摘要
随着经济发展对石油资源需求量的不断增大,各种石油污染问题日渐严重,对生态环境及人类健康造成巨大威胁。因此,准确识别及时处理油类污染物对减轻溢油危害具有重要意义。石油是一种复杂的有机化合物,主要由较强荧光特性的芳香烃成分及其衍生物组成,不同类型的石油所含多环芳烃的成分和含量不同,三维荧光光谱3D-EEM在石油污染物的检测领域应用十分广泛。基于三维荧光光谱技术,采用BP神经网络结合自加权交替三线性分解(SWATLAD)算法对油类污染物进行定性定量的研究。实验以0^(#)柴油、95^(#)汽油和煤油为研究对象,首先,使用F-7000荧光光谱仪采集待测样品的光谱数据,对得到的数据进行激发、发射校正和去散射处理。其次,为解决小波阈值去噪阈值处信号不连续和过度收缩小波系数带来的难以准确还原真实信号的问题,提出了一种改进的阈值函数,去噪后的信噪比(SNR)和均方误差(MSE)分别为18.3547和10.2617,更为真实的还原有用信号。并通过基于误差反向传播的BP神经网络对预处理后的光谱数据进行训练,训练后预测值与真实值的曲线拟合度较好,表明后续经光谱仪采集的荧光数据直接输入神经网络即可输出预处理好的待测数据,简化了实验操作步骤。最后,采用SWATLD对经小波变换和BP神经网络处理后的数据进行分解,解析得到的0^(#)柴油、95^(#)汽油和煤油的激发与发射光谱与真实光谱拟合度较高,计算平均回收率分别为103.64%、99.33%和97.85%,经验证,三维荧光光谱结合改进小波变换和BP神经网络的方法可以对荧光物质进行快速、精确检测。
With the increasing demand for oil resources by economic development,oil pollution problems have become increasingly serious,posing a huge threat to the ecological environment and human health.Therefore,accurate identification and timely treatment of oil pollutants is significant in reducing oil spill hazards.Petroleum is a complex organic compound mainly composed of aromatic hydrocarbons and their derivatives with strong fluorescence characteristics.Different types of petroleum contain different components and contents of polycyclic aromatic hydrocarbons.Three-dimensional fluorescence spectroscopy 3D-EEM is widely used to detect petroleum pollutants.Based on three-dimensional fluorescence spectroscopy,the improved wavelet threshold function and BP(backpropagation)neural network combined with the method of self-weighted alternating trilinear decomposition(SWATLAD)algorithm for qualitative and quantitative research on oil pollutants.The experiment used 0^(#)diesel,95^(#)gasoline and kerosene as the research objects.Firstly,the samples were detected using an F-7000 fluorescence spectrometer,and the obtained data were processed by excitation,and emission correction.Secondly,an improved threshold function is proposed to solve the problem of signal discontinuity and excessive shrinkage of wavelet coefficients at the threshold of wavelet threshold denoising.The signal-to-noise ratio(SNR)and mean square error(MSE)are 18.3547 and 10.2617,respectively,which can more accurately restore useful signals.The preprocessed spectral data were trained by BP neural network based on error backpropagation.After training,the curve of the predicted value after training was in good agreement with the real value,indicating that the subsequent fluorescence data collected by the spectrometer can be directly input into the neural network to output the preprocessed data,simplifying the experimental operation steps.Finally,Finally,SWATLD was used to decompose the data processed by improved wavelet transform and BP neural network.The excitation and emission spectra of 0^(#)diesel,95^(#)gasoline and kerosene obtained by the analysis were in good agreement with the real spectra,and the calculated average recoveries were 103.64%,99.33%and 97.85%.It is proved that three-dimensional fluorescence spectroscopy combined with improved wavelet transform and BP neural network can detect fluorescent substances quickly and accurately.
作者
朱燕萍
崔传金
程朋飞
潘金燕
苏皓
张怡
ZHU Yan-ping;CUI Chuan-jin;CHENG Peng-fei;PAN Jin-yan;SU Hao;ZHANG Yi(College of Electrical Engineering,North China University of Science and Technology,Tangshan 063210,China;Tangshan Key Laboratory of Semiconductor Integrated Circuits,Tangshan 063210,China)
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2023年第8期2467-2475,共9页
Spectroscopy and Spectral Analysis
基金
国家自然科学基金项目(61803154)
河北省自然科学基金项目(F2019209323,F2019209443,F2019209599)
河北省研究生示范课程建设项目(KCJSX2021061)
河北省高校基本科研业务费项目(JQN2021020)资助。
关键词
三维荧光光谱
小波阈值去噪
BP神经网络
自加权交替三线性分解
Three-dimensional fluorescence spectrum
Wavelet threshold denoising
BP neural network
Self-weighted alternating trilinear decomposition
