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_2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_2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。
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 2 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 2 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.
作者
季文海
吕晓翠
胡文泽
李国林
JI Wen-hai;Lv Xiao-cui;HU Wen-ze;LI Guo-lin(College of Information and Control Engineering,China University of Petroleum,Qingdao 266580,China)
出处
《光学精密工程》
EI
CAS
CSCD
北大核心
2018年第8期1837-1845,共9页
Optics and Precision Engineering
基金
山东省自然科学基金资助项目(No.ZR2017LF023)
青岛科技惠民专项(No.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(No.IOSKL2017KF0)
中国石油大学(华东)研究生创新工程资助项目(No.YCX2018065)
关键词
烯烃生产
裂解炉清焦
调制吸收光谱技术
多组分分析
多元回归分析
偏最小二乘法
olefin production
cracking furnace coal cleaning
tunable diode laser absorption spectroscopy(TDLAS)
multicomponent analysis
multivariate classical least square fitting(CLS)
partial least square fitting(PLS)
