On-line identification of biomass fuels based on flame radical imaging and application of radical basis function neural network techniques
On-line identification of biomass fuels based on flame radical imaging and application of radical basis function neural network techniques
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DOI：
10.1049/iet-rpg.2013.0392
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发表时间：
2015-05-01
影响因子：
2.6
通讯作者：
Liu, Shi
中科院分区：
文献类型：
--
作者：
Li, Xinli;Wu, Mengjiao;Liu, Shi
在生物质燃烧发电厂中，一系列生物质燃料用于发电。为了提高燃烧效率和减少污染物排放，需要连续在线识别生物质燃料的类型。基于火焰自由基成像和径向基函数（RBF）神经网络（NN）技术相结合的生物质燃料在线识别的最新研究。通过计算火焰自由基（OH*、CN*、CH* 和C-2*）的强度比、强度等值线、平均强度、面积和偏心率等特征值，构造了两种RBF网络，即精确型和概率型RBF网络。在实验室规模的燃烧试验台上对三种生物质燃料（面粉、杨柳木屑和棕榈仁壳）进行了燃烧实验，实验结果验证了该方法的有效性。
In biomass fired power plants a range of biomass fuels are used to generate electric power. It is desirable to identify the type of biomass fuels on-line continuously in order to achieve an improved combustion efficiency, and reduced pollutant emissions. This paper presents the recent investigations into the on-line identification of biomass fuels based on the combination of flame radical imaging and radical basis function (RBF) neural network (NN) techniques. The characteristic values of flame radicals (OH*, CN*, CH* and C-2*), including the intensity ratio, intensity contour, mean intensity, area and eccentricity, are computed to reconstruct two types of RBF NN, that is, accurate and probabilistic RBF networks. Experimental results obtained for three types of biomass fuels (flour, willow sawdust and palm kernel shell) firing on a laboratory-scale combustion test rig are presented to demonstrate the effectiveness of the proposed method.