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.

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.

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.

Fault Diagnosis of Chemical Process Based on ACO-BP Neural Network

2012 ◽  
Vol 217-219 ◽  
pp. 2722-2725
Author(s):  
Jian Xue Chen
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Hybrid Algorithm ◽  
Chemical Process ◽  
Back Propagation ◽  
Bp Algorithm ◽  
Continuous Stirred Tank Reactor ◽  
The Neural Network ◽  
Continuous Stirred Tank

Fault diagnosis is an important problem in the process of chemical industry and the artificial neural network is widely applied in fault diagnosis of chemical process. A hybrid algorithm combining ant colony optimization (ACO) algorithm with back-propagation (BP) algorithm, also referred to as ACO-BP algorithm, is proposed to train the neural network weights and thresholds. The basic theory and steps of ACO-BP algorithm are given, and applied in fault diagnosis of the continuous stirred-tank reactor (CSTR). Experimental results prove that ACO-BP algorithm has good fault diagnosis precision, and it can detect the fault in CSTR promptly and effectively.

Control of Continuous Stirred Tank Reactor Using Artificial Neural Networks Based Predictive Control

2012 ◽  
Vol 550-553 ◽  
pp. 2908-2912 ◽  
Author(s):  
Ginuga Prabhaker Reddy ◽  
G. Radhika ◽  
K Anil
Keyword(s):  
Neural Network ◽  
Neural Network Model ◽  
Stirred Tank ◽  
Continuous Stirred Tank Reactor ◽  
Model Predictive Controller ◽  
Stirred Tank Reactor ◽  
Tank Reactor ◽  
The Neural Network ◽  
Predictive Controller ◽  
Continuous Stirred Tank

In this work, a Neural network based predictive controller is analyzed to a non linear continuous stirred tank reactor (CSTR) carrying out series and parallel reactions: A→B→C and 2A→D. In the first step, the neural network model of continuous stirred tank reactor is obtained by Levenburg- Marquard training. The data for the training the network is generated using state space model of continuous stirred tank reactor. The neural network model of continuous stirred tank reactor is used in model predictive controller design. The performance of present neural network based model predictive controller (NNMPC) is evaluated through simulations for servo & regulatory problems of CSTR. The performance of neural network based predictive controller is found to be superior than conventional PI controller for setpoint tracking problems.

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 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.

Constant Cutting-Power Control of Shearer Based on Neural Network Model Predictive Control

2013 ◽  
Vol 823 ◽  
pp. 340-344
Author(s):  
Yuan Hua Zhou ◽  
Hong Wei Ma ◽  
Hai Yan Wu ◽  
You Jun Zhao
Keyword(s):  
Neural Network ◽  
Power Control ◽  
Network Model ◽  
Predictive Control ◽  
Neural Network Model ◽  
Control Method ◽  
Cutting Power ◽  
Nonlinear Predictive Control ◽  
The Neural Network ◽  
And Control

To solve the problem of constant power control of shearer cutting machine, the nonlinear predictive control method based on Neural Network was proposed in this thesis. In the method, the cutting current was used to identify the cutting load, and the Neural Network was used to predict and control the traction speed. A Neural Network model was built by the current and speed to control the cutting power of shearer. In MATLAB, the field data was used to simulate and the simulation verify the proposed scheme is better than PID method.

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