基于CIC的数字正交锁相放大器的甲烷检测实验研究

Experimental Study on Methane Detection Based on Cascaded Integrator Comb of Digital Orthogonal Phase-locked in Amplifier

针对甲烷在大气中背景气体成分复杂、检测难度大、稳定性差等问题,本文基于可调谐二极管激光吸收光谱技术和波长调制光谱技术,将积分梳状滤波器与有限脉冲响应滤波器相结合应用于数字正交锁相放大器,开展大气中甲烷气体的痕量检测实验研究。实验表明,与传统的数字正交锁相放大器相比较,改进的数字正交锁相放大器提取的二次谐波信号的信噪比从38.61 dB提高到44.95 dB;将非线性迭代最小二乘法-极限学习机算法模型应用于甲烷气体浓度反演,与经典的最小二乘法相比较,其均方根误差减小了0.907;通过16组浓度步进实验测试,该系统的实际检测下限为1 ppm;在压力为600 mbar,温度为25℃,甲烷浓度为50 ppm进行3 h的长期稳定性测试,检测的甲烷浓度变化范围为49.6~50.3 ppm,其标准差为0.0921 ppm。当积分时间达到56 s时,该系统的理论检测极限为25.6 ppb。积分梳状滤波器和非线性迭代最小二乘法-极限学习机算法模型在红外气体检测方面具有较高的优越性和实用前景。

Aiming at the problems of complicated background gas composition,difficult detection,and poor stability of methane in the atmosphere,based on tunable diode laser absorption spectroscopy technology and wavelength modulation spectroscopy technology,the cascaded integrator comb filter and finite impulse response filter are applied to the digital quadrature lock-in amplifier to investigate experimental research on trace detection of methane gas in the atmosphere in this paper.Experiments show that compared with the traditional digital quadrature lock-in amplifier,the signal-noise ratio of the second harmonic signal extracted by the improved digital quadrature lock-in amplifier is improved from 38.61 dB to 44.95 dB;Applying the nonlinear iteration partial least square-extreme learning machine algorithm model to the methane gas concentration inversion,compared with the classic least square method,the root mean square error decreases by 0.907.Through 16 sets of concentration step-by-step experiment tests,the actual detection limit of the system is 1 ppm;The long-term stability test is conducted at 600 mbar pressure,25℃and 50 ppm methane concentration for 3 hours.The variation range of methane concentration detected is 49.6 ppm~50.3 ppm,and the standard deviation is 0.0921 ppm.When the integration time reaches 56 s,the theoretical detection limit of the system is 25.6 ppb.This shows that the cascaded integrator comb filter and the nonlinear iteration partial least square-extreme learning machine algorithm model have higher superiority and practical prospects in infrared gas detection.