TY  - CONF
A1  - Scheffold, Lukas
A1  - Esche, Erik
T1  - Harnessing Disjunctive Programming Formulations for Process Synthesis Problems
N2  - 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.
T2  - PEMT 2025 - Annual Meeting of Process Engineering and Materials Technology
CY  - Frankfurt am Main, Germany
DA  - 10.11.2025
KW  - Process Design
KW  - Process Optimization
KW  - Distillation Column
KW  - Generalized Disjunctive Programming
PY  - 2025
AN  - OPUS4-65160
LA  - eng
AD  - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER  -