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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Veröffentlicht in:Computer Aided Chemical Engineering Jg. 49; S. 1285 - 1290
Hauptverfasser: Bernal, David E., Liu, Yunshan, Bynum, Michael L., Laird, Carl D., Siirola, John D., Grossmann, Ignacio E.
Format: Buchkapitel
Sprache:Englisch
Veröffentlicht: 2022
Schlagworte:
generalized disjunctive programming
MINLP
superstructure optimization
superstructure optimization
MINLP
generalized disjunctive programming
ISBN:9780323851596, 0323851592
ISSN:1570-7946
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Abstract 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.
AbstractList 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.
Author Bynum, Michael L.
Bernal, David E.
Liu, Yunshan
Laird, Carl D.
Grossmann, Ignacio E.
Siirola, John D.
Author_xml – sequence: 1
  givenname: David E.
  surname: Bernal
  fullname: Bernal, David E.
  organization: Department of Chemical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, United States of America
– sequence: 2
  givenname: Yunshan
  surname: Liu
  fullname: Liu, Yunshan
  organization: Department of Chemical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, United States of America
– sequence: 3
  givenname: Michael L.
  surname: Bynum
  fullname: Bynum, Michael L.
  organization: Discrete Mathematics and Optimization, Sandia National Laboratories, 1515 Eubank SE, Albuquerque, NM, 87185, United States of America
– sequence: 4
  givenname: Carl D.
  surname: Laird
  fullname: Laird, Carl D.
  organization: Department of Chemical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, United States of America
– sequence: 5
  givenname: John D.
  surname: Siirola
  fullname: Siirola, John D.
  organization: Discrete Mathematics and Optimization, Sandia National Laboratories, 1515 Eubank SE, Albuquerque, NM, 87185, United States of America
– sequence: 6
  givenname: Ignacio E.
  surname: Grossmann
  fullname: Grossmann, Ignacio E.
  email: grossmann@cmu.edu
  organization: Department of Chemical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, United States of America
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Keywords superstructure optimization
MINLP
generalized disjunctive programming
Language English
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Snippet This manuscript presents the recent advances in Mixed-Integer Nonlinear Programming (MINLP) and Generalized Disjunctive Programming (GDP) with a particular...
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StartPage 1285
SubjectTerms generalized disjunctive programming
MINLP
superstructure optimization
Title Advances in Generalized Disjunctive and Mixed- Integer Nonlinear Programming Algorithms and Software for Superstructure Optimization
URI https://dx.doi.org/10.1016/B978-0-323-85159-6.50214-1
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