Combustion versus Gasification in Power- and Biomass-to-X Processes: An Exergetic Analysis.

Residual biomass is a promising carbon feedstock for the production of electricity-based organic chemicals and fuels since, unlike carbon dioxide captured from point sources or air, it also has a valuable energy input. Biomass can be converted into an intermediate stream suitable for Power-to-X processes mainly via combustion or gasification. Such combined processes are generally called biohybrid or Power- and Biomass-to-X processes.

Thermodynamics-consistent graph neural networks.

We propose excess Gibbs free energy graph neural networks (GE-GNNs) for predicting composition-dependent activity coefficients of binary mixtures. The GE-GNN architecture ensures thermodynamic consistency by predicting the molar excess Gibbs free energy and using thermodynamic relations to obtain activity coefficients. As these are differential, automatic differentiation is applied to learn the activity coefficients in an end-to-end manner.

Overcoming the central planner approach - Bilevel optimization of the European energy transition.

The energy transition is a multinational challenge to mitigate climate change, with a joint reduction target for greenhouse gas emissions. Simultaneously, each country is interested in minimizing its own energy supply cost. Still, most energy system models neglect national interests when identifying cost-optimal transition pathways.

Predicting the Temperature Dependence of Surfactant CMCs Using Graph Neural Networks.

The critical micelle concentration (CMC) of surfactant molecules is an essential property for surfactant applications in the industry. Recently, classical quantitative structure-property relationship (QSPR) and graph neural networks (GNNs), a deep learning technique, have been successfully applied to predict the CMC of surfactants at room temperature. However, these models have not yet considered the temperature dependence of the CMC, which is highly relevant to practical applications.

Noisecut: a python package for noise-tolerant classification of binary data using prior knowledge integration and max-cut solutions.

Background: Classification of binary data arises naturally in many clinical applications, such as patient risk stratification through ICD codes. One of the key practical challenges in data classification using machine learning is to avoid overfitting. Overfitting in supervised learning primarily occurs when a model learns random variations from noisy labels in training data rather than the underlying patterns.