Among core Industry 4.0 technologies, the Digital Twin represents a promising smart technology and tool that researchers are investigating to help improve energy efficiency at the shopfloor level by analyzing and optimizing energy consumption. Energy Digital Twins are a relatively new area of research, only gaining popularity in industry applications and academia recently. where a comprehensive literature review uncovered only one ‘true’ Energy Digital Twin application across published research to date. To address this research gap, an Energy Digital Twin for smart manufacturing systems was developed and evaluated in this study. In particular, the study focused on bidirectional parameter communication between the physical and the virtual part with the aim of optimizing the energy used in the manufacturing process. To address this research gap, the research objective is to create and evaluate an application of an energy optimizing Digital Twin for a Heating Tunnel. Following the definition of a Digital Twin, the research methodology and experimental setup have three major components: i) the Heating Tunnel as the physical object, ii) the digital counterpart constructed using Python to house the digital control logic and linear energy optimization feedback model, and iii) the connecting fabric, in form of a bidirectional OPC UA communication protocol. The optimization model ingests input parameters of setpoint temperature, power level, and targeted overshoot time, and after running the simulation, returns a calculated value of the required turn off temperature to the real-time heating process of the physical system. Results show that the Energy Digital Twin is effective at maintaining the maximum temperature range of the Heating Tunnel during the heating process, in addition to reducing the energy consumption and cost for all trial runs compared to the original process. Overall, the study successfully created and evaluated a functioning energy optimizing Digital Twin with bidirectional, automated feedback. The results of this research emphasize the potential impact of Energy Digital Twin applications in any manufacturing process and show the promise of future work in this realm of Energy Digital Twins.

中文翻译：

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智能制造系统中的能源数字孪生：案例研究

在工业 4.0 的核心技术中，数字孪生代表了一种有前景的智能技术和工具，研究人员正在研究该技术和工具，以通过分析和优化能源消耗来帮助提高车间层面的能源效率。能源数字孪生是一个相对较新的研究领域，最近才在行业应用和学术界流行起来。一项全面的文献综述在迄今为止已发表的研究中仅发现了一个“真正的”能源数字孪生应用。为了弥补这一研究空白，本研究开发并评估了用于智能制造系统的能源数字孪生。该研究特别关注物理部件和虚拟部件之间的双向参数通信，旨在优化制造过程中使用的能源。为了弥补这一研究空白，研究目标是创建和评估供热隧道能源优化数字孪生的应用。根据数字孪生的定义，研究方法和实验设置由三个主要部分组成：i) 作为物理对象的加热隧道，ii) 使用 Python 构建的数字对应物，用于容纳数字控制逻辑和线性能量优化反馈模型， iii) 连接结构，采用双向 OPC UA 通信协议的形式。优化模型获取设定点温度、功率水平和目标过冲时间等输入参数，并在运行仿真后，将所需关闭温度的计算值返回到物理系统的实时加热过程。结果表明，能源数字孪生能够有效维持加热隧道在加热过程中的最大温度范围，并且与原始过程相比，降低了所有试运行的能耗和成本。总体而言，该研究成功创建并评估了具有双向自动反馈功能的能源优化数字孪生。这项研究的结果强调了能源数字孪生应用在任何制造过程中的潜在影响，并展示了能源数字孪生这一领域未来工作的前景。