Global optimization of a combinatorially complex generalized pooling problem

Global optimization strategies are described for a generalization of the pooling problem that is important to the petrochemical, chemical, and wastewater treatment industries. The problem involves both discrete variables, modeling the structure of a flow network, and continuous variables, modeling f...

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Veröffentlicht in:AIChE journal Jg. 52; H. 3; S. 1027 - 1037
Hauptverfasser: Meyer, Clifford A., Floudas, Christodoulos A.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.03.2006
Wiley Subscription Services
American Institute of Chemical Engineers
Schlagworte:
Algorithms
Applied sciences
Chemical engineering
Exact sciences and technology
Flow rates
General purification processes
global optimization
mixed integer nonlinear programming
Nonlinear programming
Optimization
Petrochemicals
Pollution
pooling problem
wastewater network design
Wastewater treatment
Wastewaters
Water treatment and pollution
pooling problem
wastewater network design
Algorithm
Modeling
Optimization
global optimization
Waste water
Design
Petrochemical industry
mixed integer nonlinear programming
Industrial area
Waste water purification
Linearization
ISSN:0001-1541, 1547-5905
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:Global optimization strategies are described for a generalization of the pooling problem that is important to the petrochemical, chemical, and wastewater treatment industries. The problem involves both discrete variables, modeling the structure of a flow network, and continuous variables, modeling flow rates, and stream attributes. The continuous relaxation of this mixed integer nonlinear programming problem is nonconvex because of the presence of bilinear terms in the constraint functions. We propose an algorithm to find the global solution using the principles of the reformulation‐linearization technique (RLT). A novel piecewise linear RLT formulation is proposed and applied to the class of generalized pooling problems. Using this approach we verify the global solution of a combinatorially complex industrial problem containing 156 bilinear terms and 55 binary variables, reducing the gap between upper and lower bounds to within 1.2%. © 2005 American Institute of Chemical Engineers AIChE J, 2006
Bibliographie:istex:60A919EC1DD584A9AA5C5F9F01C88579D64B3FDB
ark:/67375/WNG-L0KHZHZ9-1
AspenTech Inc.
National Science Foundation
ArticleID:AIC10717
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ObjectType-Article-2
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ISSN:0001-1541
1547-5905
DOI:10.1002/aic.10717