用大模型辅助化工厂故障恢复，确保安全操作。
A Tutorial on Autonomous Fault-Tolerant Control Using Knowledge-Grounded LLM Agents
- 将大模型当作受约束的决策规划器，结合工厂知识提出修复方案。
- 每条建议经外部验证器（符号或仿真）检查后才执行，保证可行性。
- 提供可运行代码环境，适合工业自动化与AI安全研究者使用。
过程工厂中的故障恢复仍严重依赖操作员，尤其当故障超出预设监控逻辑时。操作员需解读报警、规程、P&IDs、联锁和过程趋势，决定如何将系统移至安全运行状态而不触发停机。本文探讨大型语言模型（LLM）代理在支持此类恢复决策中的作用。所提框架将LLM视为受限的监督规划器，利用特定工厂知识生成恢复动作，并在执行前由外部验证器（符号或基于仿真）审核每项提议。论文定义了三个应用该框架的设计维度：适用于LLM代理的恢复模式、区分可接受与不可接受提议的验证策略，以及由延迟、知识工程、安全集成和模型生命周期管理带来的部署约束。为使框架即刻可用，本文提供了两个公开可执行的Python环境，均重构了已有的案例研究——模块化混合单元和连续搅拌釜反应器，并扩展了可配置故障及自定义恢复与验证方法的接口。
原文摘要 · Abstract (English)
Fault recovery in process plants still relies heavily on plant operators, especially when faults fall outside predefined supervisory logic. Operators interpret alarms, procedures, P\&IDs, interlocks, and process trends, then decide how to move the plant to a safe operating mode without triggering a shutdown. This paper examines how Large Language Model (LLM) agents can support such recovery decisions. The proposed framework treats the LLM as a constrained supervisory planner. It uses plant-specific knowledge to propose recovery actions, and every proposal is checked by an external validator (symbolic or simulation-based) before actuation. The paper develops three design dimensions for applying the framework: the recovery patterns for which LLM agents are useful, the validation strategies that separate admissible from inadmissible proposals, and the deployment constraints imposed by latency, knowledge engineering, safety integration, and model lifecycle management. To make the framework directly usable, two openly available executable Python environments are provided. Both re-implement established case studies, a modular mixing module and a continuous stirred-tank reactor, extended with configurable faults and defined interfaces for custom recovery and validation methods.
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