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.

Piping and Instrumentation Diagrams (P&IDs) are the backbone of process engineering, yet they often feel impenetrable due to their intricate details and vast scope. While process engineers are trained to work with flowsheets, retrieving information can often be tough, time-consuming, and error-prone especially when dealing with bundles of flowsheets.

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.