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

Milapji Singh Gill, Javal Vyas, Artan Markaj, Felix Gehlhoff, Mehmet Mercangöz

TL;DR

The paper addresses autonomous fault handling in process plants by integrating LLM agents with a Digital Twin to interpret system states and generate corrective actions. It introduces a modular, agent-based framework with Monitoring, Action, Validation, and Reprompting components that operate in a closed loop and validate actions in a simulation before applying them to the plant. Using a four-tank mixing module as a case study, the authors demonstrate that LLM-driven actions can mitigate faults like clogging with few reprompts, and they analyze how different plant information representations in prompts affect action quality and cost. The work highlights the potential for safer, scalable fault management in industrial settings and outlines concrete future directions, including richer behavioral models and retrieval-augmented reasoning to further enhance reliability and speed.

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.

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

TL;DR

The paper addresses autonomous fault handling in process plants by integrating LLM agents with a Digital Twin to interpret system states and generate corrective actions. It introduces a modular, agent-based framework with Monitoring, Action, Validation, and Reprompting components that operate in a closed loop and validate actions in a simulation before applying them to the plant. Using a four-tank mixing module as a case study, the authors demonstrate that LLM-driven actions can mitigate faults like clogging with few reprompts, and they analyze how different plant information representations in prompts affect action quality and cost. The work highlights the potential for safer, scalable fault management in industrial settings and outlines concrete future directions, including richer behavioral models and retrieval-augmented reasoning to further enhance reliability and speed.

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.
Paper Structure (15 sections, 3 figures, 2 tables, 1 algorithm)

This paper contains 15 sections, 3 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Background
  3. Fault Handling in Process Plants
  4. Large Language Models
  5. Related Work
  6. Requirements
  7. Methodological Framework for Autonomous Fault handling in Process Plants
  8. Framework
  9. Prompt Engineering in LLM Agents using Digital Twin Information
  10. Evaluation
  11. Experimental Set Up and Implementation
  12. Evaluation Metrics
  13. Results
  14. Discussion
  15. Summary and Outlook

Figures (3)

  • Figure 1: Agent-based methodological framework for autonomous fault handling.
  • Figure 2: Prompt structure with exemplary natural text information provided to the Action Agent and Reprompting Action.
  • Figure 3: Mixing module with all relevant actuators