Advances in Generalized Disjunctive and Mixed- Integer Nonlinear Programming Algorithms and Software for Superstructure Optimization

This manuscript presents the recent advances in Mixed-Integer Nonlinear Programming (MINLP) and Generalized Disjunctive Programming (GDP) with a particular scope for superstructure optimization within Process Systems Engineering (PSE). We present an environment of open-source software packages writt...

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Vydáno v:Computer Aided Chemical Engineering Ročník 49; s. 1285 - 1290
Hlavní autoři: Bernal, David E., Liu, Yunshan, Bynum, Michael L., Laird, Carl D., Siirola, John D., Grossmann, Ignacio E.
Médium: Kapitola
Jazyk:angličtina
Vydáno: 2022
Témata:
generalized disjunctive programming
MINLP
superstructure optimization
superstructure optimization
MINLP
generalized disjunctive programming
ISBN:9780323851596, 0323851592
ISSN:1570-7946
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Shrnutí:This manuscript presents the recent advances in Mixed-Integer Nonlinear Programming (MINLP) and Generalized Disjunctive Programming (GDP) with a particular scope for superstructure optimization within Process Systems Engineering (PSE). We present an environment of open-source software packages written in Python and based on the algebraic modeling language Pyomo. These packages include MindtPy, a solver for MINLP that implements decomposition algorithms for such problems, CORAMIN, a toolset for MINLP algorithms providing relaxation generators for nonlinear constraints, Pyomo.GDP, a modeling extension for Generalized Disjunctive Programming that allows users to represent their problem as a GDP natively, and GDPOpt, a collection of algorithms explicitly tailored for GDP problems. Combining these tools has allowed us to solve several problems relevant to PSE, which we have gathered in an easily installable and accessible library, GDPLib. We show two examples of these models and how the flexibility of modeling given by Pyomo.GDP allows for efficient solutions to these complex optimization problems. Finally, we show an example of integrating these tools with the framework IDAES PSE, leading to optimal process synthesis and conceptual design with advanced multi-scale PSE modeling systems.
ISBN:9780323851596
0323851592
ISSN:1570-7946
DOI:10.1016/B978-0-323-85159-6.50214-1