Process systems engineering (PSE) has long been recognized as a critical discipline in chemical engineering for improving

process effi ciency through mathematical modeling, optimization, and control. The advent of Industry 4.0 has advanced PSE

by integrating it with innovative digital tools, including big data analytics, artifi cial intelligence (AI), and machine learning.

In this context, large language models (LLMs), which are state-of-the-art AI techniques, represent transformative generative

AI (GenAI) technologies capable of advancing automation, process optimization, and knowledge extraction in PSE. However,

the application of LLMs in PSE is in its nascent stage and is constrained by challenges, such as data quality, interpretability,

and scalability. Nonetheless, the application of LLMs is expected to foster signifi cant progress in PSE research, including

chemical process design, hybrid process modeling, autonomous control systems, and multiscale optimization. This review

aims to provide an introduction to LLM and GenAI and explore how LLMs have been utilized to overcome the traditional

limitations of PSE research by off ering innovative digital solutions, such as data enrichment and seamless integration with

digital twins. This study highlights the potential of LLMs to transform PSE methodologies and lead the fi eld into a new era

of Chemical Engineering 4.0.