Process Superstructure Optimization through Discrete Steepest Descent Optimization: a GDP Analysis and Applications in Process Intensification
This manuscript introduces a Logic-based Discrete-Steepest Descent Algorithm (LD- SDA) to tackle problems arising from process superstructure optimization. These problems often appear in Process Systems Engineering and become challenging when addressing Process Intensification applications. The curr...
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| Published in: | Computer Aided Chemical Engineering Vol. 49; pp. 1279 - 1284 |
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| Main Authors: | , , , , , |
| Format: | Book Chapter |
| Language: | English |
| Published: |
2022
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| Subjects: | |
| ISBN: | 9780323851596, 0323851592 |
| ISSN: | 1570-7946 |
| Online Access: | Get full text |
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| Abstract | This manuscript introduces a Logic-based Discrete-Steepest Descent Algorithm (LD- SDA) to tackle problems arising from process superstructure optimization. These problems often appear in Process Systems Engineering and become challenging when addressing Process Intensification applications. The current algorithm considers a disjunctive interpretation of these optimization problems through Generalized Disjunctive Programming (GDP). This formulation allows further analysis of the solution method as a tailored approach for GDP and results in a general open-source implementation of the method relying on the modeling paradigm Pyomo.GDP. Complementing our previous studies in the subject, we compare the LD-SDA against other well-known GDP solution methods and a D-SDA that does not consider the disjunctive nature of these problems. The results showcase the advantages of LD-SDA when dealing with superstructure problems arising from process intensification. |
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| AbstractList | This manuscript introduces a Logic-based Discrete-Steepest Descent Algorithm (LD- SDA) to tackle problems arising from process superstructure optimization. These problems often appear in Process Systems Engineering and become challenging when addressing Process Intensification applications. The current algorithm considers a disjunctive interpretation of these optimization problems through Generalized Disjunctive Programming (GDP). This formulation allows further analysis of the solution method as a tailored approach for GDP and results in a general open-source implementation of the method relying on the modeling paradigm Pyomo.GDP. Complementing our previous studies in the subject, we compare the LD-SDA against other well-known GDP solution methods and a D-SDA that does not consider the disjunctive nature of these problems. The results showcase the advantages of LD-SDA when dealing with superstructure problems arising from process intensification. |
| Author | Liñán, David A. Gómez, Jorge M. Bernal, David E. Ricardez-Sandoval, Luis A. Grossmann, Ignacio E. Ovalle, Daniel |
| Author_xml | – sequence: 1 givenname: David E. surname: Bernal fullname: Bernal, David E. email: grossmann@cmu.edu organization: Department of Chemical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, United States of America – sequence: 2 givenname: Daniel surname: Ovalle fullname: Ovalle, Daniel organization: Department of Chemical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, United States of America – sequence: 3 givenname: David A. surname: Liñán fullname: Liñán, David A. organization: Department of Chemical Engineering, University of Waterloo, Ontario N2L 3G1, Canada – sequence: 4 givenname: Luis A. surname: Ricardez-Sandoval fullname: Ricardez-Sandoval, Luis A. organization: Department of Chemical Engineering, University of Waterloo, Ontario N2L 3G1, Canada – sequence: 5 givenname: Jorge M. surname: Gómez fullname: Gómez, Jorge M. organization: Departamento de Ingeniería Química y de Alimentos, Universidad de Los Andes, Bogotá 111711, Colombia – sequence: 6 givenname: Ignacio E. surname: Grossmann fullname: Grossmann, Ignacio E. 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 convex discrete analysis process intensification |
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| SubjectTerms | convex discrete analysis process intensification superstructure optimization |
| Title | Process Superstructure Optimization through Discrete Steepest Descent Optimization: a GDP Analysis and Applications in Process Intensification |
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