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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*
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, [email protected]

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.

Type
Article
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

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