Ioshchikhes, Borys ; Borst, Fabian ; Weigold, Matthias (2022)
Assessing Energy Efficiency Measures for Hydraulic Systems using a Digital Twin.
In: Procedia CIRP, 107
doi: 10.1016/j.procir.2022.05.137
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
As manufacturing companies around the world face the challenge of reducing CO2 emissions and achieving their climate goals, increasing energy efficiency provides a promising solution while potentially reducing costs. Hydraulic systems are used in a wide range of applications such as heating, ventilation, air conditioning or machine tools and account for approximately 11 % of the electric energy demand in the German industry in 2017. Furthermore, up to 25 million tons of CO2 are emitted annually in Germany as a result of their operation. Against this background, the following paper aims to increase the energy efficiency of hydraulic systems through automated assessment of energy efficiency measures during system operation. Therefore, we present a modular approach for real-time assessing of energy efficiency measures using a digital twin, which contains an expert system combined with real-time simulation models. To detect inefficiencies without time consuming analysis and substantial user expertise, the expert system automatically identifies system leakage and increased flow resistance using a multi-output regression model. Finally, the expert system aims at engaging operators to implement energy efficiency measures by quantifying their respective energy saving potentials. The proposed measures are applied to the virtual representation of a hydraulic system in real-time. Therefore, a Modelica simulation model is developed, which is exported as a functional mock-up unit (FMU) and integrated into a Python framework. If measures lead to an improvement in energy efficiency, these are recommended to the operator. The overall concept is validated using a physical hydraulic system within the ETA Research Factory. The validation of the prototype shows that the developed approach can be applied to industrial applications and help in reducing their energy consumption.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Ioshchikhes, Borys ; Borst, Fabian ; Weigold, Matthias |
| Art des Eintrags: | Bibliographie |
| Titel: | Assessing Energy Efficiency Measures for Hydraulic Systems using a Digital Twin |
| Sprache: | Englisch |
| Publikationsjahr: | 2022 |
| Verlag: | Elsevier B.V. |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Procedia CIRP |
| Jahrgang/Volume einer Zeitschrift: | 107 |
| DOI: | 10.1016/j.procir.2022.05.137 |
| Kurzbeschreibung (Abstract): | As manufacturing companies around the world face the challenge of reducing CO2 emissions and achieving their climate goals, increasing energy efficiency provides a promising solution while potentially reducing costs. Hydraulic systems are used in a wide range of applications such as heating, ventilation, air conditioning or machine tools and account for approximately 11 % of the electric energy demand in the German industry in 2017. Furthermore, up to 25 million tons of CO2 are emitted annually in Germany as a result of their operation. Against this background, the following paper aims to increase the energy efficiency of hydraulic systems through automated assessment of energy efficiency measures during system operation. Therefore, we present a modular approach for real-time assessing of energy efficiency measures using a digital twin, which contains an expert system combined with real-time simulation models. To detect inefficiencies without time consuming analysis and substantial user expertise, the expert system automatically identifies system leakage and increased flow resistance using a multi-output regression model. Finally, the expert system aims at engaging operators to implement energy efficiency measures by quantifying their respective energy saving potentials. The proposed measures are applied to the virtual representation of a hydraulic system in real-time. Therefore, a Modelica simulation model is developed, which is exported as a functional mock-up unit (FMU) and integrated into a Python framework. If measures lead to an improvement in energy efficiency, these are recommended to the operator. The overall concept is validated using a physical hydraulic system within the ETA Research Factory. The validation of the prototype shows that the developed approach can be applied to industrial applications and help in reducing their energy consumption. |
| Freie Schlagworte: | expert system, machine learning, predicitve efficiency, real-time simulation, ETA im Bestand |
| Fachbereich(e)/-gebiet(e): | 16 Fachbereich Maschinenbau 16 Fachbereich Maschinenbau > Institut für Produktionsmanagement und Werkzeugmaschinen (PTW) 16 Fachbereich Maschinenbau > Institut für Produktionsmanagement und Werkzeugmaschinen (PTW) > ETA Energietechnologien und Anwendungen in der Produktion |
| Hinterlegungsdatum: | 09 Jun 2022 05:08 |
| Letzte Änderung: | 16 Jul 2024 08:56 |
| PPN: | 495452912 |
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| Suche nach Titel in: | TUfind oder in Google |
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