) emissions in flue gas through flame radical imaging, flame temperature monitoring and application of Neural Network techniques. Radiation images of flame radicals OH*, CN*, CH* and C2* are captured using an intensified multi-wavelength imaging system. Flame temperature is determined using a spectrometer and two-color pyrometry. Based on these images, the characteristic values of the flame radicals are extracted. These characteristic values, together with the flame temperature, are then used to predict NOx emissions. Experimental results from a laboratory-scale gas-fired combustion rig have shown the effectiveness of the proposed method for the prediction of NOx emissions.