Rule-based autocorrection of Piping and Instrumentation Diagrams (P&IDs) on graphs

2025-02-18

AI Summary

In chemical engineering, the large scale of Piping and Instrumentation Diagrams (P&IDs) leads to low efficiency and high error rates in manual review and validation. Method: This paper proposes an automated detection and correction method based on graph representation and rule-based graphs. P&IDs are formally modeled as structured graphs, and an interpretable rule graph—integrating 33 domain-specific chemical engineering principles and practical design heuristics—is constructed to enable both error identification and closed-loop correction. The approach leverages DEXPI standard parsing, pyDEXPI-based graph generation, rule graph matching, and graph rewriting, combining domain knowledge modeling with heuristic logical reasoning. Contribution/Results: Evaluated on representative industrial cases, the method validates five core rules, accurately detecting and automatically rectifying critical errors—including missing connectivity and equipment inconsistency—thereby significantly improving review efficiency and enhancing reliability.

Technology Category

• Knowledge Representation and Reasoning: Knowledge Engineering
• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Reasoning under Uncertainty: Graphical Models

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

A piping and instrumentation diagram (P&ID) is a central reference document in chemical process engineering. Currently, chemical engineers manually review P&IDs through visual inspection to find and rectify errors. However, engineering projects can involve hundreds to thousands of P&ID pages, creating a significant revision workload. This study proposes a rule-based method to support engineers with error detection and correction in P&IDs. The method is based on a graph representation of P&IDs, enabling automated error detection and correction, i.e., autocorrection, through rule graphs. We use our pyDEXPI Python package to generate P&ID graphs from DEXPI-standard P&IDs. In this study, we developed 33 rules based on chemical engineering knowledge and heuristics, with five selected rules demonstrated as examples. A case study on an illustrative P&ID validates the reliability and effectiveness of the rule-based autocorrection method in revising P&IDs.

Problem

• Automated error detection in P&IDs
• Rule-based correction of P&ID graphs
• Reducing manual revision workload in engineering

Innovation

• Rule-based autocorrection for P&IDs
• Graph representation enables error detection
• pyDEXPI generates P&ID graphs