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

TL;DR

This paper presents a rule-based, graph-structured approach to automatically detect and correct errors in P&IDs, significantly reducing manual review workload in chemical engineering projects.

Contribution

It introduces a novel graph-based autocorrection method using 33 chemical engineering rules and a Python tool for automated P&ID error correction.

Findings

The method effectively detects and corrects errors in P&IDs.

The approach reduces manual review effort.

The case study confirms reliability and effectiveness.

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.