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Optimal operation of a process by integrating dynamic economic optimization and model predictive control formulated with empirical model

In advanced control, a control target tracks the set points and tends to achieve optimal operation of a process. Model predictive control (MPC) is used to track the set points. When the set points correspond to an optimum economic trajectory that is sent from an economic layer, the process will be g...

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Main Authors: Tuan, T.T., Tufa, L.D., Mutalib, M.I.A., Ramli, N.M.
Format: Article
Published: Polish Academy of Sciences 2018
Online Access:http://scholars.utp.edu.my/id/eprint/21889/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046713070&doi=10.24425%2f119076&partnerID=40&md5=1c9a7d0e5a5285ef9edf5e11180106c4
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spelling oai:scholars.utp.edu.my:218892023-01-04T02:18:21Z http://scholars.utp.edu.my/id/eprint/21889/ Optimal operation of a process by integrating dynamic economic optimization and model predictive control formulated with empirical model Tuan, T.T. Tufa, L.D. Mutalib, M.I.A. Ramli, N.M. In advanced control, a control target tracks the set points and tends to achieve optimal operation of a process. Model predictive control (MPC) is used to track the set points. When the set points correspond to an optimum economic trajectory that is sent from an economic layer, the process will be gradually reaching the optimal operation. This study proposes the integration of an economic layer and MPC layer to solve the problem of different time scale and unreachable set points. Both layers require dynamic models that are subject to objective functions. The prediction output of a model is not always asymptotically equal to the measured output of a process. Therefore, Kalman filter is proposed as a state feedback to the two-layer integration. The proposed controller only considers the linear empirical model and the inherent model is identified by system identification, which is assumed to be an ample representation of the process. A depropanizer process case study has been used for demonstration of the proposed technique. The result shows that the proposed controller tends to improve the profit of the process smoothly and continuously, until the process reaches an asymptotically maximum profit point. © 2018 Institute of Automatic Control - Silesian University of Technology. All rights reserved. Polish Academy of Sciences 2018 Article PeerReviewed Tuan, T.T. and Tufa, L.D. and Mutalib, M.I.A. and Ramli, N.M. (2018) Optimal operation of a process by integrating dynamic economic optimization and model predictive control formulated with empirical model. Archives of Control Sciences, 28 (1). pp. 35-50. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046713070&doi=10.24425%2f119076&partnerID=40&md5=1c9a7d0e5a5285ef9edf5e11180106c4
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description In advanced control, a control target tracks the set points and tends to achieve optimal operation of a process. Model predictive control (MPC) is used to track the set points. When the set points correspond to an optimum economic trajectory that is sent from an economic layer, the process will be gradually reaching the optimal operation. This study proposes the integration of an economic layer and MPC layer to solve the problem of different time scale and unreachable set points. Both layers require dynamic models that are subject to objective functions. The prediction output of a model is not always asymptotically equal to the measured output of a process. Therefore, Kalman filter is proposed as a state feedback to the two-layer integration. The proposed controller only considers the linear empirical model and the inherent model is identified by system identification, which is assumed to be an ample representation of the process. A depropanizer process case study has been used for demonstration of the proposed technique. The result shows that the proposed controller tends to improve the profit of the process smoothly and continuously, until the process reaches an asymptotically maximum profit point. © 2018 Institute of Automatic Control - Silesian University of Technology. All rights reserved.
format Article
author Tuan, T.T.
Tufa, L.D.
Mutalib, M.I.A.
Ramli, N.M.
spellingShingle Tuan, T.T.
Tufa, L.D.
Mutalib, M.I.A.
Ramli, N.M.
Optimal operation of a process by integrating dynamic economic optimization and model predictive control formulated with empirical model
author_facet Tuan, T.T.
Tufa, L.D.
Mutalib, M.I.A.
Ramli, N.M.
author_sort Tuan, T.T.
title Optimal operation of a process by integrating dynamic economic optimization and model predictive control formulated with empirical model
title_short Optimal operation of a process by integrating dynamic economic optimization and model predictive control formulated with empirical model
title_full Optimal operation of a process by integrating dynamic economic optimization and model predictive control formulated with empirical model
title_fullStr Optimal operation of a process by integrating dynamic economic optimization and model predictive control formulated with empirical model
title_full_unstemmed Optimal operation of a process by integrating dynamic economic optimization and model predictive control formulated with empirical model
title_sort optimal operation of a process by integrating dynamic economic optimization and model predictive control formulated with empirical model
publisher Polish Academy of Sciences
publishDate 2018
url http://scholars.utp.edu.my/id/eprint/21889/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046713070&doi=10.24425%2f119076&partnerID=40&md5=1c9a7d0e5a5285ef9edf5e11180106c4
_version_ 1754532106491396096
score 13.211869

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