基于拉曼光谱的转炉烟气中多组分气体在线分析方法研究

doi:10.3964/j.issn.1000-0593(2025)01-0146-06

摘要
参考文献
相关文章 (15)

全文: PDF (5846 KB)
输出: BibTeX | EndNote (RIS)

摘要：冶金行业中，转炉烟气的在线分析普遍采用激光吸收光谱法等单组分气体分析技术，实践中需使用多传感器的组合以同时监测CO、CO₂、O₂、H₂等多组分，分析系统整体的复杂程度和成本高，响应速度慢，逐个传感器的日常校正维护工作量大。为简化在线分析系统的复杂度，提高分析效率，实现炼钢流程的降本增效，首创性的建立了一种转炉烟气中多组分气体实时在线分析的拉曼光谱方法，并首次研制了专用于炉气检测的多组分气体分析系统。通过检测CO、CO₂、N₂、O₂、H₂五组分的拉曼光谱特征峰强，对五组分含量进行同时定量分析。检测范围0.1%～100%，检测精密度RSD值均≤0.1%。分析系统中，样气处理模块采用耐高温探头和大功率抽气泵，快速、有效去除进入分析仪的样气中水分和灰尘，保证测试条件稳定；分析模块采用窄线宽532 nm激光器和透射式光谱仪，并通过空间光路设计以提高检测灵敏度，满足气体检测需求。相比于现有的单组分在线分析技术，所提出的多组分检测方法可同时给出五组分的定量结果，不受样气压力、测试条件变化影响，样气前处理难度低，检测准确度高，系统维护简单。N₂标准气体的拉曼散射信号特征峰强在24 h内波动≤0.48%。经标准样品验证，该方法检测误差≤0.3%，达到转炉烟气实时检测的需求。该方法不仅在冶金行业有广阔应用前景，还可通过模型扩展以拓展到其他多组分气体实时在线分析监测领域。

关键词：拉曼光谱；在线气体分析；转炉烟气；多元标准曲线模型；冶金

Abstract：In the modern metallurgical industry, online analysis of converter flue gas is commonly conducted by laser absorption spectroscopy, which can only measure a single gas component by each sensor. Therefore, a combination of multiple sensors is usually needed to monitor multiple gases simultaneously, including CO，CO₂，O₂，H₂, etc. The whole analysis system is complicated and expensive, with a slow response speed. Daily calibration and maintenance are also time-consuming and labor-intensive. This manuscript established a novel Raman spectroscopy-based methodology for real-time and multi-component analysis of converter flue gas to simplify the online analysis system and improve the efficiency of online measurement and the steelmaking process. A multi-gas analysis system for fuel gas measurement was also developed for the first time. The content of five CO, CO₂, N₂, O₂, and H₂ components could be quantified simultaneously based on the characteristic peak intensities. With a detection range of 0.1%~100%, the RSD value of our method was ≤0.1%. In the analysis system, the sample gas processing module adopted a high-temperature resistant probe and a high-power suction pump, which can effectively and quickly remove moisture and dust from the sample gas entering the analyzer, ensuring stable testing conditions. The analysis module employed a narrow-linewidth 532 nm laser, a transmission grating spectrometer, and a free space light path design to improve the detection sensitivity for gas analysis. Compared with single-component analysis techniques, the multi-component analysis method proposed in this manuscript could provide quantitative results for five components simultaneously without being affected by gas pressure variation. Sample gas processing and system maintenance were simple, while the detection accuracy was high. When N₂ standard gas was measured, the intensity of the characteristic peak was stable, with fluctuation less than 0.48% within 24 hours. The detection error of our method was less than 0.3% when quantifying standard gas samples, meeting the requirements of real-time analysis of converter flue gas. The multi-component gas analysis method by Raman spectroscopy could be widely applied in both the metallurgical industry and other fields by simple model expansion.

Key words：Raman spectroscopy; Online gas analysis; Converter flue gas; Multivariate standard curve model; Metallurgy

收稿日期: 2023-12-08 修订日期: 2024-04-20

中图分类号:

O659.2

基金资助: 国家重点研发计划项目(2021YFF0604004)，广州市科技计划项目(2023B03J1340)资助

通讯作者: 刘晓萌 E-mail: liuxiaom@nim.ac.cn

作者简介: 郑传新，1968年生，鞍钢集团本钢板材股份有限公司炼钢厂副高级工程师 e-mail: 8974151@qq.com

引用本文:

郑传新，刘晓萌，李泉，孟征，王恩宁，司星宇，王健年，李振林，王红球. 基于拉曼光谱的转炉烟气中多组分气体在线分析方法研究[J]. 光谱学与光谱分析, 2025, 45(01): 146-151.
ZHENG Chuan-xin, LIU Xiao-meng, LI Quan, MENG Zheng, WANG En-ning, SI Xing-yu, WANG Jian-nian, LI Zhen-lin, WANG Hong-qiu. Online and Multi-Component Analysis of Converter Flue Gas by Raman Spectroscopy. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(01): 146-151.

链接本文:

https://www.gpxygpfx.com/CN/10.3964/j.issn.1000-0593(2025)01-0146-06 或 https://www.gpxygpfx.com/CN/Y2025/V45/I01/146

[1] Zhou J A，Jiang X K，Xie J B，et al. J. Iron. Steel Res. Int.，2018，25：65.
[2] ZHANG Zhi-long(张志龙). Shandong Industrial Technology(山东工业技术)，2019，4：79.
[3] PAN Yu-zhu，ZHANG Cai-jun，ZHU Xin-hua，et al(潘玉柱，张彩军，朱新华，等). Henan Metallurgy(河南冶金)，2014，22(5)：15.
[4] WU Hao，CHI Yong-chao，LIU Chen(吴浩，迟永超，刘琛). Energy for Metallurgical Industry(冶金能源)，2021，40(4)：62.
[5] WEN Mei-xia(文梅霞). Automation & Instrumentation(自动化与仪器仪表)，2019，12：208.
[6] LIU Mo-yan(刘莫岩). Science & Technology Vision(科技视界)，2015，20：11.
[7] Felmy H M，Clifford A J，Medina A S，et al. Environ. Sci. Technol.，2021，55(6)：3898.
[8] Keiner R，Frosch T，Massad T J，et al. Analyst，2014，139(16)：3879.
[9] Khannanov M N，Kirpichev V E. Bull. Russ. Acad. Sci. Phys.，2021，85(2)：169.
[10] Khannanov M N，Van'kov A B，Novikov A A，et al. Appl. Spectrosc.，2020，74(12)：1496.
[11] Li B，Luo S，Yu A，et al. Laser Phys. Lett.，2017，14(9)：095701.
[12] Mollaeian K，Wei S，Islam M R，et al. Energy & Fuels，2016，30(5)：4112.
[13] Niklas C，Wackerbarth H，Ctistis G. Sensors，2021，21(5)：1698.
[14] Petrak B，Cooper J，Konthasinghe K，et al. Appl. Phys. Lett.，2016，108：091107.
[15] Petrov D，Matrosov I. Anal. Chem.，2023，95(25)：9409.
[16] Petrov D V，Matrosov I I，Zaripov A R，et al. Sensors，2022，22(9)：3492.
[17] Rupp S，James T M，Telle H H，et al. Fusion Science and Technology，2013，67(3)：547.
[18] Schwarz J，Ilic A，Kaufhold S，et al. Sustainable Energy Fuels，2022，6(19)：4388.
[19] Sieburg A，Schneider S，Yan D，et al. Analyst，2018，143(6)：1358.
[20] ZHU Hua-dong，ZHANG Si-qi，TANG Chun-jie(朱华东，张思琦，唐纯洁). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2023，43(11)：3551.