Project Description
Development of an Autonomous Laboratory for Thermophysical Properties
Process design and optimization in the chemical industry depend on reliable thermophysical property data for pure components and mixtures. Because only a small fraction of the relevant substances and mixtures can ever be studied experimentally, predictive property models are essential. Besides established physical prediction methods, machine learning has increasingly been applied in recent years, leading to the development of hybrid models. Whatever the approach, however, the quality of a property model ultimately depends on the availability of suitable experimental data for its development and training.
My project aims to develop and build an autonomous property laboratory that performs automated, high-throughput measurements of pure-component and mixture properties. These data form the basis for developing predictive property models. My work covers the design and construction of automated measurement setups, including the integration of laboratory robots, the structured storage of measurement data in a database, and the coupling of experiments with model training through design-of-experiments methods, closing the loop between measurement and prediction.
Lecture Support and Office Hours
Lecture Support
- Elektrochemische Energiespeicherung (SS 25)
- Modellierung, Simulation und Optimierung in der Verfahrenstechnik (WS 25/26)
- Datenauswertung und Versuchsplanung (SS 26)
- Grundlagenlabor für das Schwerpunktfach I (SS 26)
- Thermodynamik der Mischungen (WS 26/27)
Office Hours
By arrangement