Non-linear State Estimation for Continuous Stirred Tank Reactor using Neural Network State Filter

2006 ◽  
Author(s):  
K. Srinivasan ◽  
J. Prakash
Keyword(s):  
Neural Network ◽  
State Estimation ◽  
Stirred Tank ◽  
Continuous Stirred Tank Reactor ◽  
Stirred Tank Reactor ◽  
Tank Reactor ◽  
Network State ◽  
Non Linear ◽  
Continuous Stirred Tank ◽  
Linear State

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.

scholarly journals Fault detection and diagnosis for continuous stirred tank reactor using neural network

1970 ◽  
Vol 6 (2) ◽  
pp. 66-74 ◽  
Author(s):  
Ribhan Zafira Abdul Rahman ◽  
Azura Che Soh ◽  
Noor Fadzlina Binti Muhammad
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Fault Detection And Diagnosis ◽  
Stirred Tank ◽  
Continuous Stirred Tank Reactor ◽  
Stirred Tank Reactor ◽  
Tank Reactor ◽  
Engineering And Technology ◽  
Continuous Stirred Tank ◽  
Detection And Diagnosis

The paper focuses on the application of neural network techniques in fault detection and diagnosis. The objective of this paper is to detect and diagnose the faults to a continuous stirred tank reactor (CSTR). Fault detection is performed by using the error signals, where when error signal is zero or nearly zero, the system is in normal condition, and when the fault occurs, error signals should distinctively diverge from zero. The fault diagnosis is performed by identifying the amplitude error of the CSTR output error. Keywords: Fault Detection and Diagnosis; Neural Network; CSTR  DOI: 10.3126/kuset.v6i2.4014Kathmandu University Journal of Science, Engineering and Technology Vol.6. No II, November, 2010, pp.66-74

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