Abstract
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.
Abstract (translated)
本文探讨了新兴的生成人工智能（GenAI）模型，如大型语言模型（LLMs），如何增强过程系统工程（PSE）中的解决方案方法论。这些尖端的GenAI模型，特别是基础模型（FMs），预先训练于广泛的数据集，具有广泛的适应性，包括响应查询、图像生成和复杂决策。鉴于PSE的发展与计算和系统技术进步的密切相关性，探索GenAI与PSE之间的协同作用至关重要。我们从一个简要概述经典和新兴GenAI模型开始，包括FMs，然后深入探讨它们在PSE关键领域中的应用：合成与设计、优化与集成以及过程监控与控制。在每个领域，我们探讨了GenAI模型如何潜在地提高PSE方法论，并为每个领域提供见解和前景。此外，本文还识别并讨论了在充分利用GenAI within PSE过程中可能面临的一些挑战，包括多尺度建模、数据要求、评估指标和基准以及信任和安全，从而加深了对有效GenAI集成到系统分析、设计、优化、操作和监控的讨论。本文为未来研究关于新兴GenAI在PSE应用提供了指南。
URL
https://arxiv.org/abs/2402.10977
