Leveraging LLM Agents and Digital Twins for Fault Handling in Process Plants

2025-05-04

AI Summary

Process industry fault response heavily relies on manual expertise, lacking systematic knowledge support. Method: This paper proposes an autonomous fault-response framework integrating large language model (LLM) agents with digital twin technology in a deeply coupled manner. The framework enables real-time system state perception for dynamic system analysis, leverages LLM agents for knowledge-guided fault identification and control strategy generation, and utilizes the digital twin as both an engineering knowledge repository and a simulation-based action validator—enabling few-shot adaptive decision-making. Contribution/Results: It pioneers the first closed-loop, control-level integration of LLM agents and digital twins. Experimental evaluation on a hybrid-unit blockage-clearing task demonstrates that effective control strategies can be generated with only a few prompts, enabling fully automated fault recovery. The approach significantly improves response efficiency and generalization capability across diverse operational scenarios.

Technology Category

• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Intelligent Robots: State Estimation

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

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.

Problem

• Autonomous fault handling in process plants using LLM agents
• Integration of Digital Twins for knowledge-based fault mitigation
• Validation of corrective actions via simulation in Digital Twin

Innovation

• Integrates LLM agents with Digital Twin environment
• Uses Digital Twin for knowledge repository and simulation
• Autonomously controls and mitigates faults efficiently