Automated Anomaly Detection in a Batch Distillation Plant Using Machine Learning

The Research Unit 5359  is developing deep learning methods for anomaly detection in the operation of process plants. These methods require large amounts of training data, but such data are not yet available for chemical processes. I am therefore working on generating diverse process data by operating a laboratory-scale batch distillation plant equipped with a comprehensive set of sensors. To allow the deep learning methods to be not only trained but also tested, faulty process data are generated by deliberately introducing anomalies, in addition to process data from normal plant operation. Furthermore, the research group is working on the development of hybrid generative models that also incorporate physical knowledge of batch distillation. The aim is to apply these models to create an extensive hybrid dataset that will serve as the basis for the further development and validation of the deep learning methods.

On the path towards automated anomaly detection, real-time processing of the data generated by the process is essential. For this reason, I am also working on implementing a suitable process control system for the batch distillation plant, along with appropriate data management structures. The goal is to process the data recorded by the various sensors in a structured way and make them available, so that they can be used for the development and future application of the deep learning methods during ongoing plant operation.

Information on possible topics for student theses and projects supervised by me or my colleagues can be found here.

Lecture Support

• Process Thermodynamics (WS 2025/26)
• Data Evaluation and Design of Experiments (SoSe 2025)
• Heat Transfer (WS 2024/25)

Office Hours

By arrangement.

Publications

• M. Nagda, P. Ostheimer, J. Arweiler, I. Jungjohann, J. Werner, D. Wagner, A. Muraleedharan, P. Jafari, J. Schmid, F. Jirasek, J. Burger, M. Bortz, H. Hasse, S. Mandt, M. Kloft, S. Fellenz: Style Transfer for High-Fidelity Time Series Augmentation, In: Springer Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2025. Communications in Computer and Information Science 2841 (2026) 295-316. [DOI]
• S. Lutz, J. Arweiler, A. Muraleedharan, N. Kahlhoff, F. Hartung, I. Jungjohann, M. Nagda, D. Reinhardt, D. Wagner, J. Werner, J. Will, J. Burger, M. Bortz, H. Hasse, S. Fellenz, F. Jirasek, M. Kloft, H. Leitte, S. Mandt, S. Reithermann, J. Schmid, D. Neider: A Benchmark Suite for Verifying Neural Anomaly Detectors in Distillation Processes, In: Springer Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2024. Communications in Computer and Information Science 2559 (2026) 290-306. [DOI]
• J. Arweiler, I. Jungjohann, A. Muraleedharan, H. Leitte, J. Burger, K. Münnemann, F. Jirasek, H. Hasse: Batch Distillation Data for Developing Machine Learning Anomaly Detection Methods, Sci. Data 13, 513 (2026). [DOI]
• D. Wagner, F. Hartung, J. Arweiler, A. Muraleedharan, I. Jungjohann, A. Nair, S. Reithermann, R. Schulz, M. Bortz, D. Neider, H. Leitte, J. Pfeffinger, S. Mandt, S. Fellenz, T. Katz, F. Jirasek, J. Burger, H. Hasse, M. Kloft: NoBOOM: Chemical Process Datasets for Industrial Anomaly Detection, In: Advances in Neural Information Processing Systems 38 (2025).

Presentations

•  I. Jungjohann, J. Arweiler, J. Werner, J. Schmid, D. Wagner, F. Hartung, M. Kloft, H. Leitte, M. Bortz, F. Jirasek, H. Hasse: Anomaly Detection in Batch Distillation with Machine Learning – Influence of Data Preprocessing and Augmentation, European Symposium on Computer Aided Process Engineering (ESCAPE36), Sheffield, UK, 21.06.-24.06.2026
•  I. Jungjohann, J. Arweiler, J. Werner, J. Schmid, D. Wagner, F. Hartung, H. Leitte, M. Kloft, M. Bortz, F. Jirasek, H. Hasse : A Dataset of Normal and Anomalous Batch Distillation Data for Anomaly Detection, Joint Annual Meeting of the DECHEMA/VDI Specialist Groups on “Fluid Process Engineering” with “Heat and Mass Transfer”, Luzern, Switzerland, 09.03.-11.03.2026.

Poster

• I. Jungjohann, J. Arweiler, J. Werner, A. Muraleedharan, J. Schmid, H. Leitte, J. Burger, M. Bortz, H. Hasse, F. Jirasek: Hybrid Batch-Distillation Dataset for Deep Anomaly Detection in Chemical Processes, Thermodynamik-Kolloqium, Dortmund, 30.09.-02.10.2026.
• I. Jungjohann, J. Arweiler, J. Werner, H. Leitte, M. Bortz, F. Jirasek, H. Hasse: Batch Distillation Dataset for Deep Anomaly Detection in Chemical Processes, Annual Meeting of Process Engineering and Materials Technology, Frankfurt, 10-11.11.2025.
• I. Jungjohann, J. Arweiler, J. Werner, A. Muraleedharan, H. Leitte, J. Burger, M. Bortz, F. Jirasek, H. Hasse: Batch Distillation Data for Deep Anomaly Detection, Thermodynamik-Kolloqium, Hamburg, 22-24.09.2025.
• J. Arweiler, I. Jungjohann, A. Muraleedharan, F. Hartung, D. Wagner, M. Kloft, J. Burger, F. Jirasek, H. Hasse: Anomaly Detection on Chemical Process Data, Thermodynamik-Kolloqium, Stuttgart, 25.-27.09.2024.
• J. Will, J. Arweiler, I. Jungjohann, J. Werner, M. Nagda, M. Bortz, H. Hasse, F. Jirasek, J. Schmid, M. Kloft, S. Mandt, S. Fellenz: Enhancing Realism in Batch Distillation Simulations: Data-Efficient Time Series Style Transfer with Transformers, Workshop on Machine Learning for Chemistry and Chemical Enginnering at European Conference on Machine Learning, Vilnius, Lithuania, 09.09.2024.
• S. Lutz, J. Arweiler, A. Muraleedharan, N. Kahlhoff, F. Hartung, I. Jungjohann, M. Nagda, D. Reinhardt, D. Wagner, J. Werner, J. Will, J. Burger, M. Bortz, H. Hasse, S. Fellenz, F. Jirasek, M. Kloft, H. Leitte, S. Mandt, S. Reithermann, J. Schmid, D. Neider: A Benchmark Suite for Verifying Neural Anomaly Detectors in Distillation Processes, Workshop on Machine Learning for Chemistry and Chemical Enginnering at European Conference on Machine Learning, Vilnius, Lithuania, 09.09.2024.
• M. J. Peter, N. Ghanooni, F. Hartung, B. J. Franks, D. Wagner, P. Liznerski, A. Muraleedharan, J. Arweiler, D. Reinhardt, I. Jungjohann, S. Reithermann, S. Fellenz, F. Jirasek, M. Rudolph, D. Neider, F. Rhein, H. Leitte, C. Song, S. Mandt, M. Bortz, J. Burger, H. Hasse, M. Kloft: Anomaly Classification of Tennessee-Eastman Process Data, Workshop on Machine Learning for Chemistry and Chemical Enginnering at European Conference on Machine Learning, Vilnius, Lithuania, 09.09.2024.
• D. Reinhardt, D. Wagner, A. Muraleedharan, J. Arweiler, I. Jungjohann, F. Jirasek, J. Burger, H. Hasse, M. Kloft, H. Leitte: cPAX: Comparative Visualization of Known and Novel Anomalies for Monitoring Chemical Plants, Workshop on Machine Learning for Chemistry and Chemical Enginnering at European Conference on Machine Learning, Vilnius, Lithuania, 09.09.2024.
• J. Arweiler, A. Muraleedharan, F. Hartung, I. Jungjohann, D. Wagner, M. Kloft, J. Burger, H. Hans, F. Jirasek: Anomaly Detection on Experimental Chemical Process Data, Workshop on Machine Learning for Chemistry and Chemical Enginnering at European Conference on Machine Learning, Vilnius, Lithuania, 09.09.2024.