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Control‐relevant decomposition of process networks via optimization‐based hierarchical clustering
Author(s) -
Heo Seongmin,
Daoutidis Prodromos
Publication year - 2016
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.15323
Subject(s) - subnetwork , decomposition , cluster analysis , process (computing) , measure (data warehouse) , hierarchical clustering , integer (computer science) , computer science , data mining , mathematical optimization , mathematics , artificial intelligence , chemistry , computer security , organic chemistry , programming language , operating system
A systematic method is proposed for control‐relevant decomposition of complex process networks. Specifically, hierarchical clustering methods are adopted to identify constituent subnetworks such that the components of each subnetwork are strongly interacting while different subnetworks are loosely coupled. Optimal clustering is determined through the solution of integer optimization problems. The concept of relative degree is used to measure distance between subnetworks and compactness of subnetworks. The application of the proposed method is illustrated using an example process network. © 2016 American Institute of Chemical Engineers AIChE J , 62: 3177–3188, 2016