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IDENTIFICATION AND RETRIEVAL OF RELEVANT INFORMATION FOR INSTANTIATING DIGITAL TWINS DURING THE CONSTRUCTION OF PROCESS PLANTS

Published online by Cambridge University Press:  19 June 2023

Max Layer ,
Sebastian Neubert ,
Lea Tiemann  and
Ralph Stelzer
Max Layer*
Affiliation:
Siemens Energy Global GmbH & Co.KG;
Sebastian Neubert
Affiliation:
Siemens Energy Global GmbH & Co.KG;
Lea Tiemann
Affiliation:
Siemens Energy Global GmbH & Co.KG;
Ralph Stelzer
Affiliation:
Technische Universität Dresden
*
Layer, Max, Siemens Energy Global GmbH & Co.KG, Germany, MAX.LAYER@SIEMENS-ENERGY.COM

Abstract

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While volume-driven industries such as automotive are characterized by a high degree of data backflow across all production cycles, there is still a certain residue in the planning and construction of process plants. This is firstly due to the high proportion of customer-specific requirements and secondly to the significant amount of value added on site during construction. To handle recurring project-specific process plants as time- and cost-efficiently as possible, optimal information exchange among contractors of various disciplines and the plant developer is a prerequisite. For this purpose, a holistic digital representation of the plant is created, which consolidates all relevant information in one place serving as a foundation of multiple digital twins. An approach to identify and define relevant information depending on their subsequent use is developed. On this basis, a framework is proposed to enable a multipliable BOM-based automatic definition of information backflow to instantiate digital representations in parallel to the planning and construction process. Furthermore, project-specific contextual information will be captured and referenced in a structured form preventing their loss for subsequent similar projects.

Keywords

Information managementContextual InformationInstantiationDigital / Digitised engineering value chainsKnowledge management
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 2175 - 2184
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

References

2014/68/EU. Pressure Equipment Directive, 2014. (accessed 2/15/2023).Google Scholar
Ackoff, Russell L. (1989). From data to wisdom. Journal of applied systems analysis 16 (1), 3–9.Google Scholar
Ballesteros, John R. (Ed.) (2004). Meta-model instantiation for geoscientific data collection. ITC.Google Scholar
Bosché, Frédéric/Ahmed, Mahmoud/Turkan, Yelda/Haas, Carl T./Haas, Ralph (2015). The value of integrating Scan-to-BIM and Scan-vs-BIM techniques for construction monitoring using laser scanning and BIM: The case of cylindrical MEP components. Automation in Construction 49, 201–213. https://doi.org/10.1016/j.autcon.2014.05.014.CrossRefGoogle Scholar
Braaksma, A.J.J./Klingenberg, W./ van Exel, P.W.H.M. (2011). A review of the use of asset information standards for collaboration in the process industry. Computers in industry 62 (3), 337–350. https://doi.org/10.1016/j.compind.2010.10.003.CrossRefGoogle Scholar
Brundage, Michael P./Sexton, Thurston/Hodkiewicz, Melinda/Dima, Alden/Lukens, Sarah (2021). Technical language processing: Unlocking maintenance knowledge. Manufacturing Letters 27, 42–46. https://doi.org/10.1016/j.mfglet.2020.11.001.CrossRefGoogle Scholar
Eigner, Martin/Stelzer, Ralph (2009). Product lifecycle management: Ein Leitfaden für product development und life cycle management. Springer Science & Business Media.CrossRefGoogle Scholar
Elfving, Jan Alarik (2003). Exploration of opportunities to reduce lead times for engineered-to-order products. Berkeley, University of California.Google Scholar
Esfahani, Mansour Esnaashary/Rausch, Christopher/Sharif, Mohammad Mahdi/Chen, Qian/Haas, Carl/Adey, Bryan T. (2021). Quantitative investigation on the accuracy and precision of Scan-to-BIM under different modelling scenarios. Automation in Construction 126, 103686. https://doi.org/10.1016/j.autcon.2021.103686.CrossRefGoogle Scholar
Ge, Zhiqiang/Song, Zhihuan/Ding, Steven X./Huang, Biao (2017). Data Mining and Analytics in the Process Industry: The Role of Machine Learning. IEEE Access 5, 20590–20616. https://doi.org/10.1109/ACCESS.2017.2756872.CrossRefGoogle Scholar
Gepp, M./Vollmar, J./Schaeffler, T. (2014). Standardization programs in the industrial plant business: Best practices and lessons learned. In: 2014 IEEE International Conference on Industrial Engineering and Engineering Management, 2014 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Selangor Darul Ehsan, Malaysia, 09.12.2014 - 12.12.2014. IEEE, 122–126.CrossRefGoogle Scholar
Gorecky, Dominic/Weyer, Stephan/Hennecke, André/Zühlke, Detlef (2016). Design and Instantiation of a Modular System Architecture for Smart Factories. IFAC-PapersOnLine 49 (31), 79–84. https://doi.org/10.1016/j.ifacol.2016.12.165.CrossRefGoogle Scholar
Hicks, B. J./Culley, S. J./Allen, R. D./Mullineux, G. (2002). A framework for the requirements of capturing, storing and reusing information and knowledge in engineering design. International Journal of Information Management 22 (4), 263–280. https://doi.org/10.1016/S0268-4012(02)00012-9.CrossRefGoogle Scholar
IEC 62337:2012. Commissioning of electrical, instrumentation and control systems in the process industry, 2012. Geneva, Switzerland. (accessed 11/7/2022).Google Scholar
IEC 62381:2012. Automation systems in the process industry, 2012. Geneva, Switzerland. (accessed 11/2/2022).Google Scholar
Ismail, Ali/Strug, Barbara/Ślusarczyk, Grażyna (2018). Building Knowledge Extraction from BIM/IFC Data for Analysis in Graph Databases. In: Leszek Rutkowski/Rafał Scherer/Marcin Korytkowski et al. (Eds.). Artificial Intelligence and Soft Computing. Cham, Springer International Publishing, 652–664.CrossRefGoogle Scholar
ISO 2382-1:1993. Information technology - Vocabulary - Part 1: Fundamental Terms, 1993. Switzerland. (accessed 11/21/2022).Google Scholar
ISO 23952:2020. Automation systems and integration — Quality information framework (QIF), 2020. Switzerland. (accessed 10/31/2022).Google Scholar
Jiang, Shuo/Hu, Jie/Magee, Christopher L./Luo, Jianxi (2022). Deep Learning for Technical Document Classification. IEEE Transactions on Engineering Management, 1–17. https://doi.org/10.1109/TEM.2022.3152216.CrossRefGoogle Scholar
Kim, Byung Chul/Kim, Bongcheol/Park, Sangjin/Teijgeler, Hans/Mun, Duhwan (2020). ISO 15926–based integration of process plant life-cycle information including maintenance activity. Concurrent Engineering 28 (1), 58–71. https://doi.org/10.1177/1063293X19894041.CrossRefGoogle Scholar
Lechler, Armin/Riedel, Oliver/Coupek, Daniel (2017). VIRTUAL REPRESENTATION OF PHYSICAL OBJECTS FOR SOFTWARE DEFINED MANUFACTURING.CrossRefGoogle Scholar
Lettenmeier, Philipp (2020). Efficiency – Electrolysis. White Paper. Available online at https://assets.siemens-energy.com/siemens/assets/api/uuid:a33a8c39-b694-4d91-a0b5-4d8c9464e96c/efficiency-white-paper.pdf.Google Scholar
Li, Zhanjun/Raskin, Victor/Ramani, Karthik (2008). Developing Engineering Ontology for Information Retrieval. Journal of Computing and Information Science in Engineering 8 (1). https://doi.org/10.1115/1.2830851.CrossRefGoogle Scholar
Liew, Anthony (2007). Understanding data, information, knowledge and their inter-relationships. Journal of knowledge management practice 8 (2), 1–16.Google Scholar
Luttmer, Janosch/Ehring, Dominik/Pluhnau, Robin/Nagarajah, Arun (2021). REPRESENTATION AND APPLICATION OF DIGITAL STANDARDS USING KNOWLEDGE GRAPHS. Proceedings of the Design Society 1, 2551–2560. https://doi.org/10.1017/pds.2021.516.CrossRefGoogle Scholar
Mallier, Lise/Hétreux, Gilles/Thery-Hétreux, Raphaele/Baudet, Philippe (2021). A modelling framework for energy system planning: Application to CHP plants participating in the electricity market. Energy 214, 118976.CrossRefGoogle Scholar
Martinez, Gerardo Santillan/Sierla, Seppo/Karhela, Tommi/Vyatkin, Valeriy (2018). Automatic Generation of a Simulation-Based Digital Twin of an Industrial Process Plant. In: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society, IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society, Washington, DC, 21.10.2018 - 23.10.2018. IEEE, 3084–3089.CrossRefGoogle Scholar
May, Marvin Carl/Overbeck, Leonard/Wurster, Marco/Kuhnle, Andreas/Lanza, Gisela (2021). Foresighted digital twin for situational agent selection in production control. Procedia CIRP 99, 27–32. https://doi.org/10.1016/j.procir.2021.03.005.CrossRefGoogle Scholar
McAllester, David/Zabih, Ramin (1986). Boolean classes. In: Conference proceedings on Object-oriented programming systems, languages and applications, 417–423.CrossRefGoogle Scholar
Nouri, Mahdi/Lucke, Eberhard (2022). Life cycle of a process plant. Amsterdam, Netherlands, Elsevier.Google Scholar
Pinquié, Romain/Véron, Philippe/Segonds, Frédéric/Zynda, Thomas (2019). A Property Graph Data Model for a Context-Aware Design Assistant. In: Clement Fortin/Louis Rivest/Alain Bernard et al. (Eds.). Product Lifecycle Management in the Digital Twin Era. Cham, Springer International Publishing, 181–190.CrossRefGoogle Scholar
Ramírez, José/Molina, Arturo (2021). Improving Manufacturing System Design by Instantiation of the Integrated Product, Process and Manufacturing System Development Reference Framework. In: Alexandre Dolgui/Alain Bernard/David Lemoine et al. (Eds.). Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems. Cham, Springer International Publishing, 99–107.CrossRefGoogle Scholar
Saske, Bernhard/Schwoch, Sebastian/Paetzold, Kristin/Layer, Max/Neubert, Sebastian/Leidich, Jonathan/Robl, Peter (2022). Digitale Abbilder als Basis Digitaler Zwillinge im Anlagenbau: Besonderheiten, Herausforderungen und Lösungsansätze. Industrie 4.0 Management 2022 (5), 21–24. https://doi.org/10.30844/IM_22-5_21-24.CrossRefGoogle Scholar
Shafiee, Sara/Kristjansdottir, Katrin/Hvam, Lars (2017). Automatic Identification of Similarities across Products to Improve the Configuration Process in ETO Companies. International Journal of Industrial Engineering and Management 8, 167–176.CrossRefGoogle Scholar
Energy AG, Siemens (2022). Silyzer 300 Datasheet. Available online at https://assets.siemens-energy.com/siemens/assets/api/uuid:a193b68f-7ab4-4536-abe2-c23e01d0b526/datasheet-silyzer300.pdf?ste_sid=18909f929d5f3bfcad922e9252ae5689 (accessed 11/7/2022).Google Scholar
Son, Hyojoo/Bosché, Frédéric/Kim, Changwan (2015). As-built data acquisition and its use in production monitoring and automated layout of civil infrastructure: A survey. Advanced Engineering Informatics 29 (2), 172–183. https://doi.org/10.1016/j.aei.2015.01.009.CrossRefGoogle Scholar
Stark, Rainer/Kind, Simon/Neumeyer, Sebastian (2017). Innovations in digital modelling for next generation manufacturing system design. CIRP Annals 66 (1), 169–172. https://doi.org/10.1016/j.cirp.2017.04.045.CrossRefGoogle Scholar
Tang, D./Clarke, J. A. (1993). Application of the object oriented programming paradigm to building plant system modelling. In: Proceedings of the Building Simulation, 317–323.Google Scholar
Consulting Group, THEMA (2021). Hydrogen deployment accelerates, but a lot remains to reach 40 GW EU target by 2030. Available online at https://thema.no/en/nyheter/store-steg-naermere-men-langt-igjen-til-40-gw-elektrolyse-i-eu-i-2030/ (accessed 11/7/2022).Google Scholar
Udeaja, Chika E./Kamara, John M./Carrillo, Patricia M./Anumba, Chimay J./Bouchlaghem, Nasreddine/Tan, Hai Chen (2008). A web-based prototype for live capture and reuse of construction project knowledge. Automation in Construction 17 (7), 839–851. https://doi.org/10.1016/j.autcon.2008.02.009.CrossRefGoogle Scholar
Watermeyer, Peter (2002). Handbook for process plant project engineers. John Wiley & Sons.Google Scholar
Wheeler, Robert/Rosenblum, Bruce/West, Lesley (Eds.) (2016). NISO STS Project Overview and Update. National Center for Biotechnology Information (US).Google Scholar
Zhao, Huaxuan/Pan, Yueling/Yang, Feng (2020). Research on Information Extraction of Technical Documents and Construction of Domain Knowledge Graph. IEEE Access 8, 168087–168098. https://doi.org/10.1109/ACCESS.2020.3024070.CrossRefGoogle Scholar