A superstructure representation, generation, and modeling framework for chemical process synthesis

We present a framework for the efficient representation, generation, and modeling of superstructures for process synthesis. First, we develop a new representation based on three basic elements: units, ports, and conditioning streams. Second, we present four rules based on “minimal” and “feasible” co...

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Veröffentlicht in:AIChE journal Jg. 62; H. 9; S. 3199 - 3214
Hauptverfasser: Wu, WenZhao, Henao, Carlos A., Maravelias, Christos T.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Blackwell Publishing Ltd 01.09.2016
American Institute of Chemical Engineers
Wiley Blackwell (John Wiley & Sons)
Schlagworte:
Chemical engineering
Chemical engineers
Chemical synthesis
Feasibility
Mathematical models
mixed-integer nonlinear programming
Modelling
optimization
Representations
Rule based
Superstructures
Synthesis (chemistry)
ISSN:0001-1541, 1547-5905
Online-Zugang:Volltext
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Zusammenfassung:We present a framework for the efficient representation, generation, and modeling of superstructures for process synthesis. First, we develop a new representation based on three basic elements: units, ports, and conditioning streams. Second, we present four rules based on “minimal” and “feasible” component sets for the generation of simple superstructures containing all feasible embedded processes. Third, in terms of modeling, we develop a modular approach, and formulate models for each basic element. We also present a canonical form of element models using input/output variables and constrained/free variables. The proposed methods provide a coherent framework for superstructure‐based process synthesis, allowing efficient model generation and modification. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3199–3214, 2016
Bibliographie:National Science Foundation - No. EFRI-1240268
ArticleID:AIC15300
istex:239FF46C7C8D610392B89B895B174B78C7711777
ark:/67375/WNG-0PW41NH7-1
DOE Great Lakes Bioenergy Research Center - No. DE-FC02-07ER64494
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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USDOE
DE‐FC02‐07ER64494
ISSN:0001-1541
1547-5905
DOI:10.1002/aic.15300