News

• 01.11.2022

  
  

  FOR 5359: DFG Research Group KI-FOR FOR 5359: Deep Learning on sparse chemical process data

  Prof. Dr. Marius Kloft

• 31.12.2023

  
  

  Research Visits at UC Irvine

  Research Visits at UC Irvine of several members of FOR 5359:
  Fabian Jirasek (Feb–Apr 2023), Nicolas Hayer (Jun–Aug 2023), and Thomas Specht (Jul–Nov 2023).

• 30.04.2024

  
  

  Lab rotations at TUM Straubing

  Coordinated rotation of five PhD students to TUM Straubing in April 2024,
  where they jointly operated and analyzed the continuous batch distillation plant.

• 18.09.2025

  
  

  NoBOOM: Chemical Process Datasets for Industrial Anomaly Detection

  Published NeurIPS Paper

  Abstract:
  Monitoring chemical processes is essential to prevent catastrophic failures, optimize costs and profits, and ensure the safety of employees and the environment. A key component of modern monitoring systems is the automated detection of anomalies in sensor data over time, called time series, enabling partial automation of plant operation and adding additional layers of supervision to crucial components. The development of anomaly detection methods in this domain is challenging, since real chemical process data are usually proprietary, and simulated data are generally not a sufficient replacement. In this paper, we present NoBOOM, the first collection of datasets for anomaly detection in real-world chemical process data, including labeled data from a running process at our industry partner BASF SE — one of the world’s leading chemical companies — and several chemical processes run in laboratory‑scale and pilot‑scale plants. While we are not able to share every detail about the industrial process, for the laboratory‑ and pilot‑scale plants, we provide comprehensive information on plant configuration, process operation, and, in particular, anomaly events, enabling a differentiated analysis of anomaly detection methods. To demonstrate the complexity of the benchmark, we analyze the data with regard to common issues of time-series anomaly detection (TSAD) benchmarks, including potential triviality and bias.

• 03.02.2026

  
  

  Paper on SetPINNS distinguished as one of 2.5% of spotlight papers at AISTATS 2026