Autor(en): | Janus, Tim Engell, Sebastian |
Titel: | Iterative process design with surrogate-assisted global flowsheet optimization |
Sprache (ISO): | en |
Zusammenfassung: | Flowsheet optimization is an important part of process design where commercial process simulators are widely used, due to their extensive library of models and ease of use. However, the application of a framework for global flowsheet optimization upon them is computationally expensive. Based on machine learning methods, we added mechanisms for rejection and generation of candidates to a framework for global flowsheet optimization. These extensions halve the amount of time needed for optimization such that the integration of the framework in a workflow for iterative process design becomes applicable. |
Schlagwörter: | Deep learning Machine learning Process design Process optimization Process synthesis |
URI: | http://hdl.handle.net/2003/40793 http://dx.doi.org/10.17877/DE290R-22650 |
Erscheinungsdatum: | 2021-11-03 |
Rechte (Link): | https://creativecommons.org/licenses/by/4.0/ |
Enthalten in den Sammlungen: | Lehrstuhl Systemdynamik und Prozessfuehrung |
Dateien zu dieser Ressource:
Datei | Beschreibung | Größe | Format | |
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Chemie Ingenieur Technik - 2021 - Janus - Iterative Process Design with Surrogate‐Assisted Global Flowsheet Optimization.pdf | DNB | 652.36 kB | Adobe PDF | Öffnen/Anzeigen |
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