Generative AI and Process Systems Engineering: The Next Frontier
Benjamin Decardi-Nelson Abdulelah S. Alshehri Akshay Ajagekar Fengqi You
This article explores how emerging generative artificial intelligence (GenAI)
models, such as large language models (LLMs), can enhance solution
methodologies within process systems engineering (PSE). These cutting-edge
GenAI models, particularly foundation models (FMs), which are pre-trained on
extensive, general-purpose datasets, offer versatile adaptability for a broad
range of tasks, including responding to queries, image generation, and complex
decision-making. Given the close relationship between advancements in PSE and
developments in computing and systems technologies, exploring the synergy
between GenAI and PSE is essential. We begin our discussion with a compact
overview of both classic and emerging GenAI models, including FMs, and then
dive into their applications within key PSE domains: synthesis and design,
optimization and integration, and process monitoring and control. In each
domain, we explore how GenAI models could potentially advance PSE
methodologies, providing insights and prospects for each area. Furthermore, the
article identifies and discusses potential challenges in fully leveraging GenAI
within PSE, including multiscale modeling, data requirements, evaluation
metrics and benchmarks, and trust and safety, thereby deepening the discourse
on effective GenAI integration into systems analysis, design, optimization,
operations, monitoring, and control. This paper provides a guide for future
research focused on the applications of emerging GenAI in PSE.