Research projects

Our research project is a pioneering endeavor aimed at harnessing the power of Artificial Intelligence (AI) to revolutionize Hazard and Operability (HAZOP) studies and significantly enhance safety measures. \

We propose a novel method enabling autocompletion of engineering diagrams such as flowsheets. This idea is inspired by the autocompletion of text.

Automatically correcting errors is already standard for text documents. We develop this technology for engineering diagrams such as Piping and Instrumentation Diagrams (P&IDs), process flow diagrams (PFDs), or flowsheets.

We automatically generate engineering diagrams.

The goal of flowsheet digitization is to extract the flowsheet topologies from the flowsheet images and save them in a graph format.

Generative artificial intelligence (AI) is transforming several sectors. This Comment provides a viewpoint outlining the potential significance of generative AI for chemical process engineering. Moreover, challenges for future research and development are outlined.

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.

Autonomous reaction platforms and robots are the future of chemistry and biotechnology laboratories.