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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| Vydané v: | Proceedings (IEEE International Conference on Emerging Technologies and Factory Automation) s. 1 - 8 |
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| Jazyk: | English |
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IEEE
09.09.2025
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| ISSN: | 1946-0759 |
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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. |
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| 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 |
| Author_xml | – sequence: 1 givenname: Milapji Singh surname: Gill fullname: Gill, Milapji Singh email: milapji.gill@hsu-hh.de organization: Helmut Schmidt University,Institute of Automation Technology,Hamburg,Germany – sequence: 2 givenname: Javal surname: Vyas fullname: Vyas, Javal email: j.vyas24@imperial.ac.uk organization: Imperial College London,Autonomous Industrial Systems Lab,United Kingdom – sequence: 3 givenname: Artan surname: Markaj fullname: Markaj, Artan email: artan.markaj@hsu-hh.de organization: Helmut Schmidt University,Institute of Automation Technology,Hamburg,Germany – sequence: 4 givenname: Felix surname: Gehlhoff fullname: Gehlhoff, Felix email: felix.gehlhoff@hsu-hh.de organization: Helmut Schmidt University,Institute of Automation Technology,Hamburg,Germany – sequence: 5 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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