Nonlinear continuous stirred tank reactor (CSTR) identification and control using recurrent neural network trained Shuffled Frog Leaping Algorithm

2011 ◽  
Author(s):  
M. Shahriari-kahkeshi ◽  
J. Askari
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Stirred Tank ◽  
Continuous Stirred Tank Reactor ◽  
Shuffled Frog Leaping Algorithm ◽  
Stirred Tank Reactor ◽  
Tank Reactor ◽  
Shuffled Frog Leaping ◽  
Continuous Stirred Tank ◽  
And Control

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.

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