Model Analysis for the Implementation of a Fast Model Predictive Control Scheme on the Absorption/Stripping CO2Capture Plants

The purpose of this paper is to investigate the possible implementation of the Fast model predictive control (MPC) scheme for chemical systems. Due to the difficulties associated with complicated dynamic behavior and model sensitivity, which results in considerable offsets, the Fast MPC controller h...

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Main Authors: Sultan, T., Zabiri, H., Shahbaz, M., Maulud, A.S.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.29106 /
Published: American Chemical Society 2021
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126121152&doi=10.1021%2facsomega.1c05974&partnerID=40&md5=6d2098d256badb8a0af7684436676b68
http://eprints.utp.edu.my/29106/
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spelling utp-eprints.291062022-03-25T00:57:04Z Model Analysis for the Implementation of a Fast Model Predictive Control Scheme on the Absorption/Stripping CO2Capture Plants Sultan, T. Zabiri, H. Shahbaz, M. Maulud, A.S. The purpose of this paper is to investigate the possible implementation of the Fast model predictive control (MPC) scheme for chemical systems. Due to the difficulties associated with complicated dynamic behavior and model sensitivity, which results in considerable offsets, the Fast MPC controller has not been implemented on the CO2 capture plant based on the absorption/stripping system. The main objective of this work is to evaluate the most appropriate model for implementing the Fast MPC control strategy, which results in fast output responses, negligible offsets, and minimum errors. The steady-state and dynamic simulation models of the CO2 capture plant are designed in Aspen PLUS. In the System Identification Toolbox, multiple state-space models are identified to achieve a highly accurate model for the Fast MPC controller. The Fast MPC controller is then implemented to evaluate the performance under a setpoint tracking mode with ±5 and ±15 step changes. The results showed that the Fast MPC based on the state-space prediction focus model has on average 7.9 times lower offset than the simulation focus model and 10.4 times lower integral absolute error values. The comparison study concluded that the Fast MPC control strategy performs efficiently using prediction-based focus state-space models for CO2 capture plants using the absorption/stripping system with minimum offsets and errors. © 2021 American Chemical Society. All rights reserved. American Chemical Society 2021 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126121152&doi=10.1021%2facsomega.1c05974&partnerID=40&md5=6d2098d256badb8a0af7684436676b68 Sultan, T. and Zabiri, H. and Shahbaz, M. and Maulud, A.S. (2021) Model Analysis for the Implementation of a Fast Model Predictive Control Scheme on the Absorption/Stripping CO2Capture Plants. ACS Omega . http://eprints.utp.edu.my/29106/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description The purpose of this paper is to investigate the possible implementation of the Fast model predictive control (MPC) scheme for chemical systems. Due to the difficulties associated with complicated dynamic behavior and model sensitivity, which results in considerable offsets, the Fast MPC controller has not been implemented on the CO2 capture plant based on the absorption/stripping system. The main objective of this work is to evaluate the most appropriate model for implementing the Fast MPC control strategy, which results in fast output responses, negligible offsets, and minimum errors. The steady-state and dynamic simulation models of the CO2 capture plant are designed in Aspen PLUS. In the System Identification Toolbox, multiple state-space models are identified to achieve a highly accurate model for the Fast MPC controller. The Fast MPC controller is then implemented to evaluate the performance under a setpoint tracking mode with ±5 and ±15 step changes. The results showed that the Fast MPC based on the state-space prediction focus model has on average 7.9 times lower offset than the simulation focus model and 10.4 times lower integral absolute error values. The comparison study concluded that the Fast MPC control strategy performs efficiently using prediction-based focus state-space models for CO2 capture plants using the absorption/stripping system with minimum offsets and errors. © 2021 American Chemical Society. All rights reserved.
format Article
author Sultan, T.
Zabiri, H.
Shahbaz, M.
Maulud, A.S.
spellingShingle Sultan, T.
Zabiri, H.
Shahbaz, M.
Maulud, A.S.
Model Analysis for the Implementation of a Fast Model Predictive Control Scheme on the Absorption/Stripping CO2Capture Plants
author_sort Sultan, T.
title Model Analysis for the Implementation of a Fast Model Predictive Control Scheme on the Absorption/Stripping CO2Capture Plants
title_short Model Analysis for the Implementation of a Fast Model Predictive Control Scheme on the Absorption/Stripping CO2Capture Plants
title_full Model Analysis for the Implementation of a Fast Model Predictive Control Scheme on the Absorption/Stripping CO2Capture Plants
title_fullStr Model Analysis for the Implementation of a Fast Model Predictive Control Scheme on the Absorption/Stripping CO2Capture Plants
title_full_unstemmed Model Analysis for the Implementation of a Fast Model Predictive Control Scheme on the Absorption/Stripping CO2Capture Plants
title_sort model analysis for the implementation of a fast model predictive control scheme on the absorption/stripping co2capture plants
publisher American Chemical Society
publishDate 2021
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126121152&doi=10.1021%2facsomega.1c05974&partnerID=40&md5=6d2098d256badb8a0af7684436676b68
http://eprints.utp.edu.my/29106/
_version_ 1741197189485428736
score 11.62408

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