工作流程
透视图(图形)
基础(证据)
管理科学
计算机科学
系统工程
工程类
数据科学
科学知识社会学
工程伦理学
知识管理
化学过程
风险分析(工程)
信息系统
作者
Sophia Rupprecht,Qinghe Gao,Tanuj Karia,Artur M. Schweidtmann
标识
DOI:10.1016/j.coche.2025.101209
摘要
Large language model (LLM)-based multi-agent systems (MASs) are a recent but rapidly evolving technology with the potential to transform chemical engineering by decomposing complex workflows into teams of collaborative agents with specialized knowledge and tools. This review surveys the state-of-the-art of MASs within chemical engineering. While early studies demonstrate promising results, scientific challenges remain, including the design of tailored architectures, integration of heterogeneous data modalities, development of foundation models with domain-specific modalities, and strategies for ensuring transparency, safety, and environmental impact. As a young but fast-moving field, MASs offer exciting opportunities to rethink chemical engineering workflows.
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