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...
Uloženo v:
| Vydáno v: | Proceedings (IEEE International Conference on Emerging Technologies and Factory Automation) s. 1 - 8 |
|---|---|
| Hlavní autoři: | , , , , |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
| Vydáno: |
IEEE
09.09.2025
|
| Témata: | |
| ISSN: | 1946-0759 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| 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 |
| 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 |
| BookMark | eNo1j1FLwzAUhaMoOGf_gWD-QGfS5La5j2OuTqi4h76PtLktkZpJUxX_vR3q04FzznfgXLOLcAzE2J0UKykF3m_rcp0DSLPKRAazlwkALM5YggUapSSAMsqcs4VEnaeiALxiSYyvQoiZz1HhglUVfdJoex96XlXPfN1TmCK3wfEH3_vJDrz-8iHy7jjy0n4ME9_N4XDq-8D347GlGPl-sDN2wy47O0RK_nTJ6nJbb3Zp9fL4tFlXqUc1pdq1ymTagdAELWDTUkeCEA3JzoDUTetIm1y6vMgdCnLUFFqggUJ2mW3Vkt3-znoiOryP_s2O34f__-oHfFBQ4w |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IL CBEJK RIE RIL |
| DOI | 10.1109/ETFA65518.2025.11205597 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Xplore POP ALL IEEE Xplore All Conference Proceedings IEEE Xplore IEEE Proceedings Order Plans (POP All) 1998-Present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE/IET Electronic Library url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| EISBN | 9798331553838 |
| EISSN | 1946-0759 |
| EndPage | 8 |
| ExternalDocumentID | 11205597 |
| Genre | orig-research |
| GroupedDBID | 6IE 6IF 6IK 6IL 6IN AAJGR AAWTH ABLEC ADZIZ ALMA_UNASSIGNED_HOLDINGS BEFXN BFFAM BGNUA BKEBE BPEOZ CBEJK CHZPO IEGSK IPLJI OCL RIE RIL RNS |
| ID | FETCH-LOGICAL-i93t-4dc3824d504e5c59bcefe0e998e1f8514bcde4861d676d90edeb74098571f2ac3 |
| IEDL.DBID | RIE |
| IngestDate | Wed Oct 29 06:13:06 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | true |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-i93t-4dc3824d504e5c59bcefe0e998e1f8514bcde4861d676d90edeb74098571f2ac3 |
| PageCount | 8 |
| ParticipantIDs | ieee_primary_11205597 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-Sept.-9 |
| PublicationDateYYYYMMDD | 2025-09-09 |
| PublicationDate_xml | – month: 09 year: 2025 text: 2025-Sept.-9 day: 09 |
| PublicationDecade | 2020 |
| PublicationTitle | Proceedings (IEEE International Conference on Emerging Technologies and Factory Automation) |
| PublicationTitleAbbrev | ETFA |
| PublicationYear | 2025 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| SSID | ssj0001096939 |
| Score | 2.3161862 |
| Snippet | Advances in Automation and Artificial Intelligence continue to enhance the autonomy of process plants in handling various operational scenarios. However,... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 1 |
| 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 |
| URI | https://ieeexplore.ieee.org/document/11205597 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV09T8MwFLQoYmACRBHf8sCaNokdf4wVNOoQqg4ZulWO_YwioRQ1Kfx9bDelYmBgs61Ysp4j-Z797g6hJ60tz1Kv3GpTFtGEs6iCikdKVFZ7SXECwbWk4PO5WC7loierBy4MAITiMxj5ZnjLN2u99VdlY4cNYo-AB2jAOd-RtQ4XKg6MSyL7Gi7XG0_LfMK84phLA9NstJ_9y0clHCP52T8XcI6GB0IeXvwcNRfoCJpLVBTg_sPgMoSL4hVPPEuqxaox-KV-82YguPyqmxY7XIpztX3v8MxrKvjv6wb3FAHsbYu6dojKfFo-z6LeHCGqJekiajQRKTVZTCHTmaw0WIjBJU-QWIeiaKUNUMESwzgzMgbjdsDlciLjiU2VJlfouFk3cI2wkZQQK6QW1lDFqFQZlxVR1GEHiBW5QUMfidXHTv5itQ_C7R_jd-jUxzsUYsl7dNxttvCATvRnV7ebx7Bp31xHl2s |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3PT8MgGCU6TfSkxhl_y8Frt7ZAW46LrpmxW3boYbeFwodpYjqztvrvC6xz8eDBG5CSkI8mvA--9x5Cj1LqmIVWuVWHkUeDOPIKKGJPJIWWVlKcgHMtyeLZLFks-LwjqzsuDAC44jMY2KZ7y1cr2dqrsqHBBr5FwPvogFEaBhu61u5KxcBxTnhXxWV6w3GejiKrOWYSwZANtvN_Oam4gyQ9-ecSTlF_R8nD85_D5gztQXWOsgzMn-h8hnCWTfHI8qRqLCqFn8s3aweC86-yqrFBpjgV7XuDJ1ZVwX5fVrgjCWBrXNTUfZSn4_xp4nX2CF7JSeNRJUkSUsV8CkwyXkjQ4INJnyDQBkfRQiqgSRSoKI4U90GZPTDZXMLiQIdCkgvUq1YVXCKsOCVEJ1wmWlERUS5YzAsiqEEP4Atyhfo2EsuPjQDGchuE6z_GH9DRJJ9my-xl9nqDjm3sXVkWv0W9Zt3CHTqUn01Zr-_dBn4Dbnaasg |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=Proceedings+%28IEEE+International+Conference+on+Emerging+Technologies+and+Factory+Automation%29&rft.atitle=Leveraging+LLM+Agents+and+Digital+Twins+for+Fault+Handling+in+Process+Plants&rft.au=Gill%2C+Milapji+Singh&rft.au=Vyas%2C+Javal&rft.au=Markaj%2C+Artan&rft.au=Gehlhoff%2C+Felix&rft.date=2025-09-09&rft.pub=IEEE&rft.eissn=1946-0759&rft.spage=1&rft.epage=8&rft_id=info:doi/10.1109%2FETFA65518.2025.11205597&rft.externalDocID=11205597 |