mlsanityData to 14 Oct 2026

Generative AI and Process Systems Engineering: The Next Frontier

arXiv 2402.10977 ↗First posted 15 Feb 2024cs.LG cs.AI cs.SYcited by 9

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Abstract (arXiv)

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.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 74 of its 282 references that are in the map. First posted on arXiv 15 Feb 2024.

Builds on

74 of 282 references.

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50 most cited of 74 All 74 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 4; 2025: 4; 2026: 1.

9 papers.

  1. 2025Multi-agent systems for chemical engineering: A review and perspectivecited by 9
  2. 2024Quo Vadis ChatGPT? From Large Language Models to Large Knowledge Modelscited by 3
  3. 2025Federated Learning from Molecules to Processes: A Perspectivecited by 3
  4. 2024Towards Foundation Model for Chemical Reactor Modeling: Meta-Learning with Physics-Informed Adaptationcited by 1
  5. 2024The Systems Engineering Approach in Times of Large Language Modelscited by 1
  6. 2025CAPRAG: A Large Language Model Solution for Customer Service and Automatic Reporting using Vector and Graph Retrieval-Augmented Generationcited by 1
  7. 2024The Evolution and Future Perspectives of Artificial Intelligence Generated Contentcited by 0
  8. 2025Reinforcement Learning-Driven Plant-Wide Refinery Planning Using Model Decompositioncited by 0
  9. 2026Atoms to Processes: The Role of Artificial Intelligence and Machine Learning in Chemical Engineeringcited by 0