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Online operating performance evaluation for the plant‐wide industrial process based on a three‐level and multi‐block method
Author(s) -
Chang Yuqing,
Ma Ruxue,
Zhao Luping,
Wang Fuli,
Wang Shu
Publication year - 2019
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.23424
Subject(s) - process (computing) , block (permutation group theory) , identification (biology) , computer science , tracing , performance indicator , sensitivity (control systems) , reliability engineering , engineering , mathematics , botany , geometry , management , electronic engineering , economics , biology , operating system
Process operating performance evaluation is of great significance both in theoretical research and practical application. Process operating performance evaluation is used to determine whether or not a production process is running in excellent operating conditions. When the production process is running in a non‐optimal state, non‐optimal reason tracing will be performed to find the reasons causing the non‐optimal running state of the process so that the process can quickly return to the optimal operating state. In this article, multiple working modes and plant‐wide industrial process characteristics are considered. To solve the above problems, a novel multiple three‐level multi‐block hybrid model based online operating performance evaluation approach is proposed. Under each working condition, a three‐level multi‐block model is established. Under the corresponding working mode, each block at the bottom level is evaluated first. Second, the blocks in the middle level are evaluated according to the evaluation results of the bottom level blocks. Finally, according to the evaluation results of the middle level blocks, the operating performance of the top level can be achieved. When non‐optimal operating performance occurs, a variable contribution‐based cause identification technique is developed to locate the variables leading to non‐optimal performance and provide operation strategies to bring the process back to optimal performance. At the end of the paper, the developed evaluation and non‐optimal cause identification approaches are explored in a gold hydrometallurgy process.