Biomass Fuel Identification Based on Flame Spectroscopy and Feature Engineering
Biomass Fuel Identification Based on Flame Spectroscopy and Feature Engineering
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DOI：
10.13334/j.0258-8013.pcsee.171984
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发表时间：
2018-08
期刊：
影响因子：
64.8
通讯作者：
Xinli Li;Yijiao Li;G. Lu;Yong Yan
中科院分区：
文献类型：
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作者：
Xinli Li;Yijiao Li;G. Lu;Yong Yan
火焰光谱包含有用的燃烧信息，因此火焰自由基的光谱特征可用于识别不同的生物质燃料。提出了一种基于火焰自由基光谱特征、特征工程和改进支持向量机的生物质燃料识别方法。利用光谱仪获得了生物质火焰和火焰自由基（OH*（310.85nm）、CN*（390.00nm）、CH*（430.57nm）和C2*（515.23nm，545.59nm））的光谱强度信号。通过特征提取、基于Filter的特征选择和基于字典学习的特征学习，建立了能够准确反映样本类别特征的特征工程。采用支持向量机建立辨识模型，其中径向基核参数？和错误惩罚因子C的优化使用改进的网格搜索算法。实验室规模的燃烧装置的实验结果表明，所提出的方法的生物质燃料的识别的有效性。
Flame spectra contain useful information about combustion and hence the spectral features of flame radicals may be used to identify different biomass fuels. A technique for biomass fuel identification is proposed based on the spectral features of flame radicals, feature engineering and improved support vector machine. The spectral intensity signals of biomass flames and flame radicals (OH*(310.85nm), CN*(390.00nm), CH*(430.57nm) and C2*(515.23nm, 545.59nm)) were acquired using a spectrometer. Feature engineering was built, which can accurately reflect the characteristics of sample category, through feature extraction, feature selection based on Filter and feature learning based on dictionary learning. The support vector machine is used to build the identification model, where radial basis kernel parameter ? and error penalty factor C are optimized using an improved grid search algorithm. Experimental results from a laboratory-scale combustion rig show the effectiveness of the proposed method for the identification of biomass fuel.