# Automated Batch Distillation Process Simulation for a Large Hybrid Dataset for Deep Anomaly Detection

arXiv: https://arxiv.org/abs/2604.09166 · HTML: https://mlsanity.com/paper/2604.09166/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 57 references that are in the map.
- First posted on arXiv 10 Apr 2026.

## Builds on (most cited first, 9 of 9)

- [Deep Learning for Time Series Anomaly Detection: A Survey](https://mlsanity.com/paper/2211.05244/) (2022)
- [Anomaly Detection for a Water Treatment System Using Unsupervised Machine Learning](https://mlsanity.com/paper/1709.05342/) (2017)
- [Cross-Modal Learning for Anomaly Detection in Complex Industrial Process: Methodology and Benchmark](https://mlsanity.com/paper/2406.09016/) (2024)
- [Style Transfer: From Stitching to Neural Networks](https://mlsanity.com/paper/2409.00606/) (2024)
- [StyleTime: Style Transfer for Synthetic Time Series Generation](https://mlsanity.com/paper/2209.11306/) (2022)
- [Deep Anomaly Detection on Tennessee Eastman Process Data](https://mlsanity.com/paper/2303.05904/) (2023)
- [An equation-based batch distillation simulation to evaluate the effect of multiplicities in thermodynamic activity coefficients](https://mlsanity.com/paper/2506.21483/) (2025)
- [DiffStyleTS: Diffusion Model for Style Transfer in Time Series](https://mlsanity.com/paper/2510.11335/) (2025)
- [Batch Distillation Data for Developing Machine Learning Anomaly Detection Methods](https://mlsanity.com/paper/2510.18075/) (2025)

## Abstract (arXiv)

Anomaly detection (AD) in chemical processes based on deep learning offers significant opportunities but requires large, diverse, and well-annotated training datasets that are rarely available from industrial operations. In a recent work, we introduced a large, fully annotated experimental dataset for batch distillation under normal and anomalous operating conditions. In the present study, we augment this dataset with a corresponding simulation dataset, creating a novel hybrid dataset. The simulation data is generated in an automated workflow with a novel Python-based process simulator that employs a tailored index-reduction strategy for the underlying differential-algebraic equations. Leveraging the rich metadata and structured anomaly annotations of the experimental database, experimental records are automatically translated into simulation scenarios. After calibration to a single reference experiment, the dynamics of the other experiments are well predicted. This enabled the fully automated, consistent generation of time-series data for a large number of experimental runs, covering both normal operation and a wide range of actuator- and control-related anomalies. The resulting hybrid dataset is released openly. From a process simulation perspective, this work demonstrates the automated, consistent simulation of large-scale experimental campaigns, using batch distillation as an example. From a data-driven AD perspective, the hybrid dataset provides a unique basis for simulation-to-experiment style transfer, the generation of pseudo-experimental data, and future research on deep AD methods in chemical process monitoring.