Brief

Researchers have developed a novel method for autocompleting chemical flowsheets using a transformer-based language model. The approach represents flowsheets as strings and trains the model on their grammatical structure and common patterns. After pre-training on synthetic data and fine-tuning on real-world examples, the model can suggest completions for flowsheets, aiding chemical engineers in process synthesis. AI

Researchers have introduced SFILES 2.0, an enhanced text-based notation for representing chemical process flowsheets. This new version addresses limitations of the original SFILES, enabling unambiguous descriptions of essential configurations and control structures crucial for process operation. The development includes open-source software for converting between graph-based flowsheets and SFILES 2.0 strings, aiming to establish a standard for a FAIR (Findable, Accessible, Interoperable, Reusable) database of chemical process flowsheets. AI