aiqbench analysis
This research introduces a rule-based graph method to automate error detection and correction in Piping and Instrumentation Diagrams (P&IDs), addressing the high manual workload in chemical process engineering. By encoding 33 domain-specific heuristics into rule graphs, the approach offers a scalable alternative to visual inspection for validating complex engineering documents. The study validates the technique's reliability on illustrative cases, demonstrating its potential to streamline revision workflows without relying on large-scale training data.
Rule-based autocorrection of Piping and Instrumentation Diagrams (P&IDs) on graphs
arXiv:2502.18493v2 Announce Type: replace-cross 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.
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.
Computational Engineering, Finance, and Science (cs.CE); Artificial Intelligence (cs.AI)
arXiv:2502.18493 [cs.CE]
(or arXiv:2502.18493v2 [cs.CE] for this version)
https://doi.org/10.48550/arXiv.2502.18493 Focus to learn more arXiv-issued DOI via DataCite
https://doi.org/10.69997/sct.150968 Focus to learn more DOI(s) linking to related resources
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