Batch Distillation Data for Developing Machine Learning Anomaly Detection Methods

TL;DR

This paper presents a comprehensive, publicly available database of experimental data from a laboratory-scale batch distillation plant, designed to facilitate the development of machine learning-based anomaly detection methods in chemical processes.

Contribution

The authors created and shared an extensive, annotated dataset of batch distillation experiments, including anomalies, sensor data, and unconventional sources like NMR and video recordings.

Findings

119 experiments conducted across various conditions and mixtures.

Dataset includes sensor data, NMR, video, and audio recordings.

Annotations and metadata enable development of interpretable ML anomaly detection.

Abstract

Machine learning (ML) holds great potential to advance anomaly detection (AD) in chemical processes. However, the development of ML-based methods is hindered by the lack of openly available experimental data. To address this gap, we have set up a laboratory-scale batch distillation plant and operated it to generate an extensive experimental database, covering fault-free experiments and experiments in which anomalies were intentionally induced, for training advanced ML-based AD methods. In total, 119 experiments were conducted across a wide range of operating conditions and mixtures. Most experiments containing anomalies were paired with a corresponding fault-free one. The database that we provide here includes time-series data from numerous sensors and actuators, along with estimates of measurement uncertainty. In addition, unconventional data sources -- such as concentration profiles obtained via online benchtop NMR spectroscopy and video and audio recordings -- are provided. Extensive metadata and expert annotations of all experiments are included. The anomaly annotations are based on an ontology developed in this work. The data are organized in a structured database and made freely available via doi.org/10.5281/zenodo.17395543. This new database paves the way for the development of advanced ML-based AD methods. As it includes information on the causes of anomalies, it further enables the development of interpretable and explainable ML approaches, as well as methods for anomaly mitigation.