Application of Multivariable Model Predictive Control (4x4) for Dimethyl Ether Purification from Methanol

Multi-variable model predictive control (MMPC) was used to control the dimethyl ether (DME) purification process of methanol in the production of DME from synthesis gas. The use of MMPC aims to capture the phenomenon of the interaction between the variables in the process in order to improve the...

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Main Authors: Abdul Wahid, Wildan Raafi Utomo
Format: Article
Language:English
Published: Universitas Indonesia 2019-11-01
Series:International Journal of Technology
Subjects:
dme
Online Access:http://ijtech.eng.ui.ac.id/article/view/3631
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spelling doaj-2d651b3a8f1f4bfaae041f65cf54fa092020-11-25T01:35:55ZengUniversitas IndonesiaInternational Journal of Technology2086-96142087-21002019-11-011061211121910.14716/ijtech.v10i6.36313631Application of Multivariable Model Predictive Control (4x4) for Dimethyl Ether Purification from MethanolAbdul Wahid0Wildan Raafi Utomo1Department of Chemical Engineering, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, Depok 16424, IndonesiaDepartment of Chemical Engineering, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, Depok 16424, IndonesiaMulti-variable model predictive control (MMPC) was used to control the dimethyl ether (DME) purification process of methanol in the production of DME from synthesis gas. The use of MMPC aims to capture the phenomenon of the interaction between the variables in the process in order to improve the control performance. As the process comprises four input variables and four output variables, MMPC (4×4) is used in this study. The inter-variable interaction is shown in a 4×4 matrix, where each matrix element is a first-order plus dead-time (FOPDT) model. MMPC (4×4) was tested by changing the set point (SP) and disturbance rejection. The control performance indicators used are integral absolute error (IAE) and integral square error (ISE) and, as a comparison, the control performance of the single-input single-output (SISO) model predictive control (MPC). The results show that MMPC (4×4) is better than MPC in both IAE and ISE. In terms of SP change, MMPC (4×4) is able to significantly improve the control performance of MPC, by 78% (IAE) and 90% (ISE). Whereas in the disturbance rejection testing, the improvements in control performance were 58 % (IAE) and 81% (ISE).http://ijtech.eng.ui.ac.id/article/view/3631dmemethanolmulti-variablepredictivepurification
collection DOAJ
language English
format Article
sources DOAJ
author Abdul Wahid
Wildan Raafi Utomo
spellingShingle Abdul Wahid
Wildan Raafi Utomo
Application of Multivariable Model Predictive Control (4x4) for Dimethyl Ether Purification from Methanol
International Journal of Technology
dme
methanol
multi-variable
predictive
purification
author_facet Abdul Wahid
Wildan Raafi Utomo
author_sort Abdul Wahid
title Application of Multivariable Model Predictive Control (4x4) for Dimethyl Ether Purification from Methanol
title_short Application of Multivariable Model Predictive Control (4x4) for Dimethyl Ether Purification from Methanol
title_full Application of Multivariable Model Predictive Control (4x4) for Dimethyl Ether Purification from Methanol
title_fullStr Application of Multivariable Model Predictive Control (4x4) for Dimethyl Ether Purification from Methanol
title_full_unstemmed Application of Multivariable Model Predictive Control (4x4) for Dimethyl Ether Purification from Methanol
title_sort application of multivariable model predictive control (4x4) for dimethyl ether purification from methanol
publisher Universitas Indonesia
series International Journal of Technology
issn 2086-9614
2087-2100
publishDate 2019-11-01
description Multi-variable model predictive control (MMPC) was used to control the dimethyl ether (DME) purification process of methanol in the production of DME from synthesis gas. The use of MMPC aims to capture the phenomenon of the interaction between the variables in the process in order to improve the control performance. As the process comprises four input variables and four output variables, MMPC (4×4) is used in this study. The inter-variable interaction is shown in a 4×4 matrix, where each matrix element is a first-order plus dead-time (FOPDT) model. MMPC (4×4) was tested by changing the set point (SP) and disturbance rejection. The control performance indicators used are integral absolute error (IAE) and integral square error (ISE) and, as a comparison, the control performance of the single-input single-output (SISO) model predictive control (MPC). The results show that MMPC (4×4) is better than MPC in both IAE and ISE. In terms of SP change, MMPC (4×4) is able to significantly improve the control performance of MPC, by 78% (IAE) and 90% (ISE). Whereas in the disturbance rejection testing, the improvements in control performance were 58 % (IAE) and 81% (ISE).
topic dme
methanol
multi-variable
predictive
purification
url http://ijtech.eng.ui.ac.id/article/view/3631
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AT wildanraafiutomo applicationofmultivariablemodelpredictivecontrol4x4fordimethyletherpurificationfrommethanol
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