TY - JOUR A1 - Krone, David A1 - Esche, Erik A1 - Skiborowski, Mirko A1 - Repke, Jens-Uwe T1 - Optimization-based process synthesis by phenomena-based building blocks and an MINLP framework featuring structural screening N2 - An existing approach for optimization-based process synthesis with abstracted phenomena-based building blocks (PBB) is extended by implementing it into a novel MINLP framework with structural screening. Consistency across the multilayer MINLP framework is guaranteed by creating a MathML/XML data model and subsequently exporting the code to the different program parts. The novel framework focuses both on fidelity by implementing thermodynamically sound models and on generality by employing a state-space superstructure that spans a large search space. In order to retain tractability, we insert a structural screening layer which prescreens based on binary decision variables of the superstructure by graph- and rule-based analyses, penalizing non-physical instances without solution of the underlying MINLP. The MINLP framework is successfully applied on two challenging synthesis tasks to determine the separation of the feed streams of benzene and toluene, as well as of n-pentane, n-hexane, and n-heptane utilizing superstructures with two, respectively four PBB. KW - Process synthesis KW - Superstructure optimization KW - Distillation KW - Mathematical programming KW - Structural screening PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-621955 DO - https://doi.org/10.1016/j.compchemeng.2024.108955 VL - 194 SP - 1 EP - 19 PB - Elsevier Ltd. AN - OPUS4-62195 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Talis, Torben A1 - Pfafferott, Marie A1 - Esche, Erik A1 - Repke, Jens‐Uwe T1 - Recipe‐Free Synthesis of Optimal Operation Trajectories for Batch Processes Based on Process Models N2 - AbstractBatch processes are usually operated following recipes, which are based on experience and expert knowledge. This ensures feasible and safe operation, because process constraints are indirectly included in the recipe. However, the recipe structure itself constrains the solution space and might exclude other more efficient trajectories. Therefore, the hidden constraints are explicitly formulated, and the arising optimization problem is solved without using prior knowledge in the form of recipes. Case studies are performed on rigorous models of a batch reactor and a batch distillation column. It is demonstrated that the optimization problem formulated as a smoothed dynamic nonlinear programming problem outperforms a mixed‐integer formulation. Finally, a multi‐objective case is investigated that strongly outperforms a recipe‐based benchmark. KW - Batch process operation KW - Control vector parameterization KW - Nonlinear optimization KW - Sequential optimization PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-642012 DO - https://doi.org/10.1002/cite.70029 SN - 0009-286X SP - 1 EP - 10 PB - Wiley-VHC CY - Weinheim AN - OPUS4-64201 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Kozachynskyi, Volodymyr A1 - Staubach, Dario A1 - Esche, Erik A1 - Biegler, Lorenz T. A1 - Repke, Jens-Uwe T1 - Parameter estimation in dynamic multiphase liquid–liquid equilibrium systems N2 - Modeling dynamic systems with a variable number of liquid phases is a challenging task, especially in scenarios where the model is designed for optimization tasks such as parameter estimation. Although there exist methods to model the appearance and disappearance of liquid phases in dynamic systems, they usually require integer variables. In this work, the smoothed continuous approach (SCA) is developed for use with a large number of solvers, since it relies only on continuous variables. To demonstrate the applicability of the new method, the SCA is then applied to model the batch esterification of acetic acid with 1-propanol to water and propyl acetate, and to estimate the reaction parameters. Since the mixture may separate into two liquid phases during the course of the reaction, the parameters are estimated with information on the liquid compositions of both separated liquid phases, which improves the accuracy of the parameter estimates and opens new possibilities for optimal experimental design. KW - Parameter Estimation KW - Uncertainty KW - Dynamic Modeling PY - 2026 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-650059 DO - https://doi.org/10.1016/j.compchemeng.2025.109485 SN - 0098-1354 VL - 206 SP - 1 EP - 16 PB - Elsevier Ltd. AN - OPUS4-65005 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -