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Nonlinear predictive control of periodically forced chemical reactors
Author(s) -
Özgülşen Fatih,
Kendra Scott J.,
Çinar Ali
Publication year - 1993
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.690390407
Subject(s) - model predictive control , control theory (sociology) , nonlinear system , chemical reactor , parametric statistics , autoregressive model , continuous stirred tank reactor , controller (irrigation) , engineering , control (management) , continuous reactor , control engineering , computer science , mathematics , chemistry , catalysis , chemical engineering , physics , statistics , agronomy , quantum mechanics , artificial intelligence , biology , biochemistry
A nonlinear model‐predictive control strategy is developed to maintain the superior‐to‐steady‐state performance of a periodically forced chemical reactor. The performance of the predictive controller is investigated in the presence of measurement disturbances and parametric uncertainty. It is also shown that statistically inferred input‐output models can be a substitute whenever detailed fundamental models are not available. A nonlinear autoregressive polynomial model based on observed plant data is built and incorporated into the control scheme. The catalytic oxidation of ethylene in a periodically‐forced, continuous stirred‐tank reactor is considered as the test case.