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Systematic controllability analysis for chemical processes
Author(s) -
Yuan Zhihong,
Zhang Nan,
Chen Bingzhen,
Zhao Jinsong
Publication year - 2012
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.13722
Subject(s) - controllability , operability , chemical process , process (computing) , inherent safety , profitability index , process control , computer science , stability (learning theory) , biochemical engineering , process design , control engineering , heat exchanger , control theory (sociology) , control (management) , process engineering , engineering , reliability engineering , mathematics , process integration , mechanical engineering , software engineering , finance , chemical engineering , machine learning , artificial intelligence , economics , operating system
Modern chemical industrial processes are becoming more and more integrated and consist of multiple interconnected nonlinear process units. These strong interactions profoundly complicate a system's inherent properties and further alter the plant‐wide process dynamics. This may lead to a poor control performance and cause plant‐wide operability problems. To ensure entire processes run robustly and safely, with considerable profitability, it is crucial to recognize the inherent characteristics that can jeopardize controllability and process behavior at the early design stage. With a focus on inherently safer designs, from a plant‐wide perspective, a systematic method for chemical processes controllability analysis is addressed in this study. In the proposed framework, based on open‐loop stability/instability and minimum/nonminimum‐phase behavior, the entire operating zone of the process can be categorized into distinct subregions with different inherent properties. Variations in the inherent characteristics of a plant‐wide process with the operation and design conditions, over the feasible operation region, can be probed and analyzed. An attempt of this framework is made to illustrate how to clarify the roots of the poor controllability that arise in the design and operation of a large scale chemical process, and the results can provide guidance for both deciding the optimal operation conditions and selecting the most suitable control structure. Singularity theory is also applied in the framework to improve the computational efficiency. The framework is illustrated with two case studies. One involves a reactor‐external heat exchanger network and the other a more complex plant‐wide process, comprising a reactor, an extractor, and a distillation column. © 2012 American Institute of Chemical Engineers AIChE J, 58: 3096–3109, 2012