Model predictive control of a CSTR: A hybrid modeling approach

2010 ◽  
Vol 64 (3) ◽  
Author(s):  
Michal Kvasnica ◽  
Martin Herceg ◽  
Ľuboš Čirka ◽  
Miroslav Fikar

AbstractThis paper presents a case study of model predictive control (MPC) applied to a continuous stirred tank reactor (CSTR). It is proposed to approximate nonlinear behavior of a plant by several local linear models, enabling a piecewise affine (PWA) description of the model used to predict and optimize future evolution of the reactor behavior. Main advantage of the PWA model over traditional approaches based on single linearization is a significant increase of model accuracy which leads to a better control quality. It is also illustrated that, by adopting the PWA modeling framework, MPC strategy can be implemented using significantly less computational power compared to nonlinear MPC setups.

2002 ◽  
Vol 35 (1) ◽  
pp. 91-96
Author(s):  
Marko Lepetič ◽  
Igor Škrjanc ◽  
José Luis Figueroa ◽  
Drago Matko ◽  
Sašo Blažič

2011 ◽  
Vol 2011 ◽  
pp. 1-17 ◽  
Author(s):  
Nádson Murilo Nascimento Lima ◽  
Lamia Zuñiga Liñan ◽  
Flavio Manenti ◽  
Rubens Maciel Filho ◽  
Marcelo Embiruçu ◽  
...  

A model-based predictive control system is designed for a copolymerization reactor. These processes typically have such a high nonlinear dynamic behavior to make practically ineffective the conventional control techniques, still so widespread in process and polymer industries. A predictive controller is adopted in this work, given the success this family of controllers is having in many chemical processes and oil refineries, especially due to their possibility of including bounds on both manipulated and controlled variables. The solution copolymerization of methyl methacrylate with vinyl acetate in a continuous stirred tank reactor is considered as an industrial case study for the analysis of the predictive control robustness in the field of petrochemical and polymer production. Both regulatory and servo problems scenarios are considered to check tangible benefits deriving from model-based predictive controller implementation.


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