The goal of flowsheet digitization is to extract the flowsheet topologies from the flowsheet images and save them in a graph format.
Graph neural networks (GNNs) are a machine learning method that has shown promising results for the prediction of structure-property relationships.
Integrating knowledge into AI is of utmost importance in chemical engineering.
Knowledge graphs link our data in a meaningful way.
Data-driven surrogate models can learn nonlinear input-output relations and replace expensive simulations or experiments in optimization studies.
Physics-informed neural networks (PINNs) enforce physical laws that are described by general nonlinear partial differential equations during training. This approach drastically reduces the data demand and prevents overfitting. We explore the potential of physics-informed neural networks in bioengineering.
We develop machine learning algorithm for the classification of images from scientific publications.
Autonomous reaction platforms and robots are the future of chemistry and biotechnology laboratories.