arXiv subjects

Kerstin Wohlgemuth

Publications and source records attributed to Kerstin Wohlgemuth.

3 recordsLinked to original sources

arXiv2025

Feasibility study for physics-informed direct numerical simulation describing particle suspension in high-loaded compartments of air-segmented flow

The Archimedes Tube Crystallizer (ATC) employs air-segmented flow in coiled tubes to achieve narrow residence time distributions for continuous crystallization. Taylor and Dean vortices drive particle suspension in this system. However, one-way coupled models fail to capture the fluid-particle feedback that becomes critical at higher loadings. We present a particle-resolved Direct Numerical Simulation (DNS) framework based on a Finite Element-Fictitious Boundary Method with hard-contact modeling of particle interactions. Simulations of L-alanine suspensions across varying particle sizes, solid contents, and rotational speeds are validated against experimental side-view imaging. Three quantitative metrics-axial distribution, radial index, and vertical asymmetry-are introduced to classify suspension regimes. The DNS results reproduce the experimentally observed flow map zones (green, yellow, red/yellow, red) and resolve subtle transitions such as rear loading and loss of vertical symmetry. This feasibility study demonstrates that DNS can reliably predict dense suspension behavior and provides a mechanistic foundation for crystallizer design.

physics.flu-dyn↗

arXiv2025

Multi-stage model predictive control for slug flow crystallizers using uncertainty-aware surrogate models

This paper presents a novel dynamic model for slug flow crystallizers that addresses the challenges of spatial distribution without backmixing or diffusion, potentially enabling advanced model-based control. The developed model can accurately describe the main characteristics of slug flow crystallizers, including slug-to-slug variability but leads to a high computational complexity due to the consideration of partial differential equations and population balance equations. For that reason, the model cannot be directly used for process optimization and control. To solve this challenge, we propose two different approaches, conformalized quantile regression and Bayesian last layer neural networks, to develop surrogate models with uncertainty quantification capabilities. These surrogates output a prediction of the system states together with an uncertainty of these predictions to account for process variability and model uncertainty. We use the uncertainty of the predictions to formulate a robust model predictive control approach, enabling robust real-time advanced control of a slug flow crystallizer.

eess.SY↗

arXiv2025

A tutorial overview of model predictive control for continuous crystallization: current possibilities and future perspectives

This paper presents a systematic approach to the advanced control of continuous crystallization processes using model predictive control. We provide a tutorial introduction to controlling complex particle size distributions by integrating population balance equations with detailed models of various continuous crystallizers. Since these high-fidelity models are often too complex for online optimization, we propose the use of data-driven surrogate models that enable efficient optimization-based control. Through two case studies, one with a low-complexity system allowing direct comparison with traditional methods and another involving a spatially distributed crystallizer, we demonstrate how our approach enables real-time model predictive control while maintaining accuracy. The presented methodology facilitates the use of complex models in a model-based control framework, allowing precise control of key particle size distribution characteristics, such as the median particle size $d_{50}$ and the width $d_{90} - d_{10}$. This addresses a critical challenge in pharmaceutical and fine chemical manufacturing, where product quality depends on tight control of particle characteristics.

eess.SY↗