-
摘要: 针对固定污染源烟气超低排放以及CO2等温室气体迫切的监测需求，常规的烟气在线监测产品很难满足越来越高的监测标准，以及多因子同时监测的需求。本文介绍了基于非分散红外原理的多组分微量气体分析系统，建立气体滤波相关（GFC）和干涉滤波相关（IFC）技术的理论模型，以建立有效光程、滤光片中心波长和带宽等关键系统参数，以及待测气体浓度与测量和参考信号的关系，确定各气体组分所采用的测量技术。构建多组分微量气体分析系统，GFC和IFC相结合的技术，在时域上实现了参考和检测的双光路设计，采用长光程的多次回返气体室，实现小量程和0.5 mg/m3的检出限，以及不超过±2%F.S.的24 h零点和量程漂移，可同时在线监测SO2、NO、NO2、CO和CO2等气态污染物，满足固定污染源超低排放和碳排放的监测需求，有助于解决固定污染源烟气排放监测数据的真实、准确和全面的问题。Abstract: Ultra-low emission standards of flue gas emitted from stationary sources have been proposed, which creates a new challenge for Continuous Emission Monitoring (CEM). Peak carbon dioxide emissions and carbon neutrality are frequently-mentioned concepts, which means the monitoring of CO2 will eventually be necessary. It is difficult to satisfy the strict limits of ultra-low emission standards with conventional CEM systems. A multi-component trace gas analysis system based on non-dispersive infrared is promoted in this paper to monitor trace gases of continuous emission. A Gas Filter Correlation (GFC) model and Interference Filter Correlation (IFC) model were established, which can describe the relationship of optical length, center wavelength, bandwidth of the filters and gas concentration with measure and reference signals. To confirm the measurement technique of gases, the GFC technique combines with the IFC technique to achieve a double-beam path. With the help of white cells, a small-scale, and the detection limit better than 0.5 mg/m3 can be realized. Zero and span drift are no more than ±2% of the full scale. SO2, NO, NO2, CO and CO2 can be simultenously and continuously monitored to satisfy the requirements of ultra-low and carbon emission monitoring. This technique is helpful for obtaining factual, accurate and comprehensive CEM data.
-
图 1 (a) GFC和(b) IFC的技术原理示意图
Figure 1. Schematic diagram of (a) GFC technique and (b) IFC technique
图 4 GFC和IFC模型中，NO测量信号和参考信号之比随浓度的变化情况
Figure 4. The ratio of the measurement to the reference signal of NO varying with gas density at GFC model and IFC model
图 5 GFC和IFC模型中，NO2测量信号和参考信号之比随气候浓度的变化情况
Figure 5. The ratio of the measurement to the reference signal of NO2 varying with gas density at the GFC model and IFC model
图 7 分析系统中光学系统示意图
Figure 7. Schematic diagram of the optical system for the proposed system
图 10 气体池吸光度曲线。(a) NO吸光度曲线；(b) N2吸光度曲线
Figure 10. Absorption of (a) NO and (b) N2 gas cell
图 12 分析系统 (a) NO2、NO、CO和SO2的零点噪声及(b)CO2的零点噪声
Figure 12. Zero noise of (a) NO2、NO、CO、SO2 and (b) CO2 of analysis system
表 1 分析系统的量程
Table 1. Span of multi-component analysis system
气体名称 量程 SO2/(mg·m−3) 0~50 NO/(mg·m−3) 0~100 NO2/(mg·m−3) 0~50 CO/(mg·m−3) 0~100 CO2/(%) 25 
下载: 导出CSV
表 2 分析系统的检出限
Table 2. Detection limit of the multi-component analysis system
不同气体 检出限 SO2/(μg·m−3) 50 NO/(μg·m−3) 350 NO2/(μg·m−3) 120 CO/(μg·m−3) 130 CO2/(%) 0.05
下载: 导出CSV
表 3 分析系统的零点和量程漂移
Table 3. Zero and span drift of multi-component analysis system
24 h漂移（%F.S.） 零点 量程 NO 1.48 −1.06 NO2 −0.55 0.61 CO2 0 −0.71 CO 0.4 0.49 SO2 1.85 1.27
下载: 导出CSV
-
[1] 李相贤, 徐亮, 高闽光, 等. 分析温室气体及CO2碳同位素比值的傅里叶变换红外光谱仪[J]. 光学 精密工程,2014,22(9):2359-2368. doi: 10.3788/OPE.20142209.2359LI X X, XU L, GAO M G, et al. Fourier transform infrared greenhouse analyzer for gases and carbon isotope ratio[J]. Optics and Precision Engineering, 2014, 22(9): 2359-2368. (in Chinese) doi: 10.3788/OPE.20142209.2359 [2] 张志荣, 夏滑, 董凤忠, 等. 利用可调谐半导体激光吸收光谱法同时在线监测多组分气体浓度[J]. 光学 精密工程,2013,21(11):2771-2777. doi: 10.3788/OPE.20132111.2771ZHANG ZH R, XIA H, DONG F ZH, et al. Simultaneous and on-line detection of multiple gas concentration with tunable diode laser absorption spectroscopy[J]. Optics and Precision Engineering, 2013, 21(11): 2771-2777. (in Chinese) doi: 10.3788/OPE.20132111.2771 [3] 季文海, 吕晓翠, 胡文泽, 等. TDLAS技术在烯烃生产过程中的多组分检测应用[J]. 光学 精密工程,2018,26(8):1837-1845. doi: 10.3788/OPE.20182608.1837JI W H, LÜ X C, HU W Z, et al. Application of TDLAS technology to multicomponent detection in olefin production process[J]. Optics and Precision Engineering, 2018, 26(8): 1837-1845. (in Chinese) doi: 10.3788/OPE.20182608.1837 [4] SUN Y W, LIU W Q, WANG SH M, et al. Method of sensitivity improving in the non-dispersive infrared gas analysis system[J]. Chinese Optics Letters, 2011, 9(6): 060101. doi: 10.3788/COL201109.060101 [5] 徐驰, 刘娟, 居力, 等. 非分散红外法应用于在用柴油车NOx排放检测的研究[J]. 中国环境监测,2019,35(3):28-33.XU CH, LIU J, JU L, et al. Research of NDIR applied on measuring NOx emission from in-use diesel vehicles[J]. Environmental Monitoring in China, 2019, 35(3): 28-33. (in Chinese) [6] 刘通浩, 张守斌, 敬红, 等. 甲烷对非分散红外吸收法测定固定污染源废气中二氧化硫的干扰[J]. 中国环境监测,2020,36(6):143-149.LIU T H, ZHANG SH B, JING H, et al. Study on the interference of methane to the determination of sulfur dioxide in stationary pollutant exhaust gas by non-dispersive infrared absorption method[J]. Environmental Monitoring in China, 2020, 36(6): 143-149. (in Chinese) [7] 李唐安, 李世阳, 张家明, 等. 基于Goertzel算法的红外气体检测方法[J]. 红外与激光工程,2019,48(3):0304003. doi: 10.3788/IRLA201948.0304003LI T A, LI SH Y, ZHANG J M, et al. Infrared detection method of gas based on Goertzel algorithm[J]. Infrared and Laser Engineering, 2019, 48(3): 0304003. (in Chinese) doi: 10.3788/IRLA201948.0304003 [8] 高颖, 戴连奎, 朱华东, 等. 基于拉曼光谱的天然气主要组分定量分析[J]. 分析化学,2019,47(1):67-76. doi: 10.1016/S1872-2040(18)61135-1GAO Y, DAI L K, ZHU H D, et al. Quantitative analysis of main components of natural gas based on Raman spectroscopy[J]. Chinese Journal of Analytical Chemistry, 2019, 47(1): 67-76. (in Chinese) doi: 10.1016/S1872-2040(18)61135-1 [9] 聂新明, 陈正毅, 宗成华, 等. 环境中挥发性污染物现场快捷检测系统研究[J]. 分析化学,2020,48(8):981-989.NIE X M, CHEN ZH Y, ZONG CH H, et al. A rapid on-site detection system for volatile pollutants in environment[J]. Chinese Journal of Analytical Chemistry, 2020, 48(8): 981-989. (in Chinese) [10] 温国基, 戴连奎, 刘薇, 等. 基于遗传算法与线性叠加模型的混合物组成拉曼光谱定量分析[J]. 分析化学,2021,49(1):85-94.WEN G J, DAI L K, LIU W, et al. Raman spectroscopic quantitative analysis based on genetic algorithm and linear superposition principle[J]. Chinese Journal of Analytical Chemistry, 2021, 49(1): 85-94. (in Chinese) [11] 孙友文, 刘文清, 汪世美, 等. 非分散红外多组分气体检测技术及其在CEMS中的应用[J]. 红外,2011,32(5):23-26. doi: 10.3969/j.issn.1672-8785.2011.05.005SUN Y W, LIU W Q, WANG SH M, et al. Non-dispersive infrared multi-component gas analysis technology and it’s application in CEMS[J]. Infrared, 2011, 32(5): 23-26. (in Chinese) doi: 10.3969/j.issn.1672-8785.2011.05.005 [12] 叶刚, 赵静, 陈建伟. 基于NDIR原理的多组分气体在线监测系统的设计与实现[J]. 计算机应用与软件,2019,36(8):115-119, 188. doi: 10.3969/j.issn.1000-386x.2019.08.021YE G, ZHAO J, CHEN J W. Design and implementation of multicomponent gas online monitoring system based on NDIR[J]. Computer Applications and Software, 2019, 36(8): 115-119, 188. (in Chinese) doi: 10.3969/j.issn.1000-386x.2019.08.021 [13] 熊涛, 高明, GIBSON D, 等. 新型光室结构的主流式NDIR呼吸CO2监测系统[J]. 红外与激光工程,2020,49(6):20190575. doi: 10.3788/irla.30_2019-0575XIONG T, GAO M, GIBSON D, et al. Mainstream NDIR breathing CO2 monitoring system based on new light chamber structure[J]. Infrared and Laser Engineering, 2020, 49(6): 20190575. (in Chinese) doi: 10.3788/irla.30_2019-0575 [14] SEBACHER D I. A gas filter correlation monitor for CO, CH4, and HCL[R]. NASA Technical Papers 1113. Langley Station: National Aeronautics and Space Administration, 1977: 10. [15] 司福祺, 刘建国, 刘文清, 等. 基于气体相关滤波技术的非分散红外CO气体监测系统的研究[J]. 量子电子学报,2004,21(4):425-428. doi: 10.3969/j.issn.1007-5461.2004.04.006SI F Q, LIU J G, LIU W Q, et al. Non-dispersive infrared instrument based on gas filter correlation technology for atmospheric CO monitoring[J]. Chinese Journal of Quantum Electronics, 2004, 21(4): 425-428. (in Chinese) doi: 10.3969/j.issn.1007-5461.2004.04.006 [16] 陈晓宁, 刘建国, 司福祺, 等. 气体滤波相关技术在红外甲烷监测系统中的应用[J]. 光电工程,2008,35(4):49-52.CHEN X N, LIU J G, SI F Q, et al. Application of gas filter correlation technique in IR monitoring system of methane[J]. Opto-Electronic Engineering, 2008, 35(4): 49-52. (in Chinese) -
下载:
计量
- 文章访问数: 1992
- HTML全文浏览量: 688
- PDF下载量: 203
- 被引次数: 0
