Robust model-based predictive control of exothermic chemical reactor

2015 ◽  
Vol 69 (10) ◽  
Author(s):  
Juraj Oravec ◽  
Monika Bakošová
Keyword(s):  
Predictive Control ◽  
Chemical Reactor ◽  
Continuous Stirred Tank Reactor ◽  
Total Consumption ◽  
Quality Of Control ◽  
Model Based ◽  
Robust Model ◽  
Continuous Stirred Tank

AbstractA case study of the robust model-based predictive control (MPC) of an exothermic continuous stirred tank reactor (CSTR) with uncertain parameters is presented. Three robust MPC approaches are considered and the simulation results are compared in terms of quality of control performance and total consumption of coolant. The results reveal the main benefits of the considered approaches and confirm that the robust MPC can bring about a reduction in consumption of the coolant.

scholarly journals Advanced Control of a Continuous Solution Copolymerization Process

2011 ◽  
Vol 2011 ◽  
pp. 1-17 ◽  
Author(s):  
Nádson Murilo Nascimento Lima ◽  
Lamia Zuñiga Liñan ◽  
Flavio Manenti ◽  
Rubens Maciel Filho ◽  
Marcelo Embiruçu ◽  
...  
Keyword(s):  
Predictive Control ◽  
Continuous Stirred Tank Reactor ◽  
Industrial Case Study ◽  
Oil Refineries ◽  
Model Based ◽  
Control Robustness ◽  
Advanced Control ◽  
Predictive Controller ◽  
Continuous Stirred Tank

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.

scholarly journals Neural network based explicit MPC for chemical reactor control

2019 ◽  
Vol 12 (2) ◽  
pp. 218-223 ◽  
Author(s):  
Karol Kiš ◽  
Martin Klaučo
Keyword(s):  
Neural Network ◽  
Predictive Control ◽  
Chemical Reactor ◽  
Control Input ◽  
Continuous Stirred Tank Reactor ◽  
The Neural Network ◽  
Approach Training ◽  
Continuous Stirred Tank ◽  
And Control

Abstract In this paper, implementation of deep neural networks applied in process control is presented. In our approach, training of the neural network is based on model predictive control, which is popular for its ability to be tuned by the weighting matrices and for it respecting the system constraints. A neural network that can approximate the MPC behavior by mimicking the control input trajectory while the constraints on states and control input remain unimpaired by the weighting matrices is introduced. This approach is demonstrated in a simulation case study involving a continuous stirred tank reactor where a multi-component chemical reaction takes place.

Model predictive control of a CSTR: A hybrid modeling approach

2010 ◽  
Vol 64 (3) ◽  
Author(s):  
Michal Kvasnica ◽  
Martin Herceg ◽  
Ľuboš Čirka ◽  
Miroslav Fikar
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Linear Models ◽  
Nonlinear Behavior ◽  
Continuous Stirred Tank Reactor ◽  
Modeling Framework ◽  
Future Evolution ◽  
Continuous Stirred Tank ◽  
Traditional Approaches

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.

FUZZY PREDICTIVE CONTROL: CONTINUOUS STIRRED–TANK REACTOR CASE STUDY

2002 ◽  
Vol 35 (1) ◽  
pp. 91-96
Author(s):  
Marko Lepetič ◽  
Igor Škrjanc ◽  
José Luis Figueroa ◽  
Drago Matko ◽  
Sašo Blažič
Keyword(s):  
Predictive Control ◽  
Stirred Tank ◽  
Continuous Stirred Tank Reactor ◽  
Stirred Tank Reactor ◽  
Tank Reactor ◽  
Continuous Stirred Tank ◽  
Fuzzy Predictive Control

scholarly journals Robust MPC of an unstable chemical reactor using the nominal system optimization

2014 ◽  
Vol 7 (2) ◽  
pp. 87-93 ◽  
Author(s):  
Monika Bakošová ◽  
Juraj Oravec
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Chemical Reactor ◽  
System Optimization ◽  
Open Loop ◽  
Continuous Stirred Tank Reactor ◽  
Worst Case ◽  
Nominal System ◽  
Robust Model Predictive Control ◽  
Robust Model

Abstract The continuous stirred-tank reactor with uncertain parameters was stabilized in the open-loop unstable steady state using the robust model predictive control. The gain matrices of the robust state-feedback controller were designed using the nominal system optimization and the quadratic parameter-dependent Lyapunov functions. The controller was verified by simulations using the non-linear model of the reactor and compared with the robust model predictive controller designed using the worst-case system optimization. The values of the quadratic cost function and the consumption of coolant were observed. Both robust model predictive controllers stabilized the reactor despite constrained control inputs and states. The robust model predictive control based on the nominal system optimization improved control responses and decreased the consumption of coolant.

scholarly journals Fast nonlinear model predictive control of a chemical reactor: a random shooting approach

2018 ◽  
Vol 11 (2) ◽  
pp. 175-181 ◽  
Author(s):  
Peter Bakaráč ◽  
Michal Kvasnica
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Chemical Reactor ◽  
Point Of View ◽  
Nonlinear Model Predictive Control ◽  
Continuous Stirred Tank Reactor ◽  
Computational Point ◽  
The One

Abstract This paper presents a fast way of implementing nonlinear model predictive control (NMPC) using the random shooting approach. Instead of calculating the optimal control sequence by solving the NMPC problem as a nonlinear programming (NLP) problem, which is time consuming, a sub-optimal, but feasible, sequence of control inputs is determined randomly. To minimize the induced sub-optimality, numerous random control sequences are selected and the one that yields the smallest cost is selected. By means of a motivating case study we demonstrate that the random shooting-based approach is superior, from a computational point of view, to state-of-the-art NLP solvers, and features a low level of sub-optimality. The case study involves a continuous stirred tank reactor where a fast multi-component chemical reaction takes place.

Model predictive control-based robust stabilization of a chemical reactor

2013 ◽  
Vol 67 (9) ◽  
Author(s):  
Monika Bakošová ◽  
Juraj Oravec ◽  
Katarína Matejičková
Keyword(s):  
Predictive Control ◽  
Robust Stabilization ◽  
Chemical Reactor ◽  
Linear Quadratic ◽  
Stirred Tank Reactors ◽  
Model Based ◽  
Prediction Horizon ◽  
Robust Model ◽  
Integral Action ◽  
Optimal Linear

AbstractThe paper addresses an approach to robust stabilization of chemical continuous stirred tank reactors. State feedback was used for the stabilization and the feedback controller was designed using the robust model-based predictive control algorithm in which the symmetric constraints on input and output variables are taken into account. The known strategy was modified by adding integral action to the controller. Parameters of robust feedback controllers with and without integral action were found as solutions of a constrained optimization problem solved on the infinite prediction horizon. The possibility to stabilize chemical reactors with uncertainty using the robust model-based predictive control has been verified by simulations and compared with the optimal linear quadratic control and the model-based predictive control. The obtained results confirm that the robust model-based predictive control provides better results than other approaches.

Towards Test-Driven and Architecture Model-Based Security and Resilience Engineering

2013 ◽  
pp. 163-188
Author(s):  
Ayda Saidane ◽  
Nicolas Guelfi
Keyword(s):  
Software Systems ◽  
Critical Systems ◽  
Functional Requirements ◽  
Security Testing ◽  
Modeling Framework ◽  
Architecture Model ◽  
Model Based ◽  
Security Modeling

The quality of software systems depends strongly on their architecture. For this reason, taking into account non-functional requirements at architecture level is crucial for the success of the software development process. Early architecture model validation facilitates the detection and correction of design errors. In this research, the authors are interested in security critical systems, which require a reliable validation process. So far, they are missing security-testing approaches providing an appropriate compromise between software quality and development cost while satisfying certification and audit procedures requirements through automated and documented validation activities. In this chapter, the authors propose a novel test-driven and architecture model-based security engineering approach for resilient systems. It consists of a test-driven security modeling framework and a test based validation approach. The assessment of the security requirement satisfaction is based on the test traces analysis. Throughout this study, the authors illustrate the approach using a client server architecture case study.

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