Classification Method for Petroleum Pollutants Based on Inception-One-Dimensional Convolutional Neural Network and Infrared Spectroscopy
10.19756/j.issn.0253-3820.231220
- VernacularTitle:基于离散小波变换算法和Inception卷积模块一维卷积神经网络的石油类污染物红外光谱定性分析
- Author:
De-Ming KONG
1
;
Shao-Wei HE
;
Xin-Yi LI
;
Jun-Yu ZHAO
;
Xiao-Dong NING
Author Information
1. 燕山大学电气工程学院,秦皇岛 066000
- Keywords:
Infrared spectrum;
Petroleum pollutants;
Inception module;
Convolution neural network;
Discrete wavelet transform
- From:
Chinese Journal of Analytical Chemistry
2024;52(9):1287-1297
- CountryChina
- Language:Chinese
-
Abstract:
Infrared spectroscopy technology has many advantages such as high efficiency and non-destructiveness,and has an important research and application value in the field of petroleum pollutant classification and detection.In this study,a petroleum pollutant classification method by combing the discrete wavelet transform(DWT)algorithm and a one-dimensional convolutional neural network based on the Inception module(Inception-1D-CNN)was proposed.Firstly,the DWT algorithm was used to denoise the original infrared spectral data to eliminate the interference information caused by experimental environment,instrument error and manual operation.Then,the inception-1D-CNN model was used to obtain multi-scale infrared spectroscopy feature information,and then classify the petroleum pollutants.Experimental results showed that compared with preprocessing methods such as standard normal variable(SNV),adaptive iteratively reweighted penalized least squares(AirPLS),and Savitzky-Golay smoothing(S-G),the prediction accuracy of the DWT algorithm combined with the 1D-CNN model with a convolutional kernel size of 3×1 was 86.6%,which was 6.6%,6.6%and 3.3%higher,respectively.The prediction accuracy of DWT algorithm combined with 1D-CNN model with a convolutional kernel size of 5×1 was 93.3%,which was 10.0%,7.0%and 3.3%higher,respectively.The prediction accuracy of the DWT algorithm combined with the 1D-CNN model with a convolutional kernel size of 7×1 was 90.0%,which was 6.7%,10.0%and 3.4%higher,respectively.The prediction accuracy of the DWT algorithm combined with the inception-1D-CNN model was 100.0%,which was 10.0%,10.0%and 3.4%higher,respectively.Therefore,the DWT algorithm combined with the inception-1D-CNN model could accurately classify and predict petroleum pollutants,and provided a certain basis for the subsequent treatment of oil spills on the sea surface.
