基于量子级联激光的石化过程多组分气体遥测技术研究

    Multi-component gas remote sensing technology in petrochemical processes based on quantum cascade lasers

    • 摘要: 针对石油化工过程中不同典型场景下H2S、CH4与C2H2等危险气体的非接触式逸散监测需求,提出了一种基于波长调制光谱的中红外量子级联激光器多组分气体遥测技术。采用中心波长为8.309 μm的量子级联激光器,同时对H2S、CH4与C2H2的吸收光谱进行扫描,解决了单光源覆盖多组分气体吸收峰且需避开水汽干扰的难题。为验证系统在工业现场的适应性,建立了基于Phong模型的漫反射理论分析平台并开展了实验验证。实验结果表明,系统能够从铝板、环氧树脂等典型非合作目标表面获取有效回波,验证了其面向金属及涂层表面进行气体遥测的可行性。Allan方差分析表明,系统在最优积分时间下的最低检测限分别为:H2S为8.75×10−7,CH4为5.792×10−6,C2H22.4204×10−5。此外,系统具备优异的动态响应能力,波长切换响应时间小于500 ms。该技术集高灵敏度、非接触遥测与快速响应于一体,有望为石化过程安全监测提供有力的技术支撑。

       

      Abstract: To address the challenge of non-contact fugitive emission monitoring for multi-component hazardous gases, such as H2S, CH4, and C2H2, throughout the entire petrochemical industry chain, a multi-component gas remote sensing technology based on mid-infrared quantum cascade lasers (QCL) and wavelength modulation spectroscopy was proposed in this paper. A QCL centered at 8.309 μm was employed to simultaneously scan the absorption spectra of H2S, CH4, and C2H2. By this approach, the difficulty of covering the absorption peaks of multiple gas components with a single light source while avoiding water vapor interference was successfully resolved. To verify the system's adaptability to industrial field conditions, a theoretical analysis based on the Phong reflection model and an experimental verification platform were established. Experimental results show that effective return signals could be acquired from the surfaces of typical non-cooperative targets, such as aluminum plates and epoxy resin, by which the feasibility of gas remote sensing on metal and coating surfaces is verified. Allan variance analysis indicated that the limits of detection (LOD) at the optimal integration time are 8.75×10−7 for H2S, 5.792×10−6 for CH4, and 2.4204×10−5 for C2H2. Furthermore, the system is found to exhibit excellent dynamic response capabilities, with a wavelength switching response time of less than 500 ms. By integrating high sensitivity, non-contact remote sensing, and rapid response, this technology is considered to provide robust technical support for safety monitoring in petrochemical processes.

       

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