The startling success of ChatGPT and other large language models (LLMs) using
transformer-based generative neural network architecture in applications such
as natural language processing and image synthesis has many researchers excited
about potential opportunities in process systems engineering (PSE). The almost
human-like performance of LLMs in these areas is indeed very impressive,
surprising, and a major breakthrough. Their capabilities are very useful in
certain tasks, such as writing first drafts of documents, code writing
assistance, text summarization, etc. However, their success is limited in
highly scientific domains as they cannot yet reason, plan, or explain due to
their lack of in-depth domain knowledge. This is a problem in domains such as
chemical engineering as they are governed by fundamental laws of physics and
chemistry (and biology), constitutive relations, and highly technical knowledge
about materials, processes, and systems. Although purely data-driven machine
learning has its immediate uses, the long-term success of AI in scientific and
engineering domains would depend on developing hybrid AI systems that use first
principles and technical knowledge effectively. We call these hybrid AI systems
Large Knowledge Models (LKMs), as they will not be limited to only NLP-based
techniques or NLP-like applications. In this paper, we discuss the challenges
and opportunities in developing such systems in chemical engineering.

通过使用基于 transformer 的生成神经网络架构的 ChatGPT 和其他大型语言模型（LLM）在自然语言处理和图像合成等领域的惊人成功，许多研究人员对过程系统工程（PSE）中的潜在机会感到兴奋。然而，尽管它们在某些任务中非常有用，如撰写文件的初稿、代码编写辅助、文本摘要等，但它们在高度科学领域的成功受限于缺乏深度领域知识而无法推理、规划或解释。在这篇论文中，我们讨论了在化学工程领域开发这种系统面临的挑战和机遇。

ChatGPT：从大型语言模型向大型知识模型发展

Quo Vadis ChatGPT? From Large Language Models to Large Knowledge Models

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.

本文探讨了新兴生成式人工智能（GenAI）模型，如大型语言模型（LLM），如何增强过程系统工程（PSE）中的解决方法学。研究发现，这些尖端的 GenAI 模型，特别是基础模型（FM），在广泛的任务范围内，如回答查询、图像生成和复杂决策等方面提供了多功能适应性。本文深入介绍了经典和新兴的 GenAI 模型，包括 FM，并探讨了它们在 PSE 的关键领域内的应用，如合成和设计、优化和整合以及过程监测和控制。此外，本文还讨论了在充分利用 GenAI 在 PSE 中的潜力方面所面临的潜在挑战，包括多尺度建模、数据要求、评估指标和基准、以及信任与安全性，从而加深对如何将 GenAI 有效集成到系统分析、设计、优化、运营、监控和控制中的探讨。该研究为未来关于在 PSE 中应用新兴 GenAI 的研究提供了指导。