Batch Distillation Data for Developing Machine Learning Anomaly Detection Methods

This database provides a resource for machine-learning-based anomaly detection (AD) in chemical processes. It includes data from 119 experimental runs on a laboratory-scale batch distillation plant across diverse operating conditions and mixtures, with paired fault-free runs and deliberately induced anomalies. For each experiment, we provide multivariate time-series from sensors and actuators with measurement-uncertainty estimates, complemented by unconventional modalities: online benchtop NMR concentration profiles, image, and audio recordings. Every anomalous experiment is richly documented with extensive metadata and expert annotations. This metadata captures both the presence and causes of anomalies. The data are organized in a structured, ready-to-use format and made freely available to support benchmarking and development of advanced AD methods. By linking anomalies to their underlying causes, the database also enables research on interpretable and explainable machine learning (ML), as well as strategies for anomaly mitigation.