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.
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.
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.