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Operating Performance Assessment for Transition State of Industrial Processes
Author(s) -
Li Ling,
Wang Yalin,
Sun Bei,
Qian Yingcan
Publication year - 2020
Publication title -
chemical engineering and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.403
H-Index - 81
eISSN - 1521-4125
pISSN - 0930-7516
DOI - 10.1002/ceat.201900657
Subject(s) - state (computer science) , multivariable calculus , process (computing) , transition time , transition (genetics) , set (abstract data type) , computer science , focus (optics) , steady state (chemistry) , reliability engineering , process engineering , engineering , control engineering , algorithm , artificial intelligence , chemistry , biochemistry , gene , physics , optics , programming language , operating system
Traditional operating performance assessment approaches mainly focus on the steady state. However, the assessment indicators designed for the steady state cannot be directly applied to the transition state. Moreover, identifying the process state for a multivariable system is difficult and time‐consuming. An operating performance assessment method for the transition state is developed. In the proposed framework, a transition state detection method is first proposed to detect the transition state from the data automatically and accurately. Then, a systematic set of several special indicators is developed based on the characteristic of the transition state. The effectiveness of the developed method is demonstrated with a case study of an industrial hydrocracking process.