Multivariable control of a debutanizer column using equation based artificial neural network model inverse control strategies

The debutanizer column is an important unit operation in petroleum refining industries as it is the main column to produce liquefied petroleum gas as its top product and light naphtha as its bottom product. This system is difficult to handle from a control standpoint due to its nonlinear behavior, m...

Full description

Main Authors: Ramli, N.M., Hussain, M.A., Jan, B.M.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.30849 /
Published: Elsevier B.V. 2016
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84959421442&doi=10.1016%2fj.neucom.2016.02.026&partnerID=40&md5=54e40fcec4ce689ae45c2e42858f54f5
http://eprints.utp.edu.my/30849/
Tags: Add Tag
No Tags, Be the first to tag this record!
id utp-eprints.30849
recordtype eprints
spelling utp-eprints.308492022-03-25T07:39:13Z Multivariable control of a debutanizer column using equation based artificial neural network model inverse control strategies Ramli, N.M. Hussain, M.A. Jan, B.M. The debutanizer column is an important unit operation in petroleum refining industries as it is the main column to produce liquefied petroleum gas as its top product and light naphtha as its bottom product. This system is difficult to handle from a control standpoint due to its nonlinear behavior, multivariable interaction and existence of numerous constraints on both its manipulated and state variable. Neural network techniques have been increasingly used for a wide variety of applications where statistical methods have been traditionally employed. In this work we propose to use an equation based MIMO (Multi Input Multi Output) neural network based multivariable control strategy to control the top and bottom temperatures of the column simultaneously, while manipulating the reflux and reboiler flow rates respectively. This equation based neural network model represented by a multivariable equation, instead of the normal black box structure, has the advantage of being robust in nature while being easier to interpret in terms of its input output variables. It is implemented for set point changes and disturbance changes and the results show that the neural network based model method in the direct inverse and internal model approach performs better than the conventional PID method in both cases. © 2016 Elsevier B.V. Elsevier B.V. 2016 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-84959421442&doi=10.1016%2fj.neucom.2016.02.026&partnerID=40&md5=54e40fcec4ce689ae45c2e42858f54f5 Ramli, N.M. and Hussain, M.A. and Jan, B.M. (2016) Multivariable control of a debutanizer column using equation based artificial neural network model inverse control strategies. Neurocomputing, 194 . pp. 135-150. http://eprints.utp.edu.my/30849/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description The debutanizer column is an important unit operation in petroleum refining industries as it is the main column to produce liquefied petroleum gas as its top product and light naphtha as its bottom product. This system is difficult to handle from a control standpoint due to its nonlinear behavior, multivariable interaction and existence of numerous constraints on both its manipulated and state variable. Neural network techniques have been increasingly used for a wide variety of applications where statistical methods have been traditionally employed. In this work we propose to use an equation based MIMO (Multi Input Multi Output) neural network based multivariable control strategy to control the top and bottom temperatures of the column simultaneously, while manipulating the reflux and reboiler flow rates respectively. This equation based neural network model represented by a multivariable equation, instead of the normal black box structure, has the advantage of being robust in nature while being easier to interpret in terms of its input output variables. It is implemented for set point changes and disturbance changes and the results show that the neural network based model method in the direct inverse and internal model approach performs better than the conventional PID method in both cases. © 2016 Elsevier B.V.
format Article
author Ramli, N.M.
Hussain, M.A.
Jan, B.M.
spellingShingle Ramli, N.M.
Hussain, M.A.
Jan, B.M.
Multivariable control of a debutanizer column using equation based artificial neural network model inverse control strategies
author_sort Ramli, N.M.
title Multivariable control of a debutanizer column using equation based artificial neural network model inverse control strategies
title_short Multivariable control of a debutanizer column using equation based artificial neural network model inverse control strategies
title_full Multivariable control of a debutanizer column using equation based artificial neural network model inverse control strategies
title_fullStr Multivariable control of a debutanizer column using equation based artificial neural network model inverse control strategies
title_full_unstemmed Multivariable control of a debutanizer column using equation based artificial neural network model inverse control strategies
title_sort multivariable control of a debutanizer column using equation based artificial neural network model inverse control strategies
publisher Elsevier B.V.
publishDate 2016
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-84959421442&doi=10.1016%2fj.neucom.2016.02.026&partnerID=40&md5=54e40fcec4ce689ae45c2e42858f54f5
http://eprints.utp.edu.my/30849/
_version_ 1741197477174837248
score 11.62408

Similar Items