用大模型+数字孪生自动处理工厂故障，减少对人工经验依赖。
Leveraging LLM Agents and Digital Twins for Fault Handling in Process Plants
- 大模型代理实时分析系统状态并生成控制指令。
- 在管道堵塞场景下仅需少量重提示即可生成有效修复方案。
- 适合需要高自主性故障处理的工业自动化领域。
自动化与人工智能的发展持续提升过程工厂在各类运行场景中的自主性。然而，故障处理等任务仍具挑战性，高度依赖人类经验。为此，我们提出一种将大语言模型（LLM）代理与数字孪生环境相结合的方法框架。LLM代理持续解析系统状态，并发起控制动作，包括对意外故障的响应，目标是使系统恢复至正常运行。在此框架中，数字孪生既作为针对特定工厂的工程知识结构化存储库以供代理提示，也作为仿真平台，用于系统验证和确认生成的纠正控制动作。在某混合模块过程工厂的评估中，该框架不仅实现了对混合模块的自主控制，还能在仅有少数重提示的情况下生成有效纠正措施以缓解管道堵塞。
原文摘要 · Abstract (English)
Advances in Automation and Artificial Intelligence continue to enhance the autonomy of process plants in handling various operational scenarios. However, certain tasks, such as fault handling, remain challenging, as they rely heavily on human expertise. This highlights the need for systematic, knowledge-based methods. To address this gap, we propose a methodological framework that integrates Large Language Model (LLM) agents with a Digital Twin environment. The LLM agents continuously interpret system states and initiate control actions, including responses to unexpected faults, with the goal of returning the system to normal operation. In this context, the Digital Twin acts both as a structured repository of plant-specific engineering knowledge for agent prompting and as a simulation platform for the systematic validation and verification of the generated corrective control actions. The evaluation using a mixing module of a process plant demonstrates that the proposed framework is capable not only of autonomously controlling the mixing module, but also of generating effective corrective actions to mitigate a pipe clogging with only a few reprompts.
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