Implementation of Neural Control for Continuous Stirred Tank Reactor (CSTR

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...

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Autor principal:	Karima M. Putrus
Formato:	article
Lenguaje:	EN
Publicado:	Al-Khwarizmi College of Engineering – University of Baghdad 2011
Materias:	Predictive control PID control neural network nonlinear control continuous stirred tank reactor. Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/efe20ec004294843898c4f22f5857b85
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spelling	oai:doaj.org-article:efe20ec004294843898c4f22f5857b852021-12-02T04:16:48ZImplementation of Neural Control for Continuous Stirred Tank Reactor (CSTR1818-1171https://doaj.org/article/efe20ec004294843898c4f22f5857b852011-01-01T00:00:00Zhttp://www.iasj.net/iasj?func=fulltext&aId=2219https://doaj.org/toc/1818-1171In 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.<br />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. <br />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.<br />Karima M. PutrusAl-Khwarizmi College of Engineering – University of BaghdadarticlePredictive controlPID controlneural networknonlinear controlcontinuous stirred tank reactor.Chemical engineeringTP155-156Engineering (General). Civil engineering (General)TA1-2040ENAl-Khawarizmi Engineering Journal, Vol 7, Iss 1, Pp 39-55 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Predictive control PID control neural network nonlinear control continuous stirred tank reactor. Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Predictive control PID control neural network nonlinear control continuous stirred tank reactor. 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.<br />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. <br />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.<br />
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	2011
url	https://doaj.org/article/efe20ec004294843898c4f22f5857b85
work_keys_str_mv	AT karimamputrus implementationofneuralcontrolforcontinuousstirredtankreactorcstr
_version_	1718401342689509376

Implementation of Neural Control for Continuous Stirred Tank Reactor (CSTR

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