65160 2025 eng poster 0 -- -- -- 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 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. PEMT 2025 - Annual Meeting of Process Engineering and Materials Technology Frankfurt am Main, Germany 10.11.2025 publish false true Lukas Scheffold Erik Esche eng uncontrolled Process Design eng uncontrolled Process Optimization eng uncontrolled Distillation Column eng uncontrolled Generalized Disjunctive Programming Chemische Verfahrenstechnik 2 Prozess- und Anlagensicherheit 2.2 Prozesssimulation Chemie und Prozesstechnik Datei im Netzwerk der BAM verfügbar ("Closed Access") Präsentation Anlagensicherheit und Prozesssimulation