# Toward autocorrection of chemical process flowsheets using large language models

arXiv: https://arxiv.org/abs/2312.02873 · HTML: https://mlsanity.com/paper/2312.02873/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 5 Dec 2023.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Toward automatic generation of control structures for process flow diagrams with large language models](https://mlsanity.com/paper/2211.05583/) (2022)
- [SFILES 2.0: An extended text-based flowsheet representation](https://mlsanity.com/paper/2208.00778/) (2022)
- [Learning from flowsheets: A generative transformer model for autocompletion of flowsheets](https://mlsanity.com/paper/2208.00859/) (2022)
- [Controllable Data Synthesis Method for Grammatical Error Correction](https://mlsanity.com/paper/1909.13302/) (2019)
- [The Unreasonable Effectiveness of Transformer Language Models in Grammatical Error Correction](https://mlsanity.com/paper/1906.01733/) (2019)

## Built on by (most cited first, 6 of 6)

- [LLM-guided Chemical Process Optimization with a Multi-Agent Approach](https://mlsanity.com/paper/2506.20921/) (2025)
- [Graph-to-SFILES: Control structure prediction from process topologies using generative artificial intelligence](https://mlsanity.com/paper/2412.00508/) (2024)
- [Federated Learning from Molecules to Processes: A Perspective](https://mlsanity.com/paper/2506.18525/) (2025)
- [Rule-based autocorrection of Piping and Instrumentation Diagrams (P&IDs) on graphs](https://mlsanity.com/paper/2502.18493/) (2025)
- [Automating Cause-Effect Specification with Knowledge Graphs and Large Language Models](https://mlsanity.com/paper/2606.31614/) (2026)
- [CRAFTS: Collaborative Role-Adaptive Fine-Tuning of LLM Agents for Chemical Process Simulation](https://mlsanity.com/paper/2608.01369/) (2026)

## Abstract (arXiv)

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.