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Batch Distillation Data For Developing Machine Learning Anomaly Detection Methods.

Home
Batch Distillation Data For Developing Machine Learning Anomaly Detection Methods.

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Batch Distillation Data for Developing Machine Learning Anomaly Detection Methods.

Justus Arweiler¹, Indra Jungjohann¹, Aparna Muraleedharan²

¹Laboratory of Engineering Thermodynamics, RPTU Kaiserslautern, Erwin-Schrödinger-Straße 44, 67663, Kaiserslautern, Germany.

Scientific Data

|March 31, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

Researchers created a new dataset for machine learning anomaly detection in chemical processes. This freely available data includes diverse sensor readings and expert annotations to train advanced methods.

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Area of Science:

Chemical Engineering
Data Science
Process Control

Background:

Machine learning (ML) shows promise for anomaly detection (AD) in chemical processes.
Development of ML-based AD methods is limited by the scarcity of public experimental data.

Purpose of the Study:

To address the data gap for ML-based AD in chemical processes.
To generate a comprehensive experimental dataset for training and validating ML models.
To enable the development of interpretable and explainable AD methods.

Main Methods:

Established a laboratory-scale batch distillation plant for data generation.
Conducted 119 experiments with varying operating conditions and mixtures, including induced anomalies.
Collected time-series sensor/actuator data, measurement uncertainty, NMR spectroscopy, video, and audio recordings.

Main Results:

Generated an extensive, structured dataset with fault-free and anomalous experiments.
Included detailed metadata, expert annotations, and an anomaly ontology.
Dataset is publicly available, facilitating ML-based AD research.

Conclusions:

The new dataset supports the advancement of ML-based AD in chemical processes.
Enables development of interpretable, explainable, and mitigable AD solutions.
Promotes further research in data-driven process monitoring and control.