[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-657698-105":59,"doc-detail-657698-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","using-artificial-intelligence-to-support-the-engineering-of-piping-and-instrumentation-diagrams-modeling-prototyping","Using artificial intelligence to support the engineering of piping and instrumentation diagrams - modeling & prototyping","","This talk presents a data-driven approach to engineering piping and instrumentation diagrams (P&IDs) by combining machine-readable formats with artificial intelligence. It motivates digitization through structured, machine-readable engineering data such as CAEX, AutomationML, and DEXPI, then models P&IDs as directed graphs with equipment and piping/signal-line edges. Graph Neural Networks are used to learn patterns for real-time consistency checks, aiming to avoid errors and reduce engineering costs via anomaly detection.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":30,"@type":76,"position":81},"https://docshare.wps.com/document/technology/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/using-artificial-intelligence-to-support-the-engineering-of-piping-and-instrumentation-diagrams-modeling-prototyping/657698/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/using-artificial-intelligence-to-support-the-engineering-of-piping-and-instrumentation-diagrams-modeling-prototyping/657698.png","ImageObject",300,407,{"name":92,"@type":93},"Jordan Avery","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-10-26",true,{"@type":102,"interactionType":103,"userInteractionCount":29},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"How does the approach make P&IDs usable for AI methods?","Question",{"text":112,"@type":113},"P&IDs are treated as machine-readable directed graphs, where nodes represent equipment/components and edges represent piping and signal lines with process stream direction.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What is the primary goal of using AI in P&ID engineering?",{"text":117,"@type":113},"The goal is to support engineering with real-time consistency checks, enabling anomaly detection to avoid errors and reduce costs.",{"name":119,"@type":110,"acceptedAnswer":120},"What type of neural network is used to learn from P&IDs?",{"text":121,"@type":113},"The method uses Graph Neural Networks (GNN), specifically leveraging neighborhood aggregation and message passing to generate node embeddings.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},657698,1793038661,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":29,"category_name":30,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":29,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},1099523882367,"https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","Using artificial intelligence to support the engineering of piping and instrumentation diagrams: modeling & prototyping  \nJ. Oeing 1 , W. Welscher2 , K. Brandt 1 , N. Kockmann 1  \n1. TU Dortmund, Laboratory of Equipment Design, Dortmund/DE  \n2. X-Visual Technologies GmbH, Berlin/DE  \nJ. Oeing, W. Welscher | PAAT 2022  \nMotivation  \n􀂃 Digitization requires well-structured data [1]  \n􀂃 Machine-readable engineering data  \n• CAEX[2]  \n• AutomationML [3]  \n• DEXPI [4]  \n•    \n􀂾 Combine machine-readable P&IDsand artificial intelligence (AI) to use stored knowledge  \nCAEX  \n[1] Wiedau et al. , Chem. Ing.Tech. , 2021 [3] [automationml.org](automationml.org) , 2022  \n[2] IEC 62424, 2016 [4] [dexpi.org](dexpi.org), 2022  \nJ. Oeing, W. Welscher | PAAT 2022  \nConsistency checks in P&IDs  \nHypothesis: AI is able to learn patterns in P&IDs and thus can detect anomalies in P&IDs.  \n􀂾 Added value through direct involvment in the drawing process of P&IDs  \nAim of this talk:  \n􀂃 Using Graph Neural Networks (GNN) to learn from P&IDs  \n􀂃 Support the Engineering of P&IDs (real time consistency checks)  \n• Avoid errors  \n• Reduce costs  \n􀂾 Understanding machine-readable P&IDs as a broad information model  \nJ. Oeing, W. Welscher | PAAT 2022  \nP&IDs as Graphs  \n􀂃 Abstraction of P&IDs as directed graphs  \n• Nodes 􀃆 equipment , components  \n• Edges 􀃆 piping, signal lines  \n• Direction 􀃆 direction of the process streams  \n􀂃 Advantages  \n􀂾 Mathematical topology discription  \n􀂾 Data availability for AI methods  \n􀂾 How do we get the data we need?  \nP&ID flowsheet  \nP&ID as machine readable graph  \nJ. Oeing, W. Welscher | PAAT 2022  \nData  \n􀂃 25 P&IDs (DEXPI)  \n• 8x distillation plants  \n• 6x absorption desorption plants  \n• 11x modular laboratory plants  \n􀂃 Directed graphs (GraphML)  \n• 2504 nodes  \n• 2842 edges (directed)  \n􀂃 Categories  \n• 16 categories  \n• Unbalanced distribution  \n| Category | Number | Category | Number | Category | Number |\n| --- | --- | --- | --- | --- | --- |\n| Valves | 725 | Heat exchanger | 82 | Vessels | 56 |\n| Ball valves | 74 | Separators | 30 | Pipe Tees | 616 |\n| Globe valves | 36 | Temperature sensors | 171 | Piping elements | 67 |\n| Check valves | 47 | Pressure sensors | 115 | Inlets / Outlets | 226 |\n| Safety valves | 68 | Flow sensors | 60 |  |  |\n| Pumps | 60 | Other sensors | 71 |  |  |\n\nJ. Oeing, W. Welscher | PAAT 2022  \nModelling – Graph Neural Networks (GNN)  \n􀂃 k-layer GNN‘s generate embeddings hu of node u based on their neighborhood structure [1]  \n􀂃 Message passing in GNNs [2]  \nℎ􀯨(􀯞+1) = 􀜷􀝌􀝀􀜽􀝐􀝁 􀯞  ℎ􀯨􀯞 , 􀜣􀝃􀝃􀝎􀝁􀝃􀜽􀝐􀝁 􀯞  ℎ􀯩􀯞 , ∀􀝒 ∈ 􀜰 􀝑    \no Aggregation: collects neighborhood information  \no  Update: introduces a non-linearity into the output of a neuron  \n[1] Leskovec , J. , Inductive Representation Learning on Large Graphs, 2017  \n[2] Hamilton, W. , Graph Representation Learning, 2020  \n[3] Oeing, J. et al. , Dig. Chem. Eng. , 2022  \nNeighborhood aggregation of a P&ID-GNN[3]  \nJ. Oeing, W. Welscher | PAAT 2022  \nChallenges – Graph Neural Networks (GNN)  \n2-layers:  \n􀂃 Aggregation of information two steps away  \n• No modelling of larger context  \nMultiple-layers:  \n􀂃 Aggregation gets to many information  \n• Features of different nodes very similar (“Oversmoothing“)  \n􀂾 Jumping Knowledge Networks (JKN)  \nJ. Oeing, W. Welscher | PAAT 2022  \nGraph neural Networks (GNN)– Training  \n􀂃 Activation functions  \n• ReLU, SeLU, tanh  \n􀂃 Train/Test split  \n• 0.7/0 .3  \n􀂃 Python  \n• Deep Graph Library (DGL) / pytorch  \n􀂃 Loss Calculation  \n• Optimizer: Adam (learning rate = 0.01)  \n• Loss: Cross-entropy  \n􀂃 Aggregation functions  \n• Sum  \n• Mean  \n• Graph Convolution Network (GCN)  \n• Max-Pooling  \n• Min-Pooling  \n• Multilayer-perceptron (MLP)  \n􀂃 Accuracy  \n􀜣 =  \n􀜶􀜲 + 􀜶􀜰  \n􀜶􀜲 + 􀜶􀜰 + 􀜨􀜲 + 􀜨􀜰  \nJ. Oeing, W. Welscher | PAAT 2022","cbCaivGxhMeT1bdu","https://ap.wps.com/l/cbCaivGxhMeT1bdu","pdf",2233416,17,"English","# Motivation\n# Consistency checks in P&IDs\n# P&IDs as Graphs\n## Data\n## Modelling – Graph Neural Networks (GNN)\n# Challenges – Graph Neural Networks (GNN)\n# Graph Neural Networks (GNN) – Training","[{\"question\":\"How does the approach make P\\u0026IDs usable for AI methods?\",\"answer\":\"P\\u0026IDs are treated as machine-readable directed graphs, where nodes represent equipment/components and edges represent piping and signal lines with process stream direction.\"},{\"question\":\"What is the primary goal of using AI in P\\u0026ID engineering?\",\"answer\":\"The goal is to support engineering with real-time consistency checks, enabling anomaly detection to avoid errors and reduce costs.\"},{\"question\":\"What type of neural network is used to learn from P\\u0026IDs?\",\"answer\":\"The method uses Graph Neural Networks (GNN), specifically leveraging neighborhood aggregation and message passing to generate node embeddings.\"}]","Using artificial intelligence to support the engineering of piping and instrumentation diagrams - modeling & prototyping | PDF",1792986424,43]