Impact of Decomposition on Distributed Model Predictive Control: A Process Network Case Study

Davood Babaei Pourkargar, Ali Almansoori, Prodromos Daoutidis

Research output: Contribution to journalArticlepeer-review

42 Scopus citations

Abstract

This paper addresses the impact of decomposition on the closed-loop performance and computational efficiency of model predictive control (MPC) of nonlinear process networks. Distributed MPC structures with different communication strategies are designed for regulation of an integrated reactor-separator process. Different system decompositions are also considered, including decompositions into local controllers with minimum interactions obtained via community detection methods. The closed-loop performance and computational effort of the different MPC designs are analyzed. Through such a comprehensive comparison, tradeoffs between performance and computation effort, and the importance of systematic choice of the system decomposition, are documented. (Graph Presented).

Original languageEnglish (US)
Pages (from-to)9606-9616
Number of pages11
JournalIndustrial and Engineering Chemistry Research
Volume56
Issue number34
DOIs
StatePublished - Aug 30 2017

Bibliographical note

Funding Information:
Financial support from the Petroleum Institute, Abu Dhabi, UAE is gratefully acknowledged.

Publisher Copyright:
© 2017 American Chemical Society.

Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.

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