Distributed Estimation and Nonlinear Model Predictive Control Using Community Detection

被引：30
|
作者：
Pourkargar, Davood B. [1 ]
Moharir, Manjiri [1 ]
Almansoori, Ali [2 ]
Daoutidis, Prodromos [1 ]
机构：
[1] Univ Minnesota, Dept Chem Engn & Mat Sci, 421 Washington Ave SE, Minneapolis, MN 55455 USA
[2] Khalifa Univ Sci Technol & Res, Dept Chem Engn, Abu Dhabi, U Arab Emirates
来源：
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH | 2019年 / 58卷 / 30期
关键词：
PROCESS NETWORKS; SUBSYSTEM DECOMPOSITION; BENZENE ALKYLATION; INTEGRATED PROCESS; STATE ESTIMATION; ARCHITECTURES; OPTIMIZATION;
D O I：
10.1021/acs.iecr.9b00820
中图分类号：
TQ [化学工业];
学科分类号：
0817 ;
摘要：
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.
引用
收藏
页码：13495 / 13507
页数：13
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