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Multivariable model predictive control of a novel rapid pressure swing adsorption system
Author(s) -
Urich Matthew D.,
Vemula Rama Rao,
Kothare Mayuresh V.
Publication year - 2018
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.16011
Subject(s) - multivariable calculus , model predictive control , control theory (sociology) , context (archaeology) , control engineering , nonlinear system , pressure swing adsorption , controller (irrigation) , pid controller , process (computing) , engineering , computer science , temperature control , control (management) , artificial intelligence , chemistry , adsorption , paleontology , agronomy , physics , organic chemistry , quantum mechanics , biology , operating system
A multivariable model predictive control (MPC) algorithm is developed for the control and operation of a rapid pressure swing adsorption (RPSA)‐based medical oxygen concentrator. The novelty of the approach is the use of all four step durations in the RPSA cycle as independent manipulated variables in a truly multivariable context. The RPSA has a complex, cyclic, nonlinear multivariable operation that requires feedback control, and MPC provides a suitable framework for controlling such a multivariable system. The multivariable MPC presented here uses a quadratic optimization program with integral action and a linear model identified using subspace system identification techniques. The controller was designed and tested in simulation using a complex, highly coupled, nonlinear RPSA process model. The model was developed with the least restrictive assumptions compared to those reported in the literature, thereby providing a more realistic representation of the underlying physical phenomena. The resulting MPC effectively tracks set points, rejects realistic process disturbances and is shown to outperform conventional PID control. © 2017 American Institute of Chemical Engineers AIChE J , 64: 1234–1245, 2018