# Batch Distillation Data for Developing Machine Learning Anomaly Detection Methods

arXiv: https://arxiv.org/abs/2510.18075 · HTML: https://mlsanity.com/paper/2510.18075/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 55 references that are in the map.
- First posted on arXiv 20 Oct 2025.

## Builds on (most cited first, 14 of 14)

- [Multivariate Time-series Anomaly Detection via Graph Attention Network](https://mlsanity.com/paper/2009.02040/) (2020)
- [Deep Learning for Time Series Anomaly Detection: A Survey](https://mlsanity.com/paper/2211.05244/) (2022)
- [Towards a Rigorous Evaluation of Time-series Anomaly Detection](https://mlsanity.com/paper/2109.05257/) (2021)
- [SOSA: A Lightweight Ontology for Sensors, Observations, Samples, and Actuators](https://mlsanity.com/paper/1805.09979/) (2018)
- [Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completion](https://mlsanity.com/paper/2001.10675/) (2020)
- [Anomaly Detection for a Water Treatment System Using Unsupervised Machine Learning](https://mlsanity.com/paper/1709.05342/) (2017)
- [HANNA: Hard-constraint Neural Network for Consistent Activity Coefficient Prediction](https://mlsanity.com/paper/2407.18011/) (2024)
- [Variational Inference for On-line Anomaly Detection in High-Dimensional Time Series](https://mlsanity.com/paper/1602.07109/) (2016)
- [Cross-Modal Learning for Anomaly Detection in Complex Industrial Process: Methodology and Benchmark](https://mlsanity.com/paper/2406.09016/) (2024)
- [Advancing Thermodynamic Group-Contribution Methods by Machine Learning: UNIFAC 2.0](https://mlsanity.com/paper/2408.05220/) (2024)
- [GRAPPA -- A Hybrid Graph Neural Network for Predicting Pure Component Vapor Pressures](https://mlsanity.com/paper/2501.08729/) (2025)
- [Deep Anomaly Detection on Tennessee Eastman Process Data](https://mlsanity.com/paper/2303.05904/) (2023)
- [KnowTD-An Actionable Knowledge Representation System for Thermodynamics](https://mlsanity.com/paper/2407.17169/) (2024)
- [Hierarchical Matrix Completion for the Prediction of Properties of Binary Mixtures](https://mlsanity.com/paper/2410.06060/) (2024)

## Built on by (most cited first, 1 of 1)

- [Automated Batch Distillation Process Simulation for a Large Hybrid Dataset for Deep Anomaly Detection](https://mlsanity.com/paper/2604.09166/) (2026)

## Abstract (arXiv)

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.