107 citations · 784 across the 32 of their papers we have counts for
36 papers
Deep Anomaly Detection on Tennessee Eastman Process Data
Fabian Hartung, Billy Joe Franks, Tobias Michels +15
This paper provides the first comprehensive evaluation and analysis of modern (deep-learning) unsupervised anomaly detection methods for chemical process data. We focus on the Tenn…
Making Thermodynamic Models of Mixtures Predictive by Machine Learning: Matrix Completion of Pair Interactions
Fabian Jirasek, Robert Bamler, Sophie Fellenz +4
Predictive models of thermodynamic properties of mixtures are paramount in chemical engineering and chemistry. Classical thermodynamic models are successful in generalizing over (c…
Attribute-based Explanations of Non-Linear Embeddings of High-Dimensional Data
Jan-Tobias Sohns, Michaela Schmitt, Fabian Jirasek +2
Embeddings of high-dimensional data are widely used to explore data, to verify analysis results, and to communicate information. Their explanation, in particular with respect to th…
Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completion
Fabian Jirasek, Rodrigo A. S. Alves, Julie Damay +6
Activity coefficients, which are a measure of the non-ideality of liquid mixtures, are a key property in chemical engineering with relevance to modeling chemical and phase equilibr…
MolMod: An open access database of force fields for molecular simulations of fluids
Simon Stephan, Martin Thomas Horsch, Jadran Vrabec +1
The MolMod database is presented, which is openly accessible at http://molmod.boltzmann-zuse.de/ and contains presently intermolecular force fields for over 150 pure fluids. It was…
Comparison of predictions of the PC-SAFT equation of state and molecular simulations for the metastable region of binary mixtures
Alexander Keller, Kai Langenbach, Hans Hasse
The metastable region of binary mixtures is examined with the PC-SAFT equa-tion of state (EOS) and compared to molecular simulations. The studied mixtures are Methane + Ethane, Met…