Harnessing Disjunctive Programming Formulations for Process Synthesis Problems

  • Automating process synthesis presents a formidable challenge in chemical engineering. Particularly demanding is the development of frameworks that are both general and accurate, while remaining computationally tractable. To further increase the solvable problem size, an advanced optimization framework is proposed, leveraging Generalized Disjunctive Programming (GDP) for process synthesis and optimization problems. It allows for multiple improvements over existing MINLP formulations, aiming at improving feasibility and solution time. This is achieved by deactivation of unused model equations during the solution procedure as shown by Lee et al. [1]. Using MOSAICmodeling’s [2] capability to automatically generated code for GDP problems, several different GDP formulations were evaluated regarding their possible benefits for optimizing thermal separation problems. It is shown, that taking an MINLP formulation and solely transforming it to GDP does not necessarily yield the describedAutomating process synthesis presents a formidable challenge in chemical engineering. Particularly demanding is the development of frameworks that are both general and accurate, while remaining computationally tractable. To further increase the solvable problem size, an advanced optimization framework is proposed, leveraging Generalized Disjunctive Programming (GDP) for process synthesis and optimization problems. It allows for multiple improvements over existing MINLP formulations, aiming at improving feasibility and solution time. This is achieved by deactivation of unused model equations during the solution procedure as shown by Lee et al. [1]. Using MOSAICmodeling’s [2] capability to automatically generated code for GDP problems, several different GDP formulations were evaluated regarding their possible benefits for optimizing thermal separation problems. It is shown, that taking an MINLP formulation and solely transforming it to GDP does not necessarily yield the described benefits. However, combining the conventional MINLP formulation of Kraemer et al. [3] with a GDP approach that deactivates unused stages scales superiorly compared to the conventional approach.zeige mehrzeige weniger

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Autor*innen:Lukas ScheffoldORCiD, Erik EscheORCiD
Dokumenttyp:Posterpräsentation
Veröffentlichungsform:Präsentation
Sprache:Englisch
Jahr der Erstveröffentlichung:2025
Organisationseinheit der BAM:2 Prozess- und Anlagensicherheit
2 Prozess- und Anlagensicherheit / 2.2 Prozesssimulation
DDC-Klassifikation:Technik, Medizin, angewandte Wissenschaften / Chemische Verfahrenstechnik / Chemische Verfahrenstechnik
Freie Schlagwörter:Distillation Column; Generalized Disjunctive Programming; Process Design; Process Optimization
Themenfelder/Aktivitätsfelder der BAM:Chemie und Prozesstechnik
Chemie und Prozesstechnik / Anlagensicherheit und Prozesssimulation
Veranstaltung:PEMT 2025 - Annual Meeting of Process Engineering and Materials Technology
Veranstaltungsort:Frankfurt am Main, Germany
Beginndatum der Veranstaltung:10.11.2025
Verfügbarkeit des Dokuments:Datei im Netzwerk der BAM verfügbar ("Closed Access")
Datum der Freischaltung:17.12.2025
Referierte Publikation:Nein
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