Toward autocorrection of chemical process flowsheets using large language models

TL;DR

This paper introduces a novel AI-based method using large language models to automatically detect and correct errors in chemical process flowsheets, aiming to improve safety and efficiency.

Contribution

It adapts LLMs for flowsheet autocorrection, demonstrating effective synthetic data training and promising accuracy in error correction tasks.

Findings

Top-1 accuracy of 80% on synthetic flowsheets

Top-5 accuracy of 84% on test dataset

Potential to assist chemical engineers in error correction

Abstract

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.