Learning from flowsheets: A generative transformer model for autocompletion of flowsheets
This work addresses the need for interactive flowsheet synthesis tools for chemical engineers, but it is incremental as it adapts existing text autocompletion methods to a new domain.
The authors tackled the problem of autocompleting chemical flowsheets by representing them as strings and using a transformer-based language model, achieving results that demonstrate high potential for AI-assisted process synthesis.
We propose a novel method enabling autocompletion of chemical flowsheets. This idea is inspired by the autocompletion of text. We represent flowsheets as strings using the text-based SFILES 2.0 notation and learn the grammatical structure of the SFILES 2.0 language and common patterns in flowsheets using a transformer-based language model. We pre-train our model on synthetically generated flowsheets to learn the flowsheet language grammar. Then, we fine-tune our model in a transfer learning step on real flowsheet topologies. Finally, we use the trained model for causal language modeling to autocomplete flowsheets. Eventually, the proposed method can provide chemical engineers with recommendations during interactive flowsheet synthesis. The results demonstrate a high potential of this approach for future AI-assisted process synthesis.