# Large Language Models in Process Systems Engineering: Opportunities, Architectures, and Industrial Deployment Challenges

arXiv: https://arxiv.org/abs/2606.11589 · HTML: https://mlsanity.com/paper/2606.11589/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2026.

## Built on by (most cited first, 1 of 1)

- [When LLMs Sit Above Diagnostic Tools: Unrealized Complementarity in Industrial Fault Diagnosis](https://mlsanity.com/paper/2610.05031/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have rapidly emerged as tools of interest across engineering disciplines, and Process Systems Engineering (PSE) is no exception. This survey provides a systematic review of LLM applications in PSE, organizing the literature into seven categories: (1) process design and engineering, (2) molecular design and synthesis, (3) process modeling and simulation, (4) time-series forecasting, (5) optimization and scheduling, (6) process control, and (7) fault detection and diagnosis. For each category, we summarize the state of the art, identify common methodological approaches, and critically assess demonstrated capabilities versus aspirational claims. We find that LLMs show genuine promise for tasks involving natural language, including querying documentation, synthesizing unstructured knowledge, and enabling flexible human-machine interaction. However, applications requiring real-time execution, constraint satisfaction, or formal safety guarantees remain challenging. We conclude by identifying open problems and productive research directions for the PSE community.