Implementation of Neural Control for Continuous Stirred Tank Reactor (CSTR)

Implementation of Neural Control for Continuous Stirred Tank Reactor (CSTR)

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In this paper a dynamic behavior and control of  a jacketed continuous stirred tank reactor (CSTR)  is developed using different control strategies, conventional feedback control (PI and PID), and neural network (NARMA-L2, and NN Predictive) control. The dynamic model for CSTR process is described...

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Autor principal: Karima M. Putrus
Formato: article
Lenguaje:EN
Publicado: Al-Khwarizmi College of Engineering – University of Baghdad 2019
Materias:
Chemical engineering
TP155-156
Engineering (General). Civil engineering (General)
TA1-2040
Acceso en línea:https://doaj.org/article/6ac388c963d64b1fbad7347d72502aa6
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spelling oai:doaj.org-article:6ac388c963d64b1fbad7347d72502aa62021-12-02T07:32:58ZImplementation of Neural Control for Continuous Stirred Tank Reactor (CSTR)1818-11712312-0789https://doaj.org/article/6ac388c963d64b1fbad7347d72502aa62019-03-01T00:00:00Zhttp://alkej.uobaghdad.edu.iq/index.php/alkej/article/view/469https://doaj.org/toc/1818-1171https://doaj.org/toc/2312-0789 In this paper a dynamic behavior and control of  a jacketed continuous stirred tank reactor (CSTR)  is developed using different control strategies, conventional feedback control (PI and PID), and neural network (NARMA-L2, and NN Predictive) control. The dynamic model for CSTR process is described by a first order lag system with dead time. The optimum tuning of control parameters are found by two different methods; Frequency Analysis Curve method (Bode diagram) and Process Reaction Curve using the mean of Square Error (MSE) method. It is found that the Process Reaction Curve method is better than the Frequency Analysis Curve method and PID feedback controller is better than PI feedback controller. The results show that the artificial neural network is the best method to control the CSTR process and it is better than the conventional method because it has smaller value of mean square error (MSE).   MATLAB program is used as a tool of solution for all cases used in the present work. Karima M. PutrusAl-Khwarizmi College of Engineering – University of BaghdadarticleChemical engineeringTP155-156Engineering (General). Civil engineering (General)TA1-2040ENAl-Khawarizmi Engineering Journal, Vol 7, Iss 1 (2019)
institution DOAJ
collection DOAJ
language EN
topic Chemical engineering
TP155-156
Engineering (General). Civil engineering (General)
TA1-2040
spellingShingle Chemical engineering
TP155-156
Engineering (General). Civil engineering (General)
TA1-2040
Karima M. Putrus
Implementation of Neural Control for Continuous Stirred Tank Reactor (CSTR)
description In this paper a dynamic behavior and control of  a jacketed continuous stirred tank reactor (CSTR)  is developed using different control strategies, conventional feedback control (PI and PID), and neural network (NARMA-L2, and NN Predictive) control. The dynamic model for CSTR process is described by a first order lag system with dead time. The optimum tuning of control parameters are found by two different methods; Frequency Analysis Curve method (Bode diagram) and Process Reaction Curve using the mean of Square Error (MSE) method. It is found that the Process Reaction Curve method is better than the Frequency Analysis Curve method and PID feedback controller is better than PI feedback controller. The results show that the artificial neural network is the best method to control the CSTR process and it is better than the conventional method because it has smaller value of mean square error (MSE).   MATLAB program is used as a tool of solution for all cases used in the present work.
format article
author Karima M. Putrus
author_facet Karima M. Putrus
author_sort Karima M. Putrus
title Implementation of Neural Control for Continuous Stirred Tank Reactor (CSTR)
title_short Implementation of Neural Control for Continuous Stirred Tank Reactor (CSTR)
title_full Implementation of Neural Control for Continuous Stirred Tank Reactor (CSTR)
title_fullStr Implementation of Neural Control for Continuous Stirred Tank Reactor (CSTR)
title_full_unstemmed Implementation of Neural Control for Continuous Stirred Tank Reactor (CSTR)
title_sort implementation of neural control for continuous stirred tank reactor (cstr)
publisher Al-Khwarizmi College of Engineering – University of Baghdad
publishDate 2019
url https://doaj.org/article/6ac388c963d64b1fbad7347d72502aa6
work_keys_str_mv AT karimamputrus implementationofneuralcontrolforcontinuousstirredtankreactorcstr
_version_ 1718399342322122752

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