可调谐半导体激光吸收光谱(TDLAS)技术对甲烷气体探测及甲烷浓度的准确量化具有重要意义。由于激光甲烷信号较为微弱,传统甲烷浓度量化模型在复杂工况下存在量化误差大的问题。针对此问题,基于径向基核nu-支持向量回归机(RBF-nu-SVR)构建了激光甲烷遥测浓度量化回归模型,并与传统模型、多项式核及线性核的nu-支持向量回归机、BP神经网络等模型进行了对比研究。结果表明:所构建的RBF-nu-SVR模型在测试集上展现了较强的泛化性能,平均绝对百分比误差(MAPE)和皮尔逊相关系数(R2)分别为9.155和0.99947,提升了TDLAS对甲烷气体的探测精度。且由于采用的是原始数据,不需要进行二次谐波提取,简化了结构。
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.
关键词
计量学 /
甲烷浓度量化 /
支持向量机 /
可调谐半导体激光吸收光谱
Key words
metrology /
methane concentration quantification /
support vector machine /
TDLAS
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基金
国家自然科学基金(12272367,11972334);浙江省科技计划项目(2021C01099)
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