Assessing Energy Efficiency Measures for Hydraulic Systems using a Digital Twin

高效能源利用 模块化设计 Python(编程语言) 可靠性工程 计算机科学 工程类 模拟 操作系统 电气工程
作者
Borys Ioshchikhes,Fabian Borst,Matthias Weigold
出处
期刊:Procedia CIRP [Elsevier]
卷期号:107: 1232-1237 被引量:2
标识
DOI:10.1016/j.procir.2022.05.137
摘要

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.

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