Generative AI and Process Systems Engineering: The Next Frontier
<|reference_start|>Generative AI and Process Systems Engineering: The Next Frontier: 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.<|reference_end|>
@article{decardi-nelson2024generative,
    title={Generative AI and Process Systems Engineering: The Next Frontier},
    author={Benjamin Decardi-Nelson, Abdulelah S. Alshehri, Akshay Ajagekar,
  Fengqi You},
    journal={Computers & Chemical Engineering, Volume 187, August 2024, 108723},
    year={2024},
    doi={10.1016/j.compchemeng.2024.108723},
    archivePrefix={arXiv},
    eprint={2402.10977},
    primaryClass={cs.LG cs.AI cs.SY eess.SY math.OC}
}