5 papers
Batch Distillation Data for Developing Machine Learning Anomaly Detection Methods
Justus Arweiler, Indra Jungjohann, Aparna Muraleedharan +5
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…
Formally Exploring Time-Series Anomaly Detection Evaluation Metrics
Dennis Wagner, Arjun Nair, Billy Joe Franks +24
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 m…
Superstudent intelligence in thermodynamics
Rebecca Loubet, Pascal Zittlau, Marco Hoffmann +6
In this short note, we report and analyze a striking event: OpenAI's large language model o3 has outwitted all students in a university exam on thermodynamics. The thermodynamics e…
Using Large Language Models for Solving Thermodynamic Problems
Rebecca Loubet, Pascal Zittlau, Luisa Vollmer +5
Large Language Models (LLMs) have made significant progress in reasoning, demonstrating their capability to generate human-like responses. This study analyzes the problem-solving c…
Hierarchical Matrix Completion for the Prediction of Properties of Binary Mixtures
Dominik Gond, Jan-Tobias Sohns, Heike Leitte +2
Predicting the thermodynamic properties of mixtures is crucial for process design and optimization in chemical engineering. Machine learning (ML) methods are gaining increasing att…