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This research applies Large Language Models to automatically detect and suggest corrections for errors in chemical process flowsheets, aiming to reduce manual verification efforts. While the model demonstrates promising accuracy on synthetically generated data, its current reliance on synthetic datasets limits immediate applicability to real-world industrial scenarios. The approach offers a potential pathway for integrating generative AI into safety-critical engineering workflows, though further validation on actual process data is required.
Toward autocorrection of chemical process flowsheets using large language models
arXiv:2312.02873v2 Announce Type: replace 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.
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.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2312.02873 [cs.LG]
(or arXiv:2312.02873v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2312.02873 Focus to learn more arXiv-issued DOI via DataCite
https://doi.org/10.1016/B978-0-443-28824-1.50519-6 Focus to learn more DOI(s) linking to related resources
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