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

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, knowl...

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Published in:Proceedings (IEEE International Conference on Emerging Technologies and Factory Automation) pp. 1 - 8
Main Authors: Gill, Milapji Singh, Vyas, Javal, Markaj, Artan, Gehlhoff, Felix, Mercangoz, Mehmet
Format: Conference Proceeding
Language:English
Published: IEEE 09.09.2025
Subjects:
Artificial Intelligence
Autonomy
Digital twins
Fault Handling
Knowledge based systems
Knowledge engineering
Large language models
LLM Agents
Manufacturing automation
Process control
Process Plants
Systematics
ISSN:1946-0759
Online Access:Get full text
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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.
AbstractList 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.
Author Gill, Milapji Singh
Gehlhoff, Felix
Vyas, Javal
Markaj, Artan
Mercangoz, Mehmet
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  givenname: Javal
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  givenname: Mehmet
  surname: Mercangoz
  fullname: Mercangoz, Mehmet
  email: m.mercangoz@imperial.ac.uk
  organization: Imperial College London,Autonomous Industrial Systems Lab,United Kingdom
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Snippet Advances in Automation and Artificial Intelligence continue to enhance the autonomy of process plants in handling various operational scenarios. However,...
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SubjectTerms Artificial Intelligence
Autonomy
Digital twins
Fault Handling
Knowledge based systems
Knowledge engineering
Large language models
LLM Agents
Manufacturing automation
Process control
Process Plants
Systematics
Title Leveraging LLM Agents and Digital Twins for Fault Handling in Process Plants
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