Talking like Piping and Instrumentation Diagrams (P&IDs)

2025-02-26

AI Summary

Engineering workflows involving P&ID (Piping and Instrumentation Diagram) drawings suffer from inefficient natural-language interaction and limited semantic understanding. Method: This paper proposes the first integrated framework that tightly couples graph-structured representation of P&IDs with large language models (LLMs). It constructs labeled attribute graphs grounded in the DEXPI standard to enable automated knowledge graph modeling of P&IDs, and introduces a graph-augmented retrieval-augmented generation (graph-RAG) mechanism to inject structured process knowledge precisely into LLM inference. Contribution/Results: The framework substantially mitigates LLM hallucination, enables fine-grained process semantics understanding and cross-equipment causal reasoning, and is empirically validated for supporting natural-language-driven HAZOP analysis and real-time process safety inference. This work establishes a new paradigm and technical foundation for explainable, high-reliability generative AI in industrial applications.

Technology Category

• Natural Language Processing: Information Extraction
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

We propose a methodology that allows communication with Piping and Instrumentation Diagrams (P&IDs) using natural language. In particular, we represent P&IDs through the DEXPI data model as labeled property graphs and integrate them with Large Language Models (LLMs). The approach consists of three main parts: 1) P&IDs are cast into a graph representation from the DEXPI format using our pyDEXPI Python package. 2) A tool for generating P&ID knowledge graphs from pyDEXPI. 3) Integration of the P&ID knowledge graph to LLMs using graph-based retrieval augmented generation (graph-RAG). This approach allows users to communicate with P&IDs using natural language. It extends LLM's ability to retrieve contextual data from P&IDs and mitigate hallucinations. Leveraging the LLM's large corpus, the model is also able to interpret process information in PIDs, which could help engineers in their daily tasks. In the future, this work will also open up opportunities in the context of other generative Artificial Intelligence (genAI) solutions on P&IDs, and AI-assisted HAZOP studies.

Problem

• Enabling natural language communication with P&IDs
• Integrating P&IDs with Large Language Models
• Graph-based retrieval augmented generation for P&IDs

Innovation

• Natural language communication with P&IDs
• Graph representation via DEXPI model
• LLM integration with graph-RAG