Data Generation and Knowledge-based Augmentation: Batch Distillation

• Applicants: Professor Dr. Michael Bortz; Professor Dr.-Ing. Hans Hasse; Professor Dr.-Ing. Fabian Jirasek
• Subject Area: Technical Thermodynamics ; Chemical and Thermal Process Engineering
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 459419731

Batch distillation is one of the most important processes in the chemical industry. Nevertheless, experimental data on the operation of batch distillation plants that could be used to develop and train machine learning methods is lacking in the open literature. Therefore, such data will be generated in the present project in a laboratory scale batch distillation column, which is equipped with advanced sensors, including an online NMR spectrometer and cameras. Additional data will be generated by simulations of batch distillation processes based on a dynamic physical model. Cases with different anomalies as well as anomaly-free cases will be studied with both methods for a wide range of operating strategies and conditions; and separations of many different fluid mixtures will be investigated, including poorly specified mixtures. The generated data will also comprise information on the uncertainties. Methods from the field of design of experiments will be used to plan the laboratory distillations as well as the simulations. The full data sets will be made publicly available. The relations between the experimental data and the simulation data will be considered in detail and the two types of data will be merged to hybrid data sets. This project will also provide the physical knowledge on batch distillation processes needed in the projects of Research Area A of this Research Unit. Our ambition is to supply the complex and heterogeneous data generated in the project in a way that is optimal for anomaly detection in chemical processes. In the project, a new holistic approach to batch distillation will be explored that could be fruitful far beyond anomaly detection.

• DFG Programme: Research Units
• Subproject of: FOR 5359: KI-FOR: Deep Learning on Sparse Chemical Process Data
• International Connection: USA
• Cooperation Partner: Professor Lorenz T. Biegler, Ph.D.