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.

Large language model (LLM)-based multi-agent systems (MASs) are a recent but rapidly evolving technology with the potential to transform chemical engineering by decomposing complex workflows into teams of collaborative agents with specialized knowledge and tools. This review surveys the state-of-the-art of MASs within chemical engineering. While early studies demonstrate promising results, scientific challenges remain, including the design of tailored architectures, integration of heterogeneous data modalities, development of foundation models with domain-specific modalities, and strategies for ensuring transparency, safety, and environmental impact. As a young but fast-moving field, MASs offer exciting opportunities to rethink chemical engineering workflows.

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.

Spray drying is one of the most widely used processes for converting liquids into powders, used in the production of chemicals, pharmaceuticals, and many food products. Yet operating multistage spray dryers reliably remains difficult: the process couples turbulent airflow, heat and mass transfer with phase change, droplet collisions, and the complex thermodynamics of multi-component formulations. Powder quality attributes such as moisture content and tapped density cannot be measured inline, while the dynamics of the dryer itself (long residence times in the fluidized beds, recycles, frequent changeovers) make the plant hard to model from first principles alone.