Reactor design and optimization are crucial aspects of chemical and biochemical engineering. With the advent of additive manufacturing, advanced reactor geometries are now possible, offering improved operational efficiency and cost-effectiveness. However, due to this extra flexibility, optimizing over these designs is even more challenging. In this talk, we discuss work that integrates computational fluid dynamics (CFD) simulations with a multi-fidelity Bayesian optimization. We introduce an approach that not only recommends optimal reactor configurations and operating conditions but also determines fidelity (level of accuracy of the CFD simulator) levels based on statistical likelihoods and information content, optimizing accuracy and computational efficiency. The methodology discussed focuses on plug-flow reactors but can be extended to various reactor types. By maximizing plug-flow performance, we identify crucial design characteristics and validate two novel geometries through 3D printing and experimental validation. Through this data-driven optimization of highly parameterized reactors, we aim to establish a framework for next-generation reactors, highlighting how machine learning and advanced manufacturing processes can revolutionize the performance and sustainability of future chemical processes.

Particles shape our world and everyday life: Macroscopic material and product properties are determined by interactions and processes at the microscopic level, such as agglomeration and breakage. When multiple materials come together, the resulting heterogeneous structures (hetero-agglomerates) are more than just the sum of their parts. One example is the microstructure in lithium-ion batteries, where active material and carbon black particles are combined and structured to provide both high capacity and good electrical conductivity.  Describing these heterogeneous systems poses its own challenges: Under what conditions do hetero-agglomerates form? How can they be characterized? How are the surface properties of a hetero-agglomerate defined? How and when do hetero-agglomerates break? The recently founded research group “Particle Dynamics in Heterogeneous Systems” (www.pdhs-group.com/) aims to answer these questions both theoretically and experimentally. This talk gives an overview of our current projects and future ideas. It focuses on challenges, solution strategies, but above all on questions that still need to be answered. Selected (more specific) areas include population balance equations (PBE), data integration via machine learning (hybrid models), continuous extrusion of battery slurries, spray flame synthesis, and multi-scale characterization of hetero-aggregates. Vortragssprache: deutsch