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 - Kozachynskyi, Volodymyr A1 - Hoffmann, Christian A1 - Esche, Erik T1 - Why fixing alpha in the NRTL model might be a bad idea – Identifiability analysis of binary vapor-liquid equilibria N2 - New vapor-liquid equilibrium (VLE) data are continuously being measured and new parameter values, e.g., for the nonrandom two-liquid (NRTL) model are estimated and published. The parameter α, the nonrandomness parameter of NRTL, is often not estimated but is heuristically fixed to a constant value based on the involved components. This can be seen as a manual application of a (subset selection) regularization method. In this work, the practical parameter identifiability of the NRTL model for describing the VLE is analyzed. It is shown that fixing α is not always a good decision and sometimes leads to worse prediction properties of the final parameter estimates. Popular regularization techniques are compared and Generalized Orthogonalization is proposed as an alternative to this heuristic. In addition, the sequential Optimal Experimental Design and Parameter Estimation (sOED-PE) method is applied to study the influence of the regularization methods on the performance of the sOED-PE loop. KW - NRTL KW - VLE KW - Nonlinear programming KW - Model-based optimal experimental design PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-623605 DO - https://doi.org/10.1016/j.ces.2024.121122 VL - 305 SP - 1 EP - 18 PB - Elsevier Ltd. AN - OPUS4-62360 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Kozachynskyi, Volodymyr A1 - Staubach, Dario T1 - Monte Carlo analysis of esterification reaction model with disappearing second liquid phase N2 - This research item is associated with the publication: Parameter estimation in dynamic multiphase liquid-liquid equilibrium systems (submitted after 2025-02-15) The software used to generate and analyze the experimental data is stored with the research data. KW - Parameter Estimation KW - NRTL KW - LLE KW - Phase Detection PY - 2025 DO - https://doi.org/10.14279/depositonce-21232 PB - Technische Universität Berlin CY - Berlin AN - OPUS4-62670 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Bubel, Martin A1 - Schmid, Jochen A1 - Carmesin, Maximilian A1 - Kozachynskyi, Volodymyr A1 - Esche, Erik A1 - Bortz, Michael T1 - Cubature-based uncertainty estimation for nonlinear regression models N2 - Models are commonly utilized in chemical engineering to simulate real-world processes and phenomena. Given their role in guiding decision-making, accurately quantifying the uncertainty of these models is essential. Typically, these models are calibrated using experimental data that contain measurement errors, leading to uncertainty in the fitted model parameters. Current methods for estimating the prediction uncertainty of nonlinear regression models are often either computationally intensive or biased. In this study, we use sparse cubature formulas to estimate the prediction uncertainty of nonlinear regression models. Our findings indicate that this method provides a favorable balance between accuracy and computational efficiency, making it suitable for application in chemical engineering. We validate the performance of our proposed method through various regression case studies, including both theoretical toy models and practical models from chemical engineering. KW - Nonlinear models KW - Model uncertainty KW - Parameter estimation PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-628008 DO - https://doi.org/10.1016/j.compchemeng.2025.109035 SN - 1873-4375 VL - 197 SP - 1 EP - 23 PB - Elsevier Ltd. AN - OPUS4-62800 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 - CONF A1 - Muñoz Gil, Hernán Darío T1 - MR4SafeOperations: Mixed reality system for training and supporting industrial plant personnel. N2 - Operation and maintenance tasks in industrial process plants are complex and safety-critical, often relying on heterogeneous documentation and limited contextual support for field personnel. This work presents MR4SafeOperations, a mixed reality–based system designed to assist operators during operation and maintenance activities through context-aware, hands-free guidance. Using a vacuum distillation sampling procedure as a case study, operational workflows are formalized with BPMN and structured according to ISA-88.1 principles. Process data, 3D plant models, P&IDs, and procedural information are integrated via standardized interfaces into a mixed reality application. The system enhances situational awareness, reduces cognitive load, and supports safer decision-making during plant operation. The results demonstrate the potential of mixed reality to improve safety, efficiency, and usability in industrial process environments. T2 - MR4B-Konferenz CY - Berlin, Germany DA - 17.07.2025 KW - Safety KW - Mixed Reality KW - Operation & Maintenance KW - Digital workflows PY - 2025 AN - OPUS4-65165 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Lewerenz, Rebecca A1 - Komander, Felix T1 - KEPLER – Kollaborative Mixed Reality Plattform für das Engineering im Anlagenbau durch Integration der Domänenmodelle BIM und DEXPI N2 - In den frühen Entwurfsphasen von verfahrenstechnischen Anlagen innerhalb von Gebäuden müssen Verfahrens- und Bauingenieur:innen eng zusammenarbeiten. Dabei nutzen sie jedoch unterschiedliche Werkzeuge, Datenmodelle und Austauschstandards – etwa DEXPI in der Verfahrenstechnik und IFC im Bauwesen –, was den Datenaustausch zwischen den Disziplinen erschwert und häufig zu Inkonsistenzen führt. Das KEPLER-Projekt entwickelt eine Mixed-Reality-Plattform für die dreidimensionale Anordnung von Anlagenkomponenten durch die Integration der Standards DEXPI und IFC. T2 - MR4B-Konferenz CY - Berlin, Germany DA - 17.07.2025 KW - Interoperabilität KW - DEXPI KW - BIM KW - IFC KW - Preengineering KW - Mixed-Reality PY - 2025 AN - OPUS4-65183 LA - deu 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 - TY - CONF A1 - Scheffold, Lukas A1 - Esche, Erik T1 - Exploring disjunctive programming formulations for optimal design of distillation columns N2 - Automating process synthesis presents a formidable challenge in chemical engineering. Developing frameworks that are both general and accurate – while remaining computationally tractable – is particularly demanding. To further increase the solvable problem size, an advanced optimization framework is proposed, leveraging Generalized Disjunctive Programming (GDP) for process synthesis and design problems. This framework allows for multiple improvements over existing Mixed Integer Nonlinear Programming (MINLP) formulations, aiming to enhance feasibility and reduce solution time. Process synthesis problems are typically posed as nonlinear optimization problems with continuous and discrete variables. Traditionally, these are formulated as MINLP problems, where discrete variables appear as integer or binary variables. These are usually relaxed to continuous variables to solve the MINLP. In practice, the (in-)equality constraints are obtained starting from logical expressions, which are reformulated as algebraic constraints. Disjunctive expressions are typically converted into mixed integer constraints by using “big-M” constraints [1]. An alternative reformulation for treating disjunctions is the convex hull (chull) formulation, which achieves superior relaxation tightness but is rarely used due to its greater complexity and potential numerical difficulties [2]. Recent advances in GDP allow for automatic reformulation of optimization problems, e.g., by big-M or chull. Furthermore, dedicated GDP solution algorithms are now available [3]. Unlike conventional Branch and Bound or Outer Approximation algorithms, their logic-based counterparts can neglect inactive equations, reducing the size and complexity of subproblems. This is achieved by deactivating unused model equations during the solution procedure, as shown by Lee et al. [4]. This point is particularly interesting for chemical engineering, as a lot of time is spent computing NLP subproblems. Also, model formulation can have a large impact on the solution times of MINLP [1]. However, evaluating various model formulations tends to be rather tedious. Especially detailed model formulations that include rigorous thermodynamics and kinetics tend to increase the model size significantly. As a first step for further exploitation of GDP for process synthesis and process design, we developed a modeling environment and automatic code generation framework for GDP. This contribution aims at investigating different problem formulations for GDP in process design facilitated by the new framework. For maximum flexibility and independence from any given programming language, the modeling and problem formulation is implemented within MOSAICmodeling [5], a platform that allows for model formulation at the documentation level. Users formulate equations in LaTeX, which are then translated into MathML/XML preserving all relevant information while remaining as general as possible. The first step hereby consists in the definition of a suitable notation. This defines all possibly occurring variables and their indices and sets the basis of all MathML/XML operations. To include GDP into this workflow, already existing variable definitions were extended to also include logical / Boolean variables. Equations are created based on those extended notations: Basic logical operators – and (∧), or (∨), not (¬), implication (⇒), and equivalence (⇔) – were added to include logical expressions. Furthermore, disjunctions can be created by linking Boolean variables with single equations or sets of equations. The resulting systems defined in MathML/XML can be exported to any programming language using MOSAICmodeling’s UDLS feature [6], which has been extended tocapture connections between disjunctive, logical variables, and their associated equations, allowing for code export to GAMS, Julia, and Pyomo. Four different MINLP/GDP formulations were implemented in MathML/XML in MOSAICmodeling, exported, and evaluated within pyomo regarding their benefits in optimizing thermal separation problems. For this case study, the MINLP formulation of Kraemer et al. [7] is evaluated (pureMINLP), which determines the optimal column height to achieve desired product specifications while minimizing costs for a multicomponent distillation column. The MINLP formulation varies locations of feed, reflux, and boilup streams. Each separation stage is modeled rigorously applying thermodynamics of varying complexity. An alternative, GDP formulation (pureGDP) specifies disjunctions for each separation stage as in [QGrossmann2000rig]. The disjunctive variables decide which stages are active or not. For active stages, the stage formulation is the same as above. For inactive stages, a passthrough of liquid and vapor streams without thermodynamic calculations is applied. The feed location is modeled as a nested disjunction for the active stages. Two additional formulations, in between pureMINLP and pureGDP, apply different levels of relaxation: Feed relaxed GDP (fr-GDP) employs the disjunctive formulation for the stages, while applying pureMINLP’s formulation for the feed location. Decision variable GDP (dv-GDP) on the other hand, applies a formulation that translates directly into the pureMINLP formulation if the dv-GDP is relaxed by BigM and the M is chosen accordingly. The four different MINLP/GDP formulations are combined with two different implementations for thermodynamics calls: (1) explicit formulation using the Antoine equation for vapor pressures, linearized heats of evaporation, and constant specific heat capacities; (2) external thermodynamic function calls using a CAPE-OPEN interface with TEA as thermodynamics engine [8]. Note that the same thermodynamic models are used for explicit and external thermodynamics implementations to ensure comparability of the solutions. However, the interface supports any CAPE-OPEN compliant thermodynamics engine, allowing for even highly complex equations of state, such as, PC-SAFT. Future work will therefore also include non-idealities, which is not in scope of this contribution. To evaluate the performance of available GDP solvers, the formulations in MathML/XML are exported to pyomo. Exports to julia and GAMS were also developed. However, they currently lack support for dedicated GDP solvers. Three different GDP formulations (rGDP, cGDP and bGDP) were investigated with respect to runtime until an optimal solution was found. These formulations were benchmarked against a commonly used pMINLP formulation. The maximum number of stages for all columns were 32, of which 30 were choosable by the optimizer. We discovered that all GDP formulations perform worse or equal to the pMINLP formulation. However, some formulations outperform others. It was shown, that the inclusion of mass and energy balances of the separation stages into the global constraints is absolutely nescessary to robustly find the optimal stage number. Their inclusion into the disjuncts leads to a degradation of the outer approximation linearization and therefore hinders the solution process. It was also shown, that the bGDP formulation can greatly improve the solution time, by structuring the active stages into binary encoded blocks. This reduces the required binary variables, leading to improved solver Performance. Lastly, we were able to show, that the bGDP achieves pairity in runtime with the pMINLP benchmark, showing strong indications that bGDP can surpass the pMINLP formulation in future, more advanced optimization Problems. This study also shows that simply transforming an MINLP formulation into a GDP does not necessarily yield benefits. Runtime strongly varies across the three GDP formulations. To this end, further investigations for efficient exploitation of GDP for process synthesis is required. Our modeling framework in MathML/XML now supports fast formulation of highly complex GDPs and evaluation in a variety of supporting platforms (pyomo, julia, GAMS), speeding up the process of tailoring problem formulations. The investigated problem sizes are small, chosen as a proof of concept for formulation, code generation, and solution of GDPs. Given the runtime of the investigated problems, increasing system size is feasible. Future work will aim to increase the total system size and move towards more general process synthesis, potentially putting GDP problem runtime below that of relaxed MINLP. T2 - AIChE Annual Meeting CY - Boston, MA, USA DA - 02.11.2025 KW - Process Design KW - Process Optimization KW - Generalized Disjunctive Programming KW - Automatic Code Generation KW - Distillation Column PY - 2025 AN - OPUS4-65157 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Muñoz Gil, Hernán Darío T1 - Mixed Reality for Safe Operations: Connecting MR Devices to Process Automation and Documentation N2 - The industrial sector faces challenges in ensuring safety and efficiency of operation and maintenance tasks (OMT). These tasks involve manual interventions such as equipment inspections or sample extractions. OMT require workers to communicate with control rooms and follow strict operational protocols while managing physical and cognitive loads. Common challenges include safety risks, the difficulty of using safety gear, limited accessibility to information, inefficient communication, and high (human) error rates. While chemical plants traditionally rely on paper-based workflows to tackle the mentioned challenges, we present an approach that leverages digital checklists to model workflows, integrating them with industrial documentation and process automation systems. We combine these elements into contextualized workflows that are deployed in a Mixed Reality (MR) application allowing to support personnel with hands-free execution, real-time verification, and an MR-based human-machine interface (HMI). This integration enhances safety through automated system checks and MR-guided validation while reducing the cognitive load on personnel. We present an MR prototype based on a vacuum distillation sampling procedure that grants access to documentation, interfaces with process control systems, and manages workflows dynamically. Compared to conventional industrial checklists, our approach reduces errors by combining gamification, IT-based validation, and immersive task visualization while enabling hands-free task execution. As future work, we envision a generalized framework for rapidly incorporating new tasks, enhancing adaptability in industrial environments. This research paves the way for standardized MR solutions in chemical plant operations, ultimately improving efficiency, safety, and workforce engagement. T2 - Process Engineering and Materials Technology (PEMT) CY - Frankfurt am Main, Germany DA - 10.11.2025 KW - Safety KW - Mixed Reality KW - Operation & Maintenance KW - Digital workflows KW - Process control system PY - 2025 AN - OPUS4-65156 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -