Leveraging Generative AI and Large Language Model for Process Systems Engineering: A State-of-the-Art Review

Abstract

Process systems engineering (PSE) has long been recognized as a critical discipline in chemical engineering for improving process efficiency 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, artificial 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 significant 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 offering 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 field into a new era of Chemical Engineering 4.0.