Can LLMs handle process plant faults using digital twins?
Leveraging LLM Agents and Digital Twins for Fault Handling in Process Plants
This paper proposes a framework using LLM agents and a digital twin to automate fault handling in process plants. The agents monitor, propose actions, validate in a simulated environment, and reprompt for better solutions, mimicking human operators. Key LLM aspects include: adaptive reasoning for unforeseen faults, closed-loop validation for safety, integration of domain knowledge via prompt engineering with system structure, function, and behavior descriptions, and transparent decision-making using chain-of-thought prompting. Experiments demonstrate its effectiveness in handling a simulated clogging fault using different knowledge representations (text, model code, and engineering diagrams), with text-based prompts performing best.