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

Researchers have developed a rule-based automated system to detect and correct errors in Piping and Instrumentation Diagrams (P&IDs), critical documents in chemical engineering. The method converts P&IDs into graph representations and applies 33 engineered rules to identify and fix mistakes, significantly reducing manual review workload for engineering projects involving hundreds or thousands of diagram pages.

Chemical engineering projects rely heavily on P&IDs as foundational reference documents that guide process design, construction, and operation. Currently, engineers manually inspect these diagrams page-by-page to identify errors—a labor-intensive process that becomes increasingly impractical as project complexity grows. This research introduces an automated solution that transforms P&IDs into machine-readable graph structures, enabling systematic error detection against a library of domain-specific rules derived from chemical engineering best practices.

The approach leverages the DEXPI standard, an established format for P&ID representation, and the authors' pyDEXPI Python package to automate the conversion process. By encoding 33 rules based on engineering knowledge and heuristics, the system can identify common mistakes in real-time without human intervention. The methodology represents a meaningful step toward digitalization in engineering workflows, where manual QA processes have historically consumed substantial project resources.

The broader implications extend beyond error correction. Automating P&ID validation creates efficiency gains that allow engineering teams to focus on complex design decisions rather than routine quality assurance. This democratizes access to standardized quality checks across organizations of varying sizes, potentially reducing project timelines and costs. For organizations managing extensive P&ID portfolios, the system could unlock significant operational value by systematizing previously ad-hoc review processes.

Future development might focus on expanding the rule set, improving rule coverage for edge cases, and integrating machine learning to identify novel error patterns. Organizations adopting such systems early could establish competitive advantages through faster project delivery and higher diagram quality standards across their engineering practices.

• →Rule-based graph analysis automates error detection in P&IDs, reducing manual review workload for large engineering projects.
• →The system uses 33 engineered rules derived from chemical engineering knowledge to identify and correct common mistakes automatically.
• →DEXPI-standard P&IDs can be converted to machine-readable graphs enabling systematic validation and correction workflows.
• →Automating P&ID QA allows engineers to prioritize complex design decisions over routine quality assurance tasks.
• →The approach scales effectively to handle hundreds or thousands of diagram pages that would be impractical to review manually.