17 papers
Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning
Zeno Romero, Maximilian Kohns, Fabian Jirasek
Activities in aqueous electrolyte solutions, usually described by ionic activity and osmotic coefficients, are important properties for modeling many processes in industry and natu…
Automated Batch Distillation Process Simulation for a Large Hybrid Dataset for Deep Anomaly Detection
Jennifer Werner, Justus Arweiler, Indra Jungjohann +4
Anomaly detection (AD) in chemical processes based on deep learning offers significant opportunities but requires large, diverse, and well-annotated training datasets that are rare…
CHAOS -- A Consistent Large-scale Database for Sigma-Profiles and Other Molecular Descriptors
Dominik Gond, Justus Arweiler, Thomas Specht +2
Sigma-profiles obtained from quantum-chemical calculations are key molecular descriptors for solvent selection, thermodynamic modeling, and data-driven molecular design. However, e…
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…
Hybrid Machine Learning for Enhanced Prediction of Diffusion Coefficients in Liquids
Jens Wagner, Zeno Romero, Kerstin Münnemann +4
Diffusion coefficients are key thermophysical properties for modeling mass transport in liquids, but experimental data are scarce, making reliable prediction methods indispensable.…
Prediction of Diffusion Coefficients in Mixtures with Tensor Completion
Zeno Romero, Kerstin Münnemann, Hans Hasse +1
Predicting diffusion coefficients in mixtures is crucial for many applications, as experimental data remain scarce, and machine learning (ML) offers promising alternatives to estab…