journal article Open Access Aug 04, 2023

The Industrial Digital Energy Twin as a Tool for the Comprehensive Optimization of Industrial Processes

Processes Vol. 11 No. 8 pp. 2353 · MDPI AG
View at Publisher Save 10.3390/pr11082353
Abstract
Industrial manufacturing processes have evolved and improved since the disruption of the Industry 4.0 paradigm, while energy has progressively become a strategic resource required to maintain industrial competitiveness while maximizing quality and minimizing environmental impacts. In this context of global changes leading to social and economic impact in the short term and an unprecedented climate crisis, Digital Twins for Energy Efficiency in manufacturing processes provide companies with a tool to address this complex situation. Nevertheless, already existing Digital Twins applied for energy efficiency in a manufacturing process lack a flexible structure that easily replicates the real behavior of consuming machines while integrating it in complex upper-level environments. This paper presents a combined multi-paradigm approach to industrial process modeling developed and applied during the GENERTWIN project. The tool allows users to predict energy consumption and costs and, at the same time, evaluates the behavior of the process under certain productive changes to maximize consumption optimization, production efficiency and process flexibility.
Topics

No keywords indexed for this article. Browse by subject →

References
25
[1]
Grieves, M. (2014). Digital Twin: Manufacturing Excellence through Virtual Factory Replication, Michael W. Grieves, LLC. White Paper.
[2]
May "Energy management in manufacturing: From literature review to a conceptual framework" J. Clean. Prod. (2017) 10.1016/j.jclepro.2016.10.191
[3]
International Organization for Standardization (2023, July 31). Energy Management Systems-Requirements with Guidance for Use (ISO 50001). Available online: https://www.iso.org/standard/69426.html.
[4]
Wahren "Methodology for energy efficiency on process level" Procedia CIRP (2013) 10.1016/j.procir.2013.06.048
[5]
Dolge "Composite index for energy efficiency evaluation of industrial sector: Sub-sectoral comparison" Environ. Sustain. Indic. (2020)
[6]
Xiong "The impact of industrial structure efficiency on provincial industrial energy efficiency in China" J. Clean. Prod. (2019) 10.1016/j.jclepro.2019.01.095
[7]
Asociación Europea de la Industria Cerámica (2023, July 20). Hoja de Ruta de la Industria Cerámica, 2050. Available online: https://www.ascer.es/verDocumento.ashx?documentoId=2714&tipo=pdf.
[8]
Digital twins-based smart manufacturing system design in Industry 4.0: A review

Jiewu Leng, Weiming Shen, Xinyu Li et al.

Journal of Manufacturing Systems 2021 10.1016/j.jmsy.2021.05.011
[9]
Tekinerdogan, B., and Verdouw, C. (2020). Systems architecture design pattern catalog for developing digital twins. Sensors, 20. 10.3390/s20185103
[10]
Maeyer, C., and Markopoulos, P. (2021, January 20–21). Future outlook on the materialisation, expectations and implementation of Digital Twins in healthcare. Proceedings of the 34th British HCI Conference (HCI2021), London, UK.
[11]
Nasirahmadi, A., and Hensel, O. (2022). Toward the Next Generation of Digitalization in Agriculture Based on Digital Twin Paradigm. Sensors, 22. 10.3390/s22020498
[12]
Opoku "Digital twin application in the construction industry: A literature review" J. Build. Eng. (2021) 10.1016/j.jobe.2021.102726
[13]
Schoonenberg, W., and Farid, A. (2015, January 9–12). A Dynamic Production Model for Industrial Systems Energy Management. Proceedings of the 2015 IEEE International Conference on Systems, Man, and Cybernetics, Hong Kong, China. 10.1109/smc.2015.14
[14]
Mo "A framework for manufacturing system reconfiguration and optimisation utilising digital twins and modular artificial intelligence" Robot. Comput.-Integr. Manuf. (2023) 10.1016/j.rcim.2022.102524
[15]
Energy digital twin technology for industrial energy management: Classification, challenges and future

Wei Yu, Panos Patros, Brent Young et al.

Renewable and Sustainable Energy Reviews 2022 10.1016/j.rser.2022.112407
[16]
Zhang "A Reconfigurable Modeling Approach for Digital Twin-based Manufacturing System" Procedia CIRP (2019) 10.1016/j.procir.2019.03.141
[17]
Fang "A new approach to scheduling in manufacturing for power consumption and carbon footprint reduction" J. Manuf. Syst. (2011) 10.1016/j.jmsy.2011.08.004
[18]
Zhang "A method for minimizing the energy consumption of machining system: Integration of process planning and scheduling" J. Clean. Prod. (2016) 10.1016/j.jclepro.2016.03.101
[19]
Ma "Energy-cyber-physical system enabled management for energy-intensive manufacturing industries" J. Clean. Prod. (2019) 10.1016/j.jclepro.2019.04.134
[20]
Keshari "Discrete event simulation approach for energy efficient resource management in paper pulp industry" Procedia CIRP (2018) 10.1016/j.procir.2018.08.324
[21]
Kant "Predictive Modelling for Energy Consumption in Machining Using Artificial Neural Network" Procedia CIRP (2015) 10.1016/j.procir.2015.08.081
[22]
Holler, J., Tsiatsis, V., Mulligan, C., Karnouskos, S., Avesand, S., and Boyle, D. (2014). From Machine-to-Machine to the Internet of Things: Introduction to a New Age of Intelligence, Elsevier Science.
[23]
Ferreno-González, S. (2023, July 31). Aproximación Metodológica a la Implantación del Gemelo Digital en Buques. Available online: https://ruc.udc.es/dspace/bitstream/handle/2183/30974/FerrenoGonzalez_Sara_TD_2022.pdf?sequence=2.
[24]
Schroeder "A methodology for digital twin modeling and deployment for industry 4.0" Proc. IEEE (2020) 10.1109/jproc.2020.3032444
[25]
(2023, July 31). Efficiency Valuation Organization: International Performance Measurement and Verification Protocol (IPMVP). Available online: https://evo-world.org/en/products-services-mainmenu-en/protocols/ipmvp.
Cited By
8
The Canadian Journal of Chemical En...
Metrics
8
Citations
25
References
Details
Published
Aug 04, 2023
Vol/Issue
11(8)
Pages
2353
License
View
Funding
IVACE (Instituto Valenciano de Competitividad Empresarial) Award: IMDEEA/2022/16
Cite This Article
Alejandro Rubio-Rico, Fernando Mengod-Bautista, Andrés Lluna-Arriaga, et al. (2023). The Industrial Digital Energy Twin as a Tool for the Comprehensive Optimization of Industrial Processes. Processes, 11(8), 2353. https://doi.org/10.3390/pr11082353
Related

You May Also Like

DPPH Radical Scavenging Assay

İlhami Gulçin, Saleh H. Alwasel · 2023

946 citations

Alkaline Water Electrolysis Powered by Renewable Energy: A Review

Jörn Brauns, Thomas Turek · 2020

696 citations

A Review of Stereolithography: Processes and Systems

Junxi Huang, Qin Qin · 2020

504 citations

Various Approaches for the Detoxification of Toxic Dyes in Wastewater

Abdulmohsen K. D. Alsukaibi · 2022

357 citations

Metal Ions, Metal Chelators and Metal Chelating Assay as Antioxidant Method

İlhami Gulçin, Saleh H. Alwasel · 2022

343 citations