Artificial intelligence for piping and instrumentation diagrams: a review and perspective

Abstract

Driven by the urgent demands for process efficiency, operational safety, and industrial intelligence, piping and instrumentation diagrams are evolving from static design documents into dynamic knowledge-intensive carriers. However, the intelligent digitization of piping and instrumentation diagrams faces systemic challenges due to their highly unstructured data format, dense symbolic information, and heterogeneous drafting styles, which hinder automated information extraction and result in a fragmented data ecosystem. Recent advancements in artificial intelligence, particularly in pattern recognition and nonlinear feature modeling, have enabled the automated extraction of core elements such as symbols, annotations, and connecting lines from piping and instrumentation diagrams, the reconstruction of process topologies, and the establishment of semantically enriched knowledge models. These developments provide a foundational framework for high-level applications including automated compliance checking, intelligent piping and instrumentation diagram generation, hazard and operability analysis, and digital twin development. This paper provides a systematic review of the state-of-the-art artificial intelligence-driven methodologies across the ‘perception-cognition-application’ pipeline, analyzes current technical bottlenecks, and outlines future directions.

Graphical abstract

Keywords

artificial intelligence / piping and instrumentation diagrams / image processing / information extraction

References

[1] Elyan E, Garcia C M, Jayne C. Symbols classification in engineering drawings. In: Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN). Rio de Janeiro: IEEE, 2018: 1–8

[2] Son H , Kim C , Kim C . 3D reconstruction of as-built industrial instrumentation models from laser-scan data and a 3D CAD database based on prior knowledge. Automation in Construction, 2015, 49: 193–200

[3] Cui L , Zhao J , Zhang R . The integration of HAZOP expert system and piping and instrumentation diagrams. Process Safety and Environmental Protection, 2010, 88(5): 327–334

[4] Rossing N L , Lind M , Jensen N , Jørgensen S B . A functional HAZOP methodology. Computers & Chemical Engineering, 2010, 34(2): 244–253

[5] Arroyo E , Hoernicke M , Rodríguez P , Fay A . Automatic derivation of qualitative plant simulation models from legacy piping and instrumentation diagrams. Computers & Chemical Engineering, 2016, 92: 112–132

[6] Stinner F, Wiecek M, Baranski M, Kümpel A, Müller D. Automatic digital twin generation of building energy systems using piping and instrumentation diagrams. In: Proceedings of the 34th International Conference on Efficiency, Cost, Optimization, Simulation, and Environmental Impact of Energy Systems (ECOS 2021). Tokyo: JapanECOS 2021 Program Organizers, 2022: 1854–1865

[7] Alpúsig S, Pruna E, Escobar I, Guano A. Extended Reality. Cham: Springer, 2024: 101–113

[8] Yang T , Yi X , Lu S , Johansson K H , Chai T . Intelligent manufacturing for the process industry driven by industrial artificial intelligence. Engineering, 2021, 7(9): 1224–1230

[9] Ren L , Wang H , Wang Y , Huang K , Wang L , Li B . Foundation models for the process industry: challenges and opportunities. Engineering, 2025, 52: 53–59

[10] Hirtreiter E , Schulze Balhorn L , Schweidtmann A M . Toward automatic generation of control structures for process flow diagrams with large language models. AIChE Journal, 2024, 70: e18259

[11] Uzuner H , Schembecker G . Wissensbasierte erstellung von R&I-fließbildern. Chemie Ingenieur Technik, 2012, 84(5): 747–761

[12] LeCun Y , Bengio Y , Hinton G . Deep learning. Nature, 2015, 521(7553): 436–444

[13] Yun D-Y , Seo S-K , Zahid U , Lee C-J . Deep neural network for automatic image recognition of engineering diagrams. Applied Sciences, 2020, 10(11): 4005

[14] Krizhevsky A , Sutskever I , Hinton G E . ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 2012, 25: 1097–1105

[15] Wolf T, Debut L, Sanh V, Chaumond J, Delangue C, Moi A, Cistac P, Rault T, Louf R, Funtowicz M, et al. Transformers: state-of-the-art natural language processing. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. Stroudsburg: ACL, 2020: 38–45

[16] Shao M , Basit A , Karri R , Shafique M . Survey of different large language model architectures: trends, benchmarks, and challenges. IEEE Access, 2024, 12: 188664–188706

[17] Peng J, Bu X, Sun M, Zhang Z, Tan T, Yan J. Large-scale object detection in the wild from imbalanced multi-labels. In: Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle: IEEE, 2020: 9706–9715

[18] Russakovsky O , Deng J , Su H , Krause J , Satheesh S , Ma S , Huang Z , Karpathy A , Khosla A , Bernstein M . et al. ImageNet large scale visual recognition challenge. International Journal of Computer Vision, 2015, 115(3): 211–252

[19] Rahul R , Paliwal S , Sharma M , Vig L . Automatic information extraction from piping and instrumentation diagrams. , 2019,

[20] Tian Z, Huang W, He T, He P, Qiao Y. Computer Vision-ECCV 2016. Cham: Springer, 2016: 56–72

[21] Zhou X, Yao C, Wen H, Wang Y, Zhou S, He W, Liang J. EAST: an efficient and accurate scene text detector. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu: IEEE, 2017: 2642–2651

[22] Baek Y , Lee B , Han D , Yun S , Lee H . Character region awareness for text detection. In: Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Long Beach: IEEE, 2019, 9357–9366

[23] Veličković P, Cucurull G, Casanova A, Romero A, Liò P, Bengio Y. Graph attention networks. In: Proceedings of the International Conference on Learning Representations, Vancouve: ICLR, 2018

[24] Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks. In: Proceedings of the International Conference on Learning Representations, Toulon: ICLR, 2017

[25] Schulze Balhorn L , Degens K , Schweidtmann A M . Graph-to-SFILES: control structure prediction from process topologies using generative artificial intelligence. Computers & Chemical Engineering, 2025, 199: 109121

[26] Wang Q , Mao Z , Wang B , Guo L . Knowledge graph embedding: a survey of approaches and applications. IEEE Transactions on Knowledge and Data Engineering, 2017, 29(12): 2724–2743

[27] Purohit S, Van N, Chin G. Semantic property graph for scalable knowledge graph analytics. In: Proceedings of the 2021 IEEE International Conference on Big Data (Big Data). Orlando: IEEE, 2021: 2672–2677

[28] Gupta M , Wei C , Czerniawski T , Eiris R . PIDQA—question answering on piping and instrumentation diagrams. Machine Learning and Knowledge Extraction, 2025, 7(2): 39

[29] Kang D-H , Choi S-W , Lee E-B , Kang S-O . Auto-routing systems (ARSs) with 3D piping for sustainable plant projects based on artificial intelligence (AI) and digitalization of 2D drawings and specifications. Sustainability, 2024, 16(7): 2770

[30] Srinivas S S, Gupta S, Runkana V. AutoChemSchematic AI: agentic physics-aware automation for chemical manufacturing scale-up. Neural Information Processing Systems 2025 Workshop on AI for Science, 2025

[31] Radford A, Kim J W, Hallacy C, Ramesh A, Goh G, Agarwal S, Sastry G, Askell A, Mishkin P, Clark J, et al. Learning transferable visual models from natural language supervision. International Conference on Machine Learning, 2021, 8748–8763

[32] Zhou K , Yang J , Loy C , Liu Z . Learning to prompt for vision-language models. International Journal of Computer Vision, 2022, 130(9): 2337–2348

[33] Ghosh S . Visual language model as a judge for object detection in industrial diagrams. , 2025,

[34] Gupta M , Wei C , Czerniawski T . Semi-supervised symbol detection for piping and instrumentation drawings. Automation in Construction, 2024, 159: 105260

[35] Kim B C , Kim H , Moon Y , Lee G , Mun D . End-to-end digitization of image format piping and instrumentation diagrams at an industrially applicable level. Journal of Computational Design and Engineering, 2022, 9(4): 1298–1326

[36] Elyan E , Jamieson L , Ali-Gombe A . Deep learning for symbols detection and classification in engineering drawings. Neural Networks, 2020, 129: 91–102

[37] Tan W C, Chen I-M, Tan H K. Automated identification of components in raster piping and instrumentation diagram with minimal pre-processing. In: Proceedings of 2016 IEEE International Conference on Automation Science and Engineering (CASE). Fort Worth: IEEE, 2016: 1301–1306

[38] Kim H , Lee W , Kim M , Moon Y , Lee T , Cho M , Mun D . Deep-learning-based recognition of symbols and texts at an industrially applicable level from images of high-density piping and instrumentation diagrams. Expert Systems with Applications, 2021, 183: 115337

[39] Lee J , Park S , Oh S , Ma B. . Can large language models automate the HAZOP process without human intervention?. Safety Science, 2026, 194: 107039

[40] Moreno-García C F , Elyan E , Jayne C . New trends on digitisation of complex engineering drawings. Neural Computing and Applications, 2019, 31(6): 1695–1712

[41] Jamieson L , Francisco Moreno-García C , Elyan E . A review of deep learning methods for digitisation of complex documents and engineering diagrams. Artificial Intelligence Review, 2024, 57(6): 136

[42] Liu S. Design and implementation of pipeline reconstruction system for petrochemical P&ID drawings. Dissertation for the Doctoral Degree. Jinan: University of Jinan, 2024

[43] Kang S-O , Lee E-B , Baek H-K . A digitization and conversion tool for imaged drawings to intelligent piping and instrumentation diagrams (P&ID). Energies, 2019, 12(13): 2593

[44] Toghraei M. Piping and Instrumentation Diagram Development. New York: John Wiley & Sons, 2019

[45] Goldstein D P , Balhorn L S , Alimin A A , Schweidtmann A M . pyDEXPI: a Python framework for piping and instrumentation diagrams (P&IDs) using the DEXPI information model. Systems and Control Transactions, 2025, 4: 1365–1370

[46] Mani S, Haddad M A, Constantini D, Douhard W, Li Q W, Poirier L. Automatic digitization of engineering diagrams using deep learning and graph search. In: Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Seattle: IEEE, 2020: 673–679

[47] Mesli-Kesraoui S, Kesraoui D, Oquendo F, Bignon A, Toguyeni A, Berruet P. Software Architecture. Cham: Springer, 2016: 210–226

[48] Cameron D B , Otten W , Temmen H , Hole M , Tolksdorf G . DEXPI process: standardizing interoperable information for process design and analysis. Computers & Chemical Engineering, 2024, 182: 108564

[49] Otsu N . A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 1979, 9(1): 62–66

[50] Sauvola J , Pietikäinen M . Adaptive document image binarization. Pattern Recognition, 2000, 33(2): 225–236

[51] Das A K , Chanda B . A fast algorithm for skew detection of document images using morphology. International Journal on Document Analysis and Recognition, 2001, 4(2): 109–114

[52] Datta R, De Sekhar Mandal P, Chanda B. Detection and identification of logic gates from document images using mathematical morphology. In: Proceedings of the 2015 Fifth National Conference on Computer Vision, Pattern Recognition, and Image Processing and Graphics (NCVPRIPG). Patna: IEEE, 2015: 1–4

[53] Fu L , Kara L B . From engineering diagrams to engineering models: visual recognition and applications. Computer-Aided Design, 2011, 43(3): 278–292

[54] Ghadekar P , Joshi S , Swain D , Acharya B , Pradhan M R , Patro P . Automatic digitization of engineering diagrams using intelligent algorithms. Journal of Computer Science, 2021, 17(9): 848–854

[55] Moon Y , Lee J , Mun D , Lim S . Deep learning-based method to recognize line objects and flow arrows from image-format piping and instrumentation diagrams for digitization. Applied Sciences, 2021, 11(21): 10054

[56] Jamieson L , Moreno-Garcia C F , Elyan E . Towards fully automated processing and analysis of construction diagrams: AI-powered symbol detection. International Journal on Document Analysis and Recognition, 2025, 28(1): 71–84

[57] Hain A, Gölzhäuser S, Réhault N, Brox T, Demant M. Machine Learning and Knowledge Discovery in Databases: Research Track and Applied Data Science Track. Berlin, Heidelberg: Springer, 2026: 403–421

[58] Bailey D, Norman A, Moretti G, North P. Electronic Schematic Recognition. Palmerston North: Massey University, 1995

[59] Singh S, Singh R. Comparison of various edge detection techniques. In: Proceedings of the 2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom). New Delhi: IEEE, 2015: 393–396

[60] Wang D , Haese-Coat V , Ronsin J . Shape decomposition and representation using a recursive morphological operation. Pattern Recognition, 1995, 28(11): 1783–1792

[61] Dalal N , Triggs B . Histograms of oriented gradients for human detection. In: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR). San Diego: IEEE, 2005, 1: 886–893

[62] Lowe D G . Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 2004, 60(2): 91–110

[63] Viola P, Jones M. Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Kauai: IEEE, 2001: I

[64] Suhailam P, Yerolla R, Besta C S. Enhancing efficiency in piping & instrumentation diagrams (P&IDs) through AI-driven digitization. In: Proceedings of the 2025 6th International Conference on Control, Communication, and Computing (ICCC). Thiruvanathapuram: IEEE, 2025: 1–6

[65] Tian D P . A Review on image feature extraction and representation techniques. International Journal of Multimedia & Ubiquitous Engineering, 2013, 8: 385–395

[66] Zhang H , Li D , Ji Y , Zhou H , Wu W , Liu K . Toward new retail: a benchmark dataset for smart unmanned vending machines. IEEE Transactions on Industrial Informatics, 2020, 16(12): 7722–7731

[67] Uijlings J R R , van de Sande K E A , Gevers T , Smeulders A W M . Selective search for object recognition. International Journal of Computer Vision, 2013, 104(2): 154–171

[68] Girshick R, Donahue J, Darrell T, Malik J. Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus: IEEE, 2014: 580–587

[69] Girshick R. Fast R-CNN. 2015 IEEE International Conference on Computer Vision (ICCV). Santiago: IEEE, 2015: 1440–1448

[70] Ren S, He K, Girshick R, Sun J. Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems. Montreal: NeurIPS, 2015, 91–99

[71] He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas: IEEE, 2016: 770–778

[72] Gao W , Zhao Y , Smidts C . Component detection in piping and instrumentation diagrams of nuclear power plants based on neural networks. Progress in Nuclear Energy, 2020, 128: 103491

[73] Theisen M F , Flores K N , Schulze Balhorn L , Schweidtmann A M . Digitization of chemical process flow diagrams using deep convolutional neural networks. Digital Chemical Engineering, 2023, 6: 100072

[74] Hu H , Zhang C , Liang Y . Detection of surface roughness of mechanical drawings with deep learning. Journal of Mechanical Science and Technology, 2021, 35(12): 5541–5549

[75] Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A. Going deeper with convolutions. In: Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston: IEEE, 2015: 1–9

[76] Gada M. Object detection for P&ID images using various deep learning techniques. In: Proceedings of the 2021 International Conference on Computer Communication and Informatics (ICCCI). Coimbatore: IEEE, 2021: 1–5

[77] Howard A, Sandler M, Chen B, Wang W, Chen L-C, Tan M, Chu G, Vasudevan V, Zhu Y, Pang R, et al. Searching for MobileNetV3. In: Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision (ICCV). Seoul: IEEE, 2019: 1314–1324

[78] Dai J, Li Y, He K, Sun J. R-FCN: object detection via region-based fully convolutional networks. In: Advances in Neural Information Processing Systems. Barcelona : NeurIPS, 2016, 379–387

[79] Redmon J, Divvala S, Girshick R, Farhadi A. You only look once: unified, real-time object detection. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas: IEEE, 2016: 779–788

[80] Redmon J, Farhadi A. YOLO9000: better, faster, stronger. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu: IEEE, 2017: 6517–6525

[81] Redmon J, Farhadi A. YOLOv3: an incremental improvement. 2018, arXiv: 1804.02767

[82] Bochkovskiy A, Wang C-Y, Liao H-Y M. YOLOv4: optimal speed and accuracy of object detection. 2020, arXiv: 2004.10934

[83] Li C, Li L, Jiang H, Weng K, Geng Y, Li L, Ke Z, Li Q, Cheng M, Nie W, et al. YOLOv6: a single-stage object detection framework for industrial applications. 2022, arXiv: 2209.02976

[84] Wang C-Y, Bochkovskiy A, Liao H-Y M. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: Proceedings of the 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Vancouver: IEEE, 2023: 7464–7475

[85] Ali-Gombe A , Elyan E . MFC-GAN: class-imbalanced dataset classification using multiple fake class generative adversarial network. Neurocomputing, 2019, 361: 212–221

[86] Bertinetto L, Valmadre J, Henriques J F, Vedaldi A, Torr P H S. Computer Vision-ECCV 2016 Workshops. Cham: Springer, 2016: 850–865

[87] Hantach R, Lechuga G, Calvez P. Document Analysis and Recognition-ICDAR 2021 Workshops. Cham: Springer, 2021: 504–508

[88] Bhanbhro H, Hooi Y K, Hassan Z, Sohu N. Modern deep learning approaches for symbol detection in complex engineering drawings. In: Proceedings of the 2022 International Conference on Digital Transformation and Intelligence (ICDI). Kuching: IEEE, 2022: 121–126

[89] Haar C, Kim H, Koberg L. Flexible Automation and Intelligent Manufacturing: the Human-Data-Technology Nexus. Cham: Springer, 2023: 374–382

[90] Lin T-Y, Goyal P, Girshick R, He K, Dollár P. Focal loss for dense object detection. In: Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV). Venice: IEEE, 2017: 2999–3007

[91] Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, Berg A C. Computer Vision-ECCV 2016. Cham: Springer, 2016: 21–37

[92] Howard A G, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H. MobileNets: efficient convolutional neural networks for mobile vision applications. 2017, arXiv: 1704.04861

[93] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. In: Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston: IEEE, 2015: 3431–3440

[94] Paliwal S, Jain A, Sharma M, Vig L. Trends and Applications in Knowledge Discovery and Data Mining. Cham: Springer, 2021: 168–180

[95] Zhang F, Li M, Zhai G, Liu Y. MultiMedia Modeling. Cham: Springer, 2021: 136–147

[96] Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, et al. An image is worth 16 × 16 words: transformers for image recognition at scale. In: International Conference on Learning Representations.Vienna: ICLR, 2021

[97] Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A N, Kaiser L, Polosukhin I. Attention is all you need. In: Advances in Neural Information Processing Systems. Long Beach: NeurIPS, 2017, 5998–6008

[98] Li Z , Zhao J , Zhou H , Hu X . A novel symbol recognition framework for digitization of piping and instrumentation diagrams. Systems and Control Transactions, 2025, 4: 1800–1805

[99] Ye Q , Doermann D . Text detection and recognition in imagery: a survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(7): 1480–1500

[100] Long S , He X , Yao C . Scene text detection and recognition: the deep learning era. International Journal of Computer Vision, 2021, 129(1): 161–184

[101] Hochreiter S , Schmidhuber J . Long short-term memory. Neural Computation, 1997, 9(8): 1735–1780

[102] Yu E-S , Cha J-M , Lee T , Kim J , Mun D . Features recognition from piping and instrumentation diagrams in image format using a deep learning network. Energies, 2019, 12(23): 4425

[103] Jamieson L, Moreno-Garcia C F, Elyan E. Deep learning for text detection and recognition in complex engineering diagrams. In: Proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN). Glasgow: IEEE, 2020: 1–7

[104] Shi B, Bai X, Belongie S. Detecting oriented text in natural images by linking segments. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu: IEEE, 2017: 3482–3490

[105] Byun J , Kang B , Mun D , Lee G , Kim H . Optimizing image format piping and instrumentation diagram recognition: integrating symbol and text recognition with a single backbone architecture. Journal of Computational Design and Engineering, 2025, 12(6): 55–72

[106] Sun P, Zhang R, Jiang Y, Kong T, Xu C, Zhan W, Tomizuka M, Li L, Yuan Z, Wang C, et al. Sparse R-CNN: end-to-end object detection with learnable proposals. In: Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Nashville: IEEE, 2021: 14449–14458

[107] Smith R. An overview of the tesseract OCR engine. In: Ninth International Conference on Document Analysis and Recognition. Kyoto: ICDAR, 2007: 629–633

[108] Francois M, Eglin V, Biou M. Document Analysis Systems. Cham: Springer, 2022: 726–740

[109] Du Y, Li C, Guo R, Yin X, Liu W, Zhou J, Bai Y, Yu Z, Yang Y, Dang Q, et al. PP-OCR: a practical ultra lightweight OCR system. 2020, arXiv: 2009.09941

[110] Eid M , Ave G D . Automated interpretation of chemical engineering diagrams using computer vision. Systems and Control Transactions, 2025, 4: 1670–1675

[111] Han S-T , Moon Y , Kim J-B , Lee H , Mun D . Graph neural network-based method for classifying continuous lines in piping and instrumentation diagram. Advanced Engineering Informatics, 2025, 66: 103457

[112] Kim G , Kim B C . Classification of functional types of lines in P&IDs using a graph neural network. IEEE Access, 2023, 11: 73680–73687

[113] Han S-T , Moon Y , Lee H , Mun D . Rule-based continuous line classification using shape and positional relationships between objects in piping and instrumentation diagram. Expert Systems with Applications, 2024, 248: 123366

[114] Bresson X, Laurent T. Residual gated graph ConvNets. 2017, arXiv: 1711.07553

[115] Sinha A, Bayer J, Bukhari S S. Table localization and field value extraction in piping and instrumentation diagram images. In: Proceedings of the 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW). Sydney: IEEE, 2019: 26–31

[116] Azangoo M , Sorsamäki L , Sierla S A , Mätäsniemi T , Rantala M , Rainio K , Vyatkin V . A methodology for generating a digital twin for process industry: a case study of a fiber processing pilot plant. IEEE Access, 2022, 10: 58787–58810

[117] Azangoo M , Sierla S , Vyatkin V. . Sitting on a gold mine: the story of the process industry’s automatic formation of a digital twin.. , 2023,

[118] Shit S, Koner R, Wittmann B, Paetzold J, Ezhov I, Li H W, Pan J Z, Sharifzadeh S, Kaissis G, Tresp V, et al. Relationformer: a unified framework for image-to-graph generation. 2022, arXiv: 2203.10202

[119] Stürmer J M, Graumann M, Koch T. From engineering diagrams to graphs: digitizing P&IDs with transformers. In: Proceedings of the 2025 IEEE 12th International Conference on Data Science and Advanced Analytics (DSAA). Birmingham: IEEE, 2025: 1–11

[120] Carion N, Massa F, Synnaeve G, Usunier N, Kirillov A, Zagoruyko S. Computer Vision-ECCV 2020. Cham: Springer, 2020: 213–229

[121] Brown T B, Mann B, Ryder N, Subbiah M, Kaplan J, Dhariwal P, Neelakantan A, Shyam P, Sastry G, Askell A, et al. Language models are few-shot learners. In: Advances in Neural Information Processing Systems. Curran Associates, 2020, 1877–1901.

[122] Medhane S , Khalkar R , Dhumane A , Mestry R , Khan A , Agarwal S . Automated counting of piping and instrumentation diagram using artificial intelligence. Journal of Integrated Science and Technology, 2025, 13: 1147–1147

[123] Kim J-B , Moon Y , Han S-T , Mun D . Automated inspection of P&ID object recognition using deep learning. Scientific Reports, 2025, 15: 39031

[124] Gowaikar S, Iyengar S, Segal S, Kalyanaraman S. An agentic approach to automatic creation of P&ID diagrams from natural language descriptions. 2024, arXiv: 2412.12898

[125] Alimin A A , Goldstein D P , Balhorn L S , Schweidtmann A M . Talking like piping and instrumentation diagrams (P&IDs). Systems and Control Transactions, 2025, 4: 1676–1681

[126] Peng B , Zhu Y , Liu Y , Bo X , Shi H , Hong C , Zhang Y , Tang S . Graph retrieval-augmented generation: a survey. ACM Transactions on Information Systems, 2026, 44(2): 1–52

[127] Procko T T, Ochoa O. Graph retrieval-augmented generation for large language models: a survey. 2024 Conference on AI, Science, Engineering, and Technology (AIxSET). Laguna Hills: IEEE, 2024: 166–169

[128] Oeing J , Welscher W , Krink N , Jansen L , Henke F B , Kockmann N . Using artificial intelligence to support the drawing of piping and instrumentation diagrams using DEXPI standard. Digital Chemical Engineering, 2022, 4: 100038

[129] Gómez-Vega F S , Acuña O , Camargo A C , Jimenez J D , Galeano S M , Franco I E , Lozano L L , Vásquez J , Puertas E . Enhancing consistency in piping and instrumentation diagrams using DistilBERT and smart PID systems. Systems and Soft Computing, 2025, 7: 200373

[130] Sanh V, Debut L, Chaumond J, Wolf T. DistilBERT, a distilled version of BERT: smaller, faster, cheaper, and lighter. 2019, arXiv: 1910.01108

[131] Balhorn L S , Seijsener N , Dao K , Kim M , Goldstein D P , Driessen G H M , Schweidtmann A M . Rule-based autocorrection of piping and instrumentation diagrams (P&IDs) on graphs. Systems and Control Transactions, 2025, 4: 1656–1661

[132] Tolksdorf G , Cameron D B , Theißen M . DEXPI 2.0: synergistic integration of PFD and P&ID in a unified digital model. Chemie Ingenieur Technik, 2025, 97(11): 1065–1069

[133] Elhosary E, Moselhi O. Utilization of artificial intelligence in HAZOP studies and reports. Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC), 2025: 617–624

[134] Grootendorst M. BERTopic: neural topic modeling with a class-based TF-IDF procedure. 2022, arXiv: 2203.05794

[135] Mokhtarname R , Urbas L , Safavi A A , Salimi F , Zerafat M M , Harasi N . An artificial neural network approach to enrich HAZOP analysis of complex processes. Journal of Loss Prevention in the Process Industries, 2024, 91: 105382

[136] Vogel G , Schulze Balhorn L , Schweidtmann A M . Learning from flowsheets: a generative transformer model for autocompletion of flowsheets. Computers & Chemical Engineering, 2023, 171: 108162

[137] Koltun G, Kolter M, Vogel-Heuser B. Automated generation of modular PLC control software from P&ID diagrams in process industry. In: Proceedings of the 2018 IEEE International Systems Engineering Symposium (ISSE). Rome: IEEE, 2018: 1–8

[138] Eilermann M , Post C , Radatz H , Bramsiepe C , Schembecker G . A general approach to module-based plant design. Chemical Engineering Research and Design, 2018, 137: 125–140

[139] d’Anterroches L. Process flow sheet generation and design through a group contribution approach. Dissertation for the Doctoral Degree. Kgs Lyngby: Technical University of Denmark, 2006

[140] Vogel G , Hirtreiter E , Schulze Balhorn L , Schweidtmann A M . SFILES 2.0: an extended text-based flowsheet representation. Optimization and Engineering, 2023, 24(4): 2911–2933

[141] Raffel C , Shazeer N , Roberts A , Lee K , Narang S , Matena M , Zhou Y , Li W , Liu P J . Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research, 2020, 21(140): 1–67

[142] Kritzinger W , Karner M , Traar G , Henjes J , Sihn W . Digital twin in manufacturing: a categorical literature review and classification. IFAC-PapersOnLine, 2018, 51(11): 1016–1022

[143] Gill M S, Vyas J, Markaj A, Gehlhoff F, Mercangöz M. Leveraging LLM agents and digital twins for fault handling in process plants. In: Proceedings of the 2025 IEEE 30th International Conference on Emerging Technologies and Factory Automation (ETFA). Porto: IEEE, 2025: 1–8

[144] Kirchhübel D , Lind M , Ravn O . Combining operations documentation and data to diagnose procedure execution. Computers & Chemical Engineering, 2020, 140: 106940

[145] Hamidishad N , Barbosa R S , Allahyarzadeh-Bidgoli A , Yanagihara J I . Digital twin frameworks for oil and gas processing plants: a comprehensive literature review. Processes, 2025, 13(11): 3488

[146] Jonach T , Haddadi B , Jordan C , Harasek M . Dynamic simulation of a gas and oil separation plant with focus on the water output quality. Energies, 2023, 16(10): 4111

[147] Allahyarzadeh-Bidgoli A , Dezan D J , Salviano L O , de Oliveira S Jr , Yanagihara J I . FPSO fuel consumption and hydrocarbon liquids recovery optimization over the lifetime of a deep-water oil field. Energy, 2019, 181: 927–942

[148] Yu W , Patros P , Young B , Klinac E , Walmsley T G . Energy digital twin technology for industrial energy management: classification, challenges, and future. Renewable and Sustainable Energy Reviews, 2022, 161: 112407

[149] Wang X , Liu J , Welsh T N , Chan A . Development of AI-assisted, immersive virtual reality learning module to enhance operation and procedural accuracy for laboratory education. In: Proceedings of the 2025 ASEE Annual Conference & Exposition. Montreal: ASEE Conferences, 2025,

[150] Aiken W , Carden L , Bhabhrawala A , Branco P , Jourdan G V , Berg A . Strategic digitalization in oil and gas: a case study on mixed reality and digital twins. IEEE Access, 2024, 12: 87248–87267

[151] Moreno-Garcia C F, Elyan E. Digitisation of assets from the oil & gas industry: challenges and opportunities. 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW). Sydney: IEEE, 2019: 2–5

[152] Sierla S A , Karhela T A , Vyatkin V . Automatic generation of pipelines into a 3D industrial process model. IEEE Access, 2017, 5: 26591–26603

[153] Shteriyanov V , Dzhusupova R , Bosch J , Olsson H H . BlueprintSymVL: a discriminative benchmark for VLM symbol recognition in engineering blueprints. Results in Engineering, 2025, 28: 108171

[154] Dzhusupova R, Bosch J, Olsson H H. Challenges in developing and deploying AI in the engineering, procurement, and construction industry. In: Proceedings of the 2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC). Los Alamitos: IEEE, 2022: 1070–1075

[155] Vogt L, Urbas L. Automation of automation: mapping LLM capabilities to the modular plant engineering workflow. In: Proceedings of the 2025 IEEE 30th International Conference on Emerging Technologies and Factory Automation (ETFA). Porto: IEEE, 2025: 1–8

[156] Huusom J K , Jones M N , Kager J , Dam-Johansen K , Dreyer J A H . Building a scalable digital infrastructure for a (bio)chemical engineering pilot plant: a case study from DTU. Digital Chemical Engineering, 2025, 17: 100269

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