  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 Process Flow Diagrams (PFDs) into
Process and Instrumentation Diagrams (P&IDs). However, artificial intelligence
methods require big data and flowsheet data is currently limited. To mitigate
this challenge of limited data, we propose a new data augmentation methodology
for flowsheet data that is represented in the SFILES 2.0 notation. We show that
the proposed data augmentation improves the performance of artificial
intelligence-based process design models. In our case study flowsheet data
augmentation improved the prediction uncertainty of the flowsheet
autocompletion model by 14.7%. In the future, our flowsheet data augmentation
can be used for other machine learning algorithms on chemical process
flowsheets that are based on SFILES notation.
