Control of Gas Dehydration Unit Using Multivariable Model Predictive Control (MMPC) to Obtain More Optimal Control Performance

Control of Gas Dehydration Unit Using Multivariable Model Predictive Control (MMPC) to Obtain More Optimal Control Performance

A multivariable model predictive control (MMPC) is proposed to improve a control performance in Gas dehydration process. The FOPDT models are used to build an MMPC derived from the selected controlled variables (CV) and manipulated variables (MV). A set point (SP) tracking is used to test the contro...

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Main Authors: Wahid Abdul, Mauricio Rickson, Maro Naufal Syafiq
Format: Article
Language:English
Published: EDP Sciences 2018-01-01
Series:E3S Web of Conferences
Online Access:https://doi.org/10.1051/e3sconf/20186703013
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spelling doaj-5a95bdcca7c942eab5e189fa29babae02021-02-02T06:28:33ZengEDP SciencesE3S Web of Conferences2267-12422018-01-01670301310.1051/e3sconf/20186703013e3sconf_i-trec2018_03013Control of Gas Dehydration Unit Using Multivariable Model Predictive Control (MMPC) to Obtain More Optimal Control PerformanceWahid AbdulMauricio RicksonMaro Naufal SyafiqA multivariable model predictive control (MMPC) is proposed to improve a control performance in Gas dehydration process. The FOPDT models are used to build an MMPC derived from the selected controlled variables (CV) and manipulated variables (MV). A set point (SP) tracking is used to test the control performance, with proportional-integral controller (PI) as a comparison. As an indicator of the control performance is the integral of square error (ISE). The result is a TITO (two-inputs two-outputs) MMPC, with sweet gas flow rate and heat duty of heater as MVs, and feed pressure and heater temperature as CVs, respectively. In the SP tracking test, MMPC showed better control performance than the PI controller with 11.29% performance improvement (pressure control) and 16.39% (temperature control).https://doi.org/10.1051/e3sconf/20186703013
collection DOAJ
language English
format Article
sources DOAJ
author Wahid Abdul
Mauricio Rickson
Maro Naufal Syafiq
spellingShingle Wahid Abdul
Mauricio Rickson
Maro Naufal Syafiq
Control of Gas Dehydration Unit Using Multivariable Model Predictive Control (MMPC) to Obtain More Optimal Control Performance
E3S Web of Conferences
author_facet Wahid Abdul
Mauricio Rickson
Maro Naufal Syafiq
author_sort Wahid Abdul
title Control of Gas Dehydration Unit Using Multivariable Model Predictive Control (MMPC) to Obtain More Optimal Control Performance
title_short Control of Gas Dehydration Unit Using Multivariable Model Predictive Control (MMPC) to Obtain More Optimal Control Performance
title_full Control of Gas Dehydration Unit Using Multivariable Model Predictive Control (MMPC) to Obtain More Optimal Control Performance
title_fullStr Control of Gas Dehydration Unit Using Multivariable Model Predictive Control (MMPC) to Obtain More Optimal Control Performance
title_full_unstemmed Control of Gas Dehydration Unit Using Multivariable Model Predictive Control (MMPC) to Obtain More Optimal Control Performance
title_sort control of gas dehydration unit using multivariable model predictive control (mmpc) to obtain more optimal control performance
publisher EDP Sciences
series E3S Web of Conferences
issn 2267-1242
publishDate 2018-01-01
description A multivariable model predictive control (MMPC) is proposed to improve a control performance in Gas dehydration process. The FOPDT models are used to build an MMPC derived from the selected controlled variables (CV) and manipulated variables (MV). A set point (SP) tracking is used to test the control performance, with proportional-integral controller (PI) as a comparison. As an indicator of the control performance is the integral of square error (ISE). The result is a TITO (two-inputs two-outputs) MMPC, with sweet gas flow rate and heat duty of heater as MVs, and feed pressure and heater temperature as CVs, respectively. In the SP tracking test, MMPC showed better control performance than the PI controller with 11.29% performance improvement (pressure control) and 16.39% (temperature control).
url https://doi.org/10.1051/e3sconf/20186703013
work_keys_str_mv AT wahidabdul controlofgasdehydrationunitusingmultivariablemodelpredictivecontrolmmpctoobtainmoreoptimalcontrolperformance
AT mauriciorickson controlofgasdehydrationunitusingmultivariablemodelpredictivecontrolmmpctoobtainmoreoptimalcontrolperformance
AT maronaufalsyafiq controlofgasdehydrationunitusingmultivariablemodelpredictivecontrolmmpctoobtainmoreoptimalcontrolperformance
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