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Model‐predictive safety system for proactive detection of operation hazards
Author(s) -
Ahooyi Taha Mohseni,
Soroush Masoud,
Arbogast Jeffrey E.,
Seider Warren D.,
Oktem Ulku G.
Publication year - 2016
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.15152
Subject(s) - operability , alarm , process (computing) , hazard , process safety , reliability engineering , estimator , constraint (computer aided design) , system safety , engineering , computer science , control engineering , control theory (sociology) , work in process , artificial intelligence , control (management) , mathematics , operations management , mechanical engineering , chemistry , statistics , organic chemistry , aerospace engineering , operating system
A method of designing model‐predictive safety systems that can detect operation hazards proactively is presented. Such a proactive safety system has two major components: a set of operability constraints and a robust state estimator. The safety system triggers alarm(s) in real time when the process is unable to satisfy an operability constraint over a receding time‐horizon into the future. In other words, the system uses a process model to project the process operability status and to generate alarm signals indicating the presence of a present or future operation hazard. Unlike typical existing safety systems, it systematically accounts for nonlinearities and interactions among process variables to generate alarm signals; it provides alarm signals tied to unmeasurable, but detectable, state variables; and it generates alarm signals before an actual operation hazard occurs. The application and performance of the method are shown using a polymerization reactor example. © 2016 American Institute of Chemical Engineers AIChE J , 62: 2024–2042, 2016