Application of TDLAS technology to multicomponent detection in olefin production process

Wen-hai JI; Xiao-cui LÜ; Wen-ze HU; Guo-lin LI

doi:10.3788/OPE.20182608.1837

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Application of TDLAS technology to multicomponent detection in olefin production process

Laser Spectroscopy | 更新时间：2020-08-13

- Application of TDLAS technology to multicomponent detection in olefin production process
- Optics and Precision Engineering Vol. 26, Issue 8, Pages: 1837-1845(2018)
- 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：
- 基金信息：
- DOI：10.3788/OPE.20182608.1837
  CLC： O433.4;O657.38
- Received：23 April 2018，
  
  Accepted：24 May 2018，
  
  Published：25 August 2018
- 稿件说明：
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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：

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.

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references

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