Comprehensive study of decomposition effects on distributed output tracking of an integrated process over a wide operating range

Davood Babaei Pourkargar; Ali Almansoori; Prodromos Daoutidis

doi:10.1016/j.cherd.2018.04.045

Comprehensive study of decomposition effects on distributed output tracking of an integrated process over a wide operating range

Davood Babaei Pourkargar, Ali Almansoori, Prodromos Daoutidis

Chemical and Petroleum Engineering

University of Minnesota Twin Cities

Research output: Contribution to journal › Article › peer-review

25 Scopus citations

Abstract

A comprehensive study of plant decomposition effects is presented for distributed model predictive control (DMPC) of an integrated process system. Different decompositions are obtained via community detection and other methods. The closed-loop performance and computational efficiency of employing various decompositions for DMPC design are evaluated through tracking outputs in different tracking zones corresponding to desired operating conditions. Different levels of communication and cooperation between local controllers, levels of system uncertainty, and dynamic optimization platforms are considered. The results are analyzed to determine the most suitable method for decomposition of the studied integrated process.

Original language	British English
Pages (from-to)	553-563
Number of pages	11
Journal	Chemical Engineering Research and Design
Volume	134
DOIs	https://doi.org/10.1016/j.cherd.2018.04.045
State	Published - Jun 2018

Keywords

Distributed control
Model predictive control
Output tracking
Process control
Process network
System decomposition

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10.1016/j.cherd.2018.04.045

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title = "Comprehensive study of decomposition effects on distributed output tracking of an integrated process over a wide operating range",

abstract = "A comprehensive study of plant decomposition effects is presented for distributed model predictive control (DMPC) of an integrated process system. Different decompositions are obtained via community detection and other methods. The closed-loop performance and computational efficiency of employing various decompositions for DMPC design are evaluated through tracking outputs in different tracking zones corresponding to desired operating conditions. Different levels of communication and cooperation between local controllers, levels of system uncertainty, and dynamic optimization platforms are considered. The results are analyzed to determine the most suitable method for decomposition of the studied integrated process.",

keywords = "Distributed control, Model predictive control, Output tracking, Process control, Process network, System decomposition",

author = "Pourkargar, {Davood Babaei} and Ali Almansoori and Prodromos Daoutidis",

note = "Funding Information: Financial support from the Petroleum Institute, Abu Dhabi, UAE is gratefully acknowledged. Appendix A Publisher Copyright: {\textcopyright} 2018 Institution of Chemical Engineers",

year = "2018",

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doi = "10.1016/j.cherd.2018.04.045",

language = "British English",

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T1 - Comprehensive study of decomposition effects on distributed output tracking of an integrated process over a wide operating range

AU - Pourkargar, Davood Babaei

AU - Almansoori, Ali

AU - Daoutidis, Prodromos

PY - 2018/6

Y1 - 2018/6

N2 - A comprehensive study of plant decomposition effects is presented for distributed model predictive control (DMPC) of an integrated process system. Different decompositions are obtained via community detection and other methods. The closed-loop performance and computational efficiency of employing various decompositions for DMPC design are evaluated through tracking outputs in different tracking zones corresponding to desired operating conditions. Different levels of communication and cooperation between local controllers, levels of system uncertainty, and dynamic optimization platforms are considered. The results are analyzed to determine the most suitable method for decomposition of the studied integrated process.

AB - A comprehensive study of plant decomposition effects is presented for distributed model predictive control (DMPC) of an integrated process system. Different decompositions are obtained via community detection and other methods. The closed-loop performance and computational efficiency of employing various decompositions for DMPC design are evaluated through tracking outputs in different tracking zones corresponding to desired operating conditions. Different levels of communication and cooperation between local controllers, levels of system uncertainty, and dynamic optimization platforms are considered. The results are analyzed to determine the most suitable method for decomposition of the studied integrated process.

KW - Distributed control

KW - Model predictive control

KW - Output tracking

KW - Process control

KW - Process network

KW - System decomposition

UR - http://www.scopus.com/inward/record.url?scp=85046779063&partnerID=8YFLogxK

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DO - 10.1016/j.cherd.2018.04.045

M3 - Article

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SN - 0263-8762

VL - 134

SP - 553

EP - 563

JO - Chemical Engineering Research and Design

JF - Chemical Engineering Research and Design

ER -

Comprehensive study of decomposition effects on distributed output tracking of an integrated process over a wide operating range

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Keywords

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