TDLAS技术在烯烃生产过程中的多组分检测应用

季文海; 吕晓翠; 胡文泽; 李国林

doi:10.3788/OPE.20182608.1837

您当前的位置：

首页 >

文章列表页 >

TDLAS技术在烯烃生产过程中的多组分检测应用

激光光谱技术 | 更新时间：2020-08-13

- TDLAS技术在烯烃生产过程中的多组分检测应用
- Application of TDLAS technology to multicomponent detection in olefin production process
- 光学精密工程 2018年26卷第8期页码：1837-1845
- 作者机构：
  
  中国石油大学(华东) 信息与控制工程学院, 山东青岛 266580
- 作者简介：
  
  [ "季文海(1975-), 男, 山东聊城人, 博士, 副教授, 硕士生导师, 1998年于华东师范大学获得学士学位, 2002年、2007年于美国俄勒冈大学分别获得硕士和博士学位, 主要从事激光光谱技术的过程分析和安全检测方面的研究。E-mail: jiwenhai@upc.edu.cn" ]
  李国林(1987-), 男, 山东潍坊人, 博士, 讲师, 2010年、2015年于吉林大学分别获得学士、博士学位, 主要从事于光电检测技术的研究。E-mail:liguolin@upc.edu.cn LI Guo-lin, E-mail:liguolin@upc.edu.cn
- 基金信息：
  
  山东省自然科学基金资助项目(ZR2017LF023);青岛科技惠民专项(17-3-3-89-nsh);吉林大学集成光电子学国家重点实验室开放课题(IOSKL2017KF0);中国石油大学（华东）研究生创新工程资助项目(YCX2018065)
- DOI：10.3788/OPE.20182608.1837
  中图分类号： O433.4;O657.38
- 收稿日期：2018-04-23，
  
  录用日期：2018-5-24，
  
  纸质出版日期：2018-08-25
- 稿件说明：
移动端阅览
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.

Wen-hai JI, Xiao-cui LÜ, Wen-ze HU, et al. Application of TDLAS technology to multicomponent detection in olefin production process[J]. Optics and precision engineering, 2018, 26(8): 1837-1845.
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.20182608.1837.

Wen-hai JI, Xiao-cui LÜ, Wen-ze HU, et al. Application of TDLAS technology to multicomponent detection in olefin production process[J]. Optics and precision engineering, 2018, 26(8): 1837-1845. DOI： 10.3788/OPE.20182608.1837.

摘要

烯烃工业生产过程中的多组分在线检测是对其工业过程有效控制、提高处理装置综合效益的重要手段。本文以在线检测烯烃裂解炉的清焦过程生成的一氧化碳和二氧化碳为应用案例，采用可调谐二极管激光吸收光谱技术（TDLAS）作为分析平台进行多组分分析。针对清焦过程，设计了检测0~5%量程CO和CO

的模拟实验。对气体含量随机分布的19组数据分别采用多变量最小二乘算法（CLS）、单组分偏最小二乘算法（PLS1）和多组分偏最小二乘算法（PLS2）进行建模和评估。在后续的多组分交叉干扰实验和CO

的扩展量程准确性测试实验中，PLS1模型的最大误差小于±0.05%，PLS2的小于±0.10%，CLS的小于±0.20%。因此，TDLAS技术结合PLS1算法在实现化工过程中的多组分在线检测时具有先进性。

Abstract

Multicomponent online gas measurement in the production of olefin is an important approach for effective control and improvement of the overall efficiency of the production process. In this study

we took the online measurement of CO and CO

for an olefin cracking furnace coal cleaning process as the application example. A Tunable Diode Laser Absorption Spectroscopy (TDLAS) based analyzing platform was developed to facilitate multicomponent measurement. To simulate the reaction process

we designed 0-5% range CO and CO

tests. Based on the first set of random concentration mixing tests with 19 collected spectra

single component partial least square fitting algorithm models (PLS1) and a multicomponent partial least square fitting algorithm model (PLS2) were developed and evaluated

along with a multivariate classical least square fitting algorithm model (CLS). In subsequent interference and full range step tests

the maximum errors for PLS1

PLS2

and CLS were less than ±0.05%

less than ±0.10%

and less than ±0.20% for CLS. These results demonstrate that the combination of TDLAS and the PLS1 algorithm performed the best during the multicomponent online measurement in the petrochemical process.

关键词

Keywords

references

邵研硕. 乙烯生产过程能效监测系统预测评估模块实现[D]. 大连: 大连理工大学, 2017.

SHAO Y SH. The Implementation of Forecast Evaluation Module in the Ethylene Production Process Inspection System of Energy Efficiency [D]. Dalian: Dalian University of Technology, 2017. (in Chinese)

熊善海. 管式乙烯裂解炉清焦的模拟与控制[D]. 北京: 北京化工大学, 2016.

XIONG SH H. The Simulation and Control of Decoking Process of Ethylene Cracking Furnace [D]. Beijing: Beijing University of Chemical Technology, 2016. (in Chinese)

葛喜慧, 邓方文, 杨红杰, 等.气相色谱法分析甲醇制烯烃产品气全组分[J].化工技术与开发, 2016, 45(9):42-45.

GE X H, DENG F W, YANG H J, et al.. Analysis of MTO gas compositions by gas chromatographic menthol[J]. Technology & Development of Chemical Industry, 2016, 45(9):42-45. (in Chinese)

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.

ZHANG 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].Opt. Precision Eng., 2013, 21(11):2771-2777. (in Chinese)

张志刚, 陈红岩, 曲健.多组分汽车尾气定量检测系统[J].电子器件, 2017, 40(3):773-778.

ZHANG ZH G, CHEN H Y, QU J. System of quantitative detecting multi-component automotive exhaust[J]. Chinese Journal of Electron Devices, 2017, 40(3):773-778. (in Chinese)

GABRYSCH M, CORSI C, PAVONE F S, et al.. Simultaneous detection of CO and CO 2 using a semiconductor DFB diode laser at 1.578μm[J].Applied Physics B, 1997, 65(1):75-79.

涂兴华, 刘文清, 张玉钧, 等. CO和CO 2 的1.58μm波段可调谐二极管激光吸收光谱的二次谐波检测研究[J].光谱学与光谱分析, 2006, 26(7):1190-1194.

TU X H, LIU W Q, ZHANG Y J, et al.. Second-harmonic detection with tunable diode laser absorption spectroscopy of CO and CO 2 at 1.58μm[J].Spectroscopy and Spectral Analysis, 2006, 26(7):1190-1194.(in Chinese)

邢昆明. 基于TDLAS技术检测卷烟主流烟气中CO和CO 2 及抽吸耗氧量[D]. 合肥: 中国科学技术大学, 2017.

XING K M. Detection of CO, CO 2 in Mainstream Cigarette Smoke and Oxygen Consumption During Cigarette Smoking using Tunable Diode Laser Absorption Spectroscopy Technology [D]. Hefei: University of Science and Technology of China, 2017. (in Chinese)

刘彬, 童敏明, 王晶晶.基于红外吸收技术的多组分混合气体检测的研究[J].煤矿安全, 2011, 42(3):5-8.

LIU B, TONG M M. WANG J J. Detection of multi-component mixed gas based on infrared absorption technology[J]. Coal Mine Safety, 2011, 42(3):5-8. (in Chinese)

韩汐. 基于拉曼光谱的气体多组分在线分析新技术研究[A]//中国化学会第30届学术年会摘要集-第二分会: 分析装置及交叉学科新方法[C]. 中国化学会, 2016: 1.

HAN X. Research on new on-line analysis system of multi-component gas based on laser raman spectroscopy[A]//The 30th Annual Academic Annual Meeting of the Chinese Chemical Society-Second Session: Analytical Devices and a New Approach to Cross-discipline[C] . Chinese Chemical Society , 2016: 1. (in Chinese)

杨雅涵, 李国林, 李小鹏, 等, 基于TDLAS技术的天然气中痕量硫化氢分析中的PLS算法应用[J].光子学报, 2017, 46(8):0230002.

YANG Y H, LI G L, LI X P, et al.. Partial least squares algorithm application in TDLAS based trace H 2 S analyses in natural gas[J]. Acta Photonica Sinica, 2017, 46(8):0230002. (in Chinese)

陈兴龙, 董凤忠, 王静鸽, 等. PLS算法在激光诱导击穿光谱分析炉渣成分中的应用[J].光子学报, 2014, 43(9):093002.

CHEN X L, DONG F ZH, WANG J G, et al.. Slag quantitative analysis based on PLS model by laser-induced breakdown spectroscopy[J]. Acta Photonica Sinica, 2014, 43(9):093002. (in Chinese)

乔晓艳, 王艳景, 李刚.偏最小二乘法荧光光谱预测啶虫脒农药残留[J].光学精密工程, 2010, 18(11):2369-2374.

QIAO X Y, WANG Y J, LI G. Prediction of acetamiprid residues by fluorescence spectroscopy based on PLS method[J]. Opt. Precision Eng., 2010, 18(11):2369-2374. (in Chinese)

余道洋, 戚功美, 瞿顶军, 等.基于SVM和PCA的痕量多组分气体检测方法[J].模式识别与人工智能, 2015, 28(8):720-727.

YU D Y, QI G M, ZAI D J, et al.. Detection method of trace multi-component gases based on SVM and PCA[J].PR&AI, 2015, 28(8):720-727. (in Chinese)

CRISTINA M, EMANUEL J N, SERGIO T F, et al.. Comparing the analytical performances of Micro-NIR and FT-NIR spectrometers in the evaluation of acerola fruit quality, using PLS and SVM regression algorithms[J]. Talanta, 2017, 165. 112-116.

王骁, 李博, 冯小琴.基于JADE的室内多组分混合污染气体定量分析[J].红外技术, 2016, 38(3):255-259.

WANG X, LI B, FENG X Q. Quantitative analysis of indoor multi-component gas mixture based on JADE[J]. Infrared Technology, 2016, 38(3):255-259.(in Chinese)

XIN ZH. Diode-laser Absorption Sensors for Combustion Control [D]. California: Stanford University, 2005.

浏览量

226

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

暂无数据