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Online estimation for catalyst activity of acetylene hydrogenation reactor
Author(s) -
Xie FuMing,
Xu Feng,
Liang ZhiShan,
Luo XiongLin
Publication year - 2020
Publication title -
asia‐pacific journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.348
H-Index - 35
eISSN - 1932-2143
pISSN - 1932-2135
DOI - 10.1002/apj.2406
Subject(s) - extended kalman filter , acetylene , catalysis , computation , ethylene , kalman filter , process engineering , computer science , process (computing) , materials science , chemistry , algorithm , engineering , organic chemistry , artificial intelligence , operating system
In the ethylene industry, the high purity of the ethylene product depends on hydrogenation in acetylene hydrogenation reactor. Because the catalyst deactivation leads to the moving of the operating point, the operation scheme must be adjusted continually according to the catalyst activity. It is necessary to estimate the catalyst activity online. Based on the discrete dynamic model of the acetylene hydrogenation reactor, the extended Kalman filter (EKF) is used to build the soft sensor for catalyst activity. Considering that EKF involves the large computation costs, we propose a method that estimates the parameters of the time‐varying deactivation kinetics model for the tradeoff of accuracy and complexity. The method is effective to reduce computation complexity of estimation, and simultaneously, the accuracy satisfies the process requirement.