A Tutorial on Autonomous Fault-Tolerant Control Using Knowledge-Grounded LLM Agents

2026-06-30

AI Summary

This work addresses the persistent reliance on manual intervention for recovering from faults in process plants that fall outside predefined monitoring logic. To enhance automation and safety, the authors propose a knowledge-guided large language model (LLM) agent framework that functions as a constrained supervisory planner. By integrating domain-specific plant knowledge, the framework generates safe recovery actions and ensures execution reliability through symbolic or simulation-based verification mechanisms. The study innovatively defines three core design dimensions for LLM agents in this context: fault recovery patterns, verification strategies, and deployment constraints. Additionally, it provides two open-source Python environments to facilitate reproduction of canonical cases and support user-defined extensions, thereby significantly advancing the automation and safety of fault recovery in industrial settings.

Abstract

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.

Problem

• Fault recovery
• Process plants
• Operator dependence
• Supervisory logic
• Safe operating mode

Innovation

• Knowledge-Grounded LLM Agents
• Autonomous Fault-Tolerant Control
• External Validation
• Recovery Patterns
• Executable Process Environments