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Article

Data-Driven Digital Twins for Technical Building Services Operation in Factories: A Cooling Tower Case Study

1
Chair of Sustainable Manufacturing and Life Cycle Engineering, Institute of Machine Tools and Production Technology (IWF), Technische Universität Braunschweig, Langer Kamp 19B, 38106 Braunschweig, Germany
2
Fraunhofer Institute for Surface Engineering and Thin Films IST, Bienroder Weg 54E, 38108 Braunschweig, Germany
*
Author to whom correspondence should be addressed.
J. Manuf. Mater. Process. 2020, 4(4), 97; https://doi.org/10.3390/jmmp4040097
Received: 30 July 2020 / Revised: 24 August 2020 / Accepted: 2 September 2020 / Published: 23 September 2020
(This article belongs to the Special Issue Cyber Physical Production Systems)
Cyber-physical production systems (CPPS) and digital twins (DT) with a data-driven core enable retrospective analyses of acquired data to achieve a pervasive system understanding and can further support prospective operational management in production systems. Cost pressure and environmental compliances sensitize facility operators for energy and resource efficiency within the whole life cycle while achieving reliability requirements. In manufacturing systems, technical building services (TBS) such as cooling towers (CT) are drivers of resource demands while they fulfil a vital mission to keep the production running. Data-driven approaches, such as data mining (DM), help to support operators in their daily business. Within this paper the development of a data-driven DT for TBS operation is presented and applied on an industrial CT case study located in Germany. It aims to improve system understanding and performance prediction as essentials for a successful operational management. The approach comprises seven consecutive steps in a broadly applicable workflow based on the CRISP-DM paradigm. Step by step, the workflow is explained including a tailored data pre-processing, transformation and aggregation as well as feature selection procedure. The graphical presentation of interim results in portfolio diagrams, heat maps and Sankey diagrams amongst others to enhance the intuitive understanding of the procedure. The comparative evaluation of selected DM algorithms confirms a high prediction accuracy for cooling capacity (R2 = 0.96) by using polynomial regression and electric power demand (R2 = 0.99) by linear regression. The results are evaluated graphically and the transfer into industrial practice is discussed conclusively. View Full-Text
Keywords: digital twin; data-driven approach; data mining; CRISP-DM; cooling tower; technical building services; energy efficiency; cooling capacity; energy efficiency ratio digital twin; data-driven approach; data mining; CRISP-DM; cooling tower; technical building services; energy efficiency; cooling capacity; energy efficiency ratio
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MDPI and ACS Style

Blume, C.; Blume, S.; Thiede, S.; Herrmann, C. Data-Driven Digital Twins for Technical Building Services Operation in Factories: A Cooling Tower Case Study. J. Manuf. Mater. Process. 2020, 4, 97. https://doi.org/10.3390/jmmp4040097

AMA Style

Blume C, Blume S, Thiede S, Herrmann C. Data-Driven Digital Twins for Technical Building Services Operation in Factories: A Cooling Tower Case Study. Journal of Manufacturing and Materials Processing. 2020; 4(4):97. https://doi.org/10.3390/jmmp4040097

Chicago/Turabian Style

Blume, Christine, Stefan Blume, Sebastian Thiede, and Christoph Herrmann. 2020. "Data-Driven Digital Twins for Technical Building Services Operation in Factories: A Cooling Tower Case Study" Journal of Manufacturing and Materials Processing 4, no. 4: 97. https://doi.org/10.3390/jmmp4040097

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