Batch Distillation Data for Developing Machine Learning Anomaly Detection Methods

2025-10-20

AI Summary

Chemical process anomaly detection is hindered by the scarcity of publicly available, high-quality experimental datasets. To address this, we developed a laboratory-scale batch distillation platform and collected 119 multimodal time-series datasets encompassing normal operations and human-introduced anomalies, including multi-source sensor signals, online benchtop NMR concentration spectra, audiovisual recordings, and uncertainty estimates for measurements. We propose the first ontology-based, structured anomaly annotation framework, employing a paired design (anomalous–normal) to facilitate root-cause analysis and model interpretability. Data were standardized via metadata harmonization and rigorously modeled for measurement uncertainty. The resulting database is publicly released on Zenodo (DOI: 10.5281/zenodo.17395544). This dataset represents the first open-source chemical process anomaly benchmark featuring expert-curated annotations, quantitative uncertainty characterization, and multimodal integration—significantly advancing research in interpretable AI modeling and intelligent process monitoring.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Machine Learning: Calibration & Uncertainty Quantification
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Security and Privacy: Data transparency and provenance
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs

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.17395544. 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.

Problem

• Addressing the lack of experimental data for machine learning anomaly detection
• Providing extensive batch distillation data with intentional anomalies and metadata
• Enabling development of interpretable ML methods for chemical process safety

Innovation

• Established laboratory-scale batch distillation plant
• Generated extensive experimental database with anomalies
• Included unconventional data sources like NMR spectroscopy