A practical study of distillation column identification and model predictive control

Distillation is a widely used separation technique in the process industries. The control of distillation column is one important problem of process control and the improved distillation control will make significant economic advantages. However, as a complex multivariable non-linear system, it dese...

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Main Author:	Tang, Yue.
Other Authors:	Ling, Keck Voon
Format:	Theses and Dissertations
Language:	English
Published:	2008
Subjects:	DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering
Online Access:	http://hdl.handle.net/10356/13218
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Institution:	Nanyang Technological University
Language:	English

id	sg-ntu-dr.10356-13218
record_format	dspace
spelling	sg-ntu-dr.10356-132182023-07-04T15:17:31Z A practical study of distillation column identification and model predictive control Tang, Yue. Ling, Keck Voon School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering Distillation is a widely used separation technique in the process industries. The control of distillation column is one important problem of process control and the improved distillation control will make significant economic advantages. However, as a complex multivariable non-linear system, it deserves more studies by both academic and industrial researchers. Among the often-used advanced techniques for process control, Model Predictive Control (MPC) offers a good balance between performance and simplicity. But the specific properties of distillation column, e.g., multivariable interaction, large disturbances, tight constraints and model uncertainties, make the distillation MPC design different with other control systems. Master of Engineering 2008-10-20T07:19:36Z 2008-10-20T07:19:36Z 1999 1999 Thesis http://hdl.handle.net/10356/13218 en 140 p. application/pdf
institution	Nanyang Technological University
building	NTU Library
continent	Asia
country	Singapore Singapore
content_provider	NTU Library
collection	DR-NTU
language	English
topic	DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering
spellingShingle	DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering Tang, Yue. A practical study of distillation column identification and model predictive control
description	Distillation is a widely used separation technique in the process industries. The control of distillation column is one important problem of process control and the improved distillation control will make significant economic advantages. However, as a complex multivariable non-linear system, it deserves more studies by both academic and industrial researchers. Among the often-used advanced techniques for process control, Model Predictive Control (MPC) offers a good balance between performance and simplicity. But the specific properties of distillation column, e.g., multivariable interaction, large disturbances, tight constraints and model uncertainties, make the distillation MPC design different with other control systems.
author2	Ling, Keck Voon
author_facet	Ling, Keck Voon Tang, Yue.
format	Theses and Dissertations
author	Tang, Yue.
author_sort	Tang, Yue.
title	A practical study of distillation column identification and model predictive control
title_short	A practical study of distillation column identification and model predictive control
title_full	A practical study of distillation column identification and model predictive control
title_fullStr	A practical study of distillation column identification and model predictive control
title_full_unstemmed	A practical study of distillation column identification and model predictive control
title_sort	practical study of distillation column identification and model predictive control
publishDate	2008
url	http://hdl.handle.net/10356/13218
_version_	1772826014938824704

A practical study of distillation column identification and model predictive control

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