三维荧光光谱结合2DPCA-SSA-GRNN对柴油占比的检测  

Detection of Diesel Proportion Using Three-Dimensional Fluorescence Spectrum and 2DPCA-SSA-GRNN

作　　者：陈晓玉[1] 杜雅欣 刘亚茹 孔德明[2] Chen Xiaoyu;Du Yaxin;Liu Yaru;Kong Deming(School of Information Science and Engineering,Yaushan Uninersity,Qinhuangdao 066004,Hebei,China;School of Elctrical Engineering,Yanshan University,Qinhuangdao 066004,Hebei,China)

机构地区：[1]燕山大学信息科学与工程学院,河北秦皇岛066004 [2]燕山大学电气工程学院,河北秦皇岛066004

出　　处：《中国激光》2022年第18期169-176,共8页Chinese Journal of Lasers

基　　金：国家自然科学基金(62173289)。

摘　　要：柴油和生物柴油混合油液三维荧光光谱中的荧光峰较多,导致混合油液的主要特征峰不明显,而且随着柴油占比的减少,三维荧光光谱的最大荧光强度位置会发生偏移,荧光强度与柴油占比不满足线性关系。因此,检测混合油液中的柴油占比较为复杂。本团队采用二维主成分分析(2DPCA)对混合油液的三维荧光光谱进行重构,重构荧光光谱中的冗余信息减少,在最佳激发波长450 nm下有能代表柴油的发射光谱(465~500 nm)。利用麻雀搜索算法(SSA)对广义回归神经网络(GRNN)进行优化,构建2DPCA-SSA-GRNN预测网络,该网络输入是训练集中的9个样本经2DPCA重构后能代表柴油的发射光谱,网络输出是柴油占比。最后利用建立的网络预测测试集中4个样本的柴油占比,柴油占比分为85.02%、73.76%、63.80%、53.37%,平均回收率为98.39%,均方根误差为0.90%,预测效果较未利用重构发射光谱的网络具有较大提升,均方误差降低了0.97个百分点,平均回收率提高了1.24个百分点。本文为优化神经网络预测物质占比提供了新方法。Objective Recently,overexploitation of oil resources has become a major problem,attracting extensive attention.Thus,alternative new fuels are needed to alleviate the problem.Biodiesel is usually mixed with diesel as an alternative to diesel because of its similar properties and excellent environmental benefits,thereby reducing the actual consumption of diesel.However,the proportion of diesel in mixed oil containing diesel and biodiesel greatly affects the power transformed.A low diesel proportion will cause problems,such as poor oil atomization,insufficient oil-gas mixing,insufficient engine pressure,and increased nitrogen dioxide emission.Although several countries have different specifications for the proportion of diesel in mixed fuel,the proportion of diesel should be more than 50%.Therefore,the rapid and effective identification of the proportion of diesel in the mixed oil of diesel and biodiesel is greatly significant for the qualitative detection of imported diesel.Methods In this paper,the peak pick-up method was used to detect the proportion of diesel in the mixed oil.Since there were many hydrocarbons in the mixed oil,the fluorescence spectrum of the sample was complex.Thus,selecting the appropriate fluorescence peak was difficult.The sample was projected directly on the two-dimensional plane using two-dimensional principal component analysis(2DPCA),and the first three principal components were selected to reconstruct the fluorescence spectrum.Observe that the reconstructed fluorescence spectrum retained the fluorescence peak with more principal component information.Using this peak intensity to predict the proportion of the diesel oil will greatly improve the prediction accuracy.However,no linear relationship between the retained fluorescence peak intensity and the proportion of diesel oil exists,thus,introducing a neural network for further analysis is necessary.Furthermore,we selected a generalized regression neural network(GRNN)to predict the proportion of diesel oil because of its advantages in dea

关 键 词：光谱学 三维荧光光谱 二维主成分分析 柴油 广义回归神经网络 麻雀搜索算法 

分 类 号：O443.4[理学—电磁学]