5 papers
Composition-Dependent Self-Diffusion Coefficients in Liquid Mixtures from Hybrid Machine Learning
Jens Wagner, Thomas Specht, Hans Hasse +1
Self-diffusion coefficients are key descriptors of molecular mobility, yet experimental data remain scarce, highlighting the need for reliable prediction methods. In previous work,…
Measurements of Diffusion Coefficients of CO in 1-Butanol with a New Laminar Jet Apparatus and PFG-NMR, Pointing to Interfacial Mass Transfer Effects
Oliver Großmann, Sarah Mross, Jens Wagner +3
A novel laminar jet apparatus (LJA) was constructed for precise gas-liquid mass transfer measurements up to 12 bar, significantly extending the technique's operating window. It was…
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…
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…