Pyosyn: A new framework for conceptual design modeling and optimization
•Pyosyn Graph (PSG): superstructure representation supports nested units to reduce complexity.•Structure and logic of single-choice units simplify modeling of superstructure.•Port material categories primary, secondary, and residual improve screening rules.•High-level modeling with Pyomo.Network and...
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| Published in: | Computers & chemical engineering Vol. 153; no. C; p. 107414 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
United Kingdom
Elsevier Ltd
01.10.2021
Elsevier |
| Subjects: | |
| ISSN: | 0098-1354, 1873-4375 |
| Online Access: | Get full text |
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| Summary: | •Pyosyn Graph (PSG): superstructure representation supports nested units to reduce complexity.•Structure and logic of single-choice units simplify modeling of superstructure.•Port material categories primary, secondary, and residual improve screening rules.•High-level modeling with Pyomo.Network and Pyomo.GDP.•Flexible optimization suite, including logic-based decomposition for zero-flow issues.
We present Pyosyn, an open-source framework for systematic superstructure-based process synthesis, including a new representation, superstructure generation approaches, modeling, and solution strategies. The new Pyosyn Graph (PSG) representation consists of units, ports, and streams, and includes support for nested units, including new “single-choice” units and modular superstructure construction. We introduce superstructure generation strategies based on both library-assisted and direct-hierarchical means-ends analysis. For the library-assisted approach, we describe generalized port annotations that describe conditions for compatibility between connected unit ports. We extend literature methods to present seven screening rules based on new material port annotations that categorize process chemical species as primary, secondary, or residual. We then describe high-level mathematical modeling of PSG representation elements using Pyomo.Network and Pyomo.GDP, including the automated handling of special cases. We also introduce the use of tailored logic-based decomposition algorithms to address “zero-flow” singularities characteristic of synthesis problems. Finally, we demonstrate the flexible use of Pyosyn tools on a set of diverse case studies. |
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| Bibliography: | USDOE |
| ISSN: | 0098-1354 1873-4375 |
| DOI: | 10.1016/j.compchemeng.2021.107414 |