Transfer learning for process design with reinforcement learning

Process design is a creative task that is currently performed manually by engineers. Artificial intelligence provides new potential to facilitate process design. Specifically, reinforcement learning (RL) has shown some success in automating process design by integrating data-driven models that learn to build process flowsheets with process...

An Educational Workshop for Effective PSE Course Development

An educational workshop for developing Process Systems Engineering (PSE) courses will be held during ESCAPE-33, following the model workshop that was run during the CAPE Forum 2022 held at the University of Twente, in the Netherlands. This 3-hour workshop distributes the participants into four teams working together to develop the outline of...

Data augmentation for machine learning of chemical process flowsheets

Artificial intelligence has great potential for accelerating the design and engineering of chemical processes. Recently, we have shown that transformer-based language models can learn to auto-complete chemical process flowsheets using the SFILES 2.0 string notation. Also, we showed that language translation models can be used to translate...

Flowsheet Recognition using Deep Convolutional Neural Networks

Flowsheets are the most important building blocks to define and communicate the structure of chemical processes. Gaining access to large data sets of machine-readable chemical flowsheets could significantly enhance process synthesis through artificial intelligence. A large number of these flowsheets are publicly available in the scientific...