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| Quantitative Study of Laser Methane Concentration Based on Support Vector Machine |
| CAO Quan-wei1,TU Cheng-xu1,XU Hao-hao2,BAO Fu-bing1,LI Xiang2,SHEN Jia-yuan3 |
1. School of Metrology and Testing Engineering, China Jiliang University, Hangzhou, Zhejiang 310018, China
2. Zhejiang Zheneng Technology Research Institute Co., Ltd., Hangzhou, Zhejiang 310018, China
3. Zhejiang Zheneng Natural Gas Operation Co., Ltd., Hangzhou, Zhejiang 310000, China |
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Abstract It is important to detect methane gas and quantify methane concentration accurately based on tunable semiconductor laser absorption spectroscopy (TDLAS).Due to the weak laser methane signal, the traditional methane concentration quantification model has the problem of large quantization error under complex working conditions.To solve this problem, a quantitative regression model of laser methane telemetering concentration was built based on radial basis RBF-nu-SVR, and was compared with the traditional model, polynomial kernel and linear kernel nu support vector regression, BP neural network and other models.In the results, the RBF-nu-SVR model built showed strong generalization performance on the test set.The average absolute percentage error (MAPE) and Pearson correlation coefficient (R2) were 9.155 and 0.99947 respectively, which improved the detection accuracy of TDLAS for methane gas.Because the original data was used, it was unnecessary to extract the second harmonic, thus simplifying the structure.
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Received: 08 October 2022
Published: 25 June 2023
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2023, Vol. 44 
