基于TDLAS的CO检测系统调制参数优化与温度补偿

Modulation parameters optimization and temperature compensation of CO detection system based on TDLAS

在可调谐激光吸收光谱(TDLAS)技术中,携带气体浓度信息的二次谐波信号易受激光扫描信号与调制信号的幅值、频率等参数影响。基于TDLAS技术搭建了CO浓度检测硬件系统,与对应仿真模型进行比较分析,研究了调制参数对二次谐波信号峰值、信噪比、对称性以及峰宽的影响,总结出具体变化规律。实验确定了系统最优调制参量,在硬件不变的情况下提高了检测精度。对CO在1567.7nm的吸收光谱进行了检测,发现测量浓度随着温度的升高而降低,最大相对误差已超过15%。为了减少温度变化对测量的影响,分别采用RBF及BP神经网络、PSO优化BP神经网络和WOA优化BP神经网络算法对系统进行补偿。结果表明,WOA优化BP神经网络方法的补偿效果最好,修正后浓度相对误差降至1%以下,有效提高了系统在变温环境下的准确性和稳定性。研究为系统的调制参数设置以及精准检测提供参考,为后续实验提供了有价值的指导。

In TDLAS detection technology,the second harmonic signal carrying gas concentration information is susceptible to the amplitude and frequency of the scanning and modulation signals.Based on TDLAS,a hardware system for CO concentration detection was built and compared with the corresponding simulation model to study the influence of modulation parameters on the peak value,signal-to-noise ratio,symmetry,and peak width of the second harmonic signal.The optimal modulation parameters for the system were determined through experiments,which improved the detection accuracy without changing the hardware.Detection of CO absorption spectroscopy at 1567.7nm revealed that the measured concentration decreases with increasing temperature,with a maximum relative error exceeding 15%.To reduce the temperature effect on measurements,compensation techniques using RBF and BP neural network,PSO-BP neural network,and WOA-BP neural network algorithm was applied to the system.The results showed that the WOA-BP neural network method provided the best compensation effect,reducing the relative error of the corrected concentration to below 1%,effectively enhancing the accuracy and stability of the system in variable temperature environments.This research provides reference for setting modulation parameters and achieving precise detection,and offers valuable guidance for subsequent experiments.