arXiv subjects

Justus Arweiler

Publications and source records attributed to Justus Arweiler.

6 recordsLinked to original sources

arXiv2026

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

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.

cs.LG↗

arXiv2025

CHAOS -- A Consistent Large-scale Database for Sigma-Profiles and Other Molecular Descriptors

Sigma-profiles obtained from quantum-chemical calculations are key molecular descriptors for solvent selection, thermodynamic modeling, and data-driven molecular design. However, existing sigma-profile libraries are limited in size and inconsistent in quality, which restricts their utility. In this work, we introduce CHAOS (Computed High-Accuracy Observables and Sigma Profiles), a large-scale and internally consistent database providing sigma-profiles for 53091 molecules, along with additional quantum-chemical observables including gas-phase geometries, single-point conductor-like polarizable continuum (C-PCM) data, infrared spectra, ideal-gas heat capacities and entropies, and atomic orbital nuclear magnetic resonance (NMR) shielding tensors. All data were generated using a standardized quantum-chemical workflow based on an wB97X-D/def2-TZVP level of theory. The CHAOS database covers molecules composed of a diverse set of elements, with molecular masses up to 400 amu and dipole moments up to 15 D, and is freely available on Zenodo under an open license. It extends the number of molecules for which sigma-profiles are publicly available by more than an order of magnitude and systematically links them to a broad range of other quantum-chemical molecular descriptors. CHAOS provides a comprehensive and consistent foundation for developing models of molecular and thermodynamic properties -- both physics-based and machine-learning approaches -- across chemistry, chemical engineering, and materials science, greatly extending the possibilities and the available quantum-chemical data basis.

physics.chem-ph↗

arXiv2025

Formally Exploring Time-Series Anomaly Detection Evaluation Metrics

Undetected anomalies in time series can trigger catastrophic failures in safety-critical systems, such as chemical plant explosions or power grid outages. Although many detection methods have been proposed, their performance remains unclear because current metrics capture only narrow aspects of the task and often yield misleading results. We address this issue by introducing verifiable properties that formalize essential requirements for evaluating time-series anomaly detection. These properties enable a theoretical framework that supports principled evaluations and reliable comparisons. Analyzing 37 widely used metrics, we show that most satisfy only a few properties, and none satisfy all, explaining persistent inconsistencies in prior results. To close this gap, we propose LARM, a flexible metric that provably satisfies all properties, and extend it to ALARM, an advanced variant meeting stricter requirements.

cs.LG↗

arXiv2025

Batch Distillation Data for Developing Machine Learning Anomaly Detection Methods

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.

cs.LG↗

arXiv2025

DiffStyleTS: Diffusion Model for Style Transfer in Time Series

Style transfer combines the content of one signal with the style of another. It supports applications such as data augmentation and scenario simulation, helping machine learning models generalize in data-scarce domains. While well developed in vision and language, style transfer methods for time series data remain limited. We introduce DiffTSST, a diffusion-based framework that disentangles a time series into content and style representations via convolutional encoders and recombines them through a self-supervised attention-based diffusion process. At inference, encoders extract content and style from two distinct series, enabling conditional generation of novel samples to achieve style transfer. We demonstrate both qualitatively and quantitatively that DiffTSST achieves effective style transfer. We further validate its real-world utility by showing that data augmentation with DiffTSST improves anomaly detection in data-scarce regimes.

cs.LG↗

arXiv2024

Prediction of Activity Coefficients by Similarity-Based Imputation using Quantum-Chemical Descriptors

In this work, we introduce a novel approach for predicting thermodynamic properties of binary mixtures, which we call the similarity-based method (SBM). The method is based on quantifying the pairwise similarity of components, which we achieve by comparing quantum-chemical descriptors of the components, namely $\sigma$-profiles. The basic idea behind the approach is that mixtures with similar pairs of components will have similar thermodynamic properties. The SBM is trained on a matrix that contains some data for a given property for different binary mixtures; the missing entries are then predicted by the SBM. As an example, we consider the prediction of isothermal activity coefficients at infinite dilution ($\gamma^\infty_{ij}$) and show that the SBM outperforms the well-established physical methods modified UNIFAC (Dortmund) and COSMO-SAC-dsp. In this case, the matrix is only sparsely occupied, and it is shown that the SBM works also if only a limited number of data for similar mixtures is available. The SBM idea can be transferred to any mixture property and is a powerful tool for generating essential data for many applications.

physics.chem-ph↗