Distributed Estimation and Nonlinear Model Predictive Control Using Community Detection

Davood B. Pourkargar; Manjiri Moharir; Ali Almansoori; Prodromos Daoutidis

doi:10.1021/acs.iecr.9b00820

Distributed Estimation and Nonlinear Model Predictive Control Using Community Detection

Davood B. Pourkargar, Manjiri Moharir, Ali Almansoori, Prodromos Daoutidis

Chemical Engineering and Materials Science

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

18 Scopus citations

Abstract

A combined distributed moving horizon estimation and distributed model predictive control architecture is proposed to address the distributed output-feedback control problem for nonlinear process systems. Community detection based on modularity maximization is used to generate separate optimal decompositions for the estimation and control problems on the basis of suitable graphs. The process of benzene alkylation with ethylene is used as a case study to illustrate the application and computational advantages of the proposed control strategy.

Original language	English (US)
Pages (from-to)	13495-13507
Number of pages	13
Journal	Industrial and Engineering Chemistry Research
Volume	58
Issue number	30
DOIs	https://doi.org/10.1021/acs.iecr.9b00820
State	Published - Jul 31 2019

Bibliographical note

Funding Information:
Financial support from the Department of Chemical Engineering, Khalifa University of Science, Technology, and Research, Abu Dhabi, UAE, is gratefully acknowledged.

Publisher Copyright:
© 2019 American Chemical Society.

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10.1021/acs.iecr.9b00820

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author = "Pourkargar, {Davood B.} and Manjiri Moharir and Ali Almansoori and Prodromos Daoutidis",

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AU - Moharir, Manjiri

AU - Almansoori, Ali

AU - Daoutidis, Prodromos

N1 - Funding Information: Financial support from the Department of Chemical Engineering, Khalifa University of Science, Technology, and Research, Abu Dhabi, UAE, is gratefully acknowledged. Publisher Copyright: © 2019 American Chemical Society.

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AB - A combined distributed moving horizon estimation and distributed model predictive control architecture is proposed to address the distributed output-feedback control problem for nonlinear process systems. Community detection based on modularity maximization is used to generate separate optimal decompositions for the estimation and control problems on the basis of suitable graphs. The process of benzene alkylation with ethylene is used as a case study to illustrate the application and computational advantages of the proposed control strategy.

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Distributed Estimation and Nonlinear Model Predictive Control Using Community Detection

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