Toward autocorrection of chemical process flowsheets using large
  language models
Lukas Schulze Balhorn Marc Caballero Artur M. Schweidtmann
The process engineering domain widely uses Process Flow Diagrams (PFDs) and
Process and Instrumentation Diagrams (P&IDs) to represent process flows and
equipment configurations. However, the P&IDs and PFDs, hereafter called
flowsheets, can contain errors causing safety hazards, inefficient operation,
and unnecessary expenses. Correcting and verifying flowsheets is a tedious,
manual process. We propose a novel generative AI methodology for automatically
identifying errors in flowsheets and suggesting corrections to the user, i.e.,
autocorrecting flowsheets. Inspired by the breakthrough of Large Language
Models (LLMs) for grammatical autocorrection of human language, we investigate
LLMs for the autocorrection of flowsheets. The input to the model is a
potentially erroneous flowsheet and the output of the model are suggestions for
a corrected flowsheet. We train our autocorrection model on a synthetic dataset
in a supervised manner. The model achieves a top-1 accuracy of 80% and a top-5
accuracy of 84% on an independent test dataset of synthetically generated
flowsheets. The results suggest that the model can learn to autocorrect the
synthetic flowsheets. We envision that flowsheet autocorrection will become a
useful tool for chemical engineers.