Author ORCID Identifier
Date of Graduation
Statler College of Engineering and Mineral Resources
Industrial and Managements Systems Engineering
In this thesis, an Energy Digital Twin for smart manufacturing systems was developed and evaluated. 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. Rising costs and the environmental impacts related to energy consumption have grown in importance worldwide. There are elevated concerns in sectors like manufacturing, leading to an urgent quest to reduce energy consumption. A recent advancement in Industry 4.0 technology, the Digital Twin, represents a promising smart technology and tool that researchers are investigating to help reduce energy costs at the shopfloor level by analyzing and optimizing energy consumption. Energy Digital Twins are a relatively new research field, only gaining popularity in applications and academia within the last five years. As a new area of interest, the state of the art of Energy Digital Twins in smart manufacturing was studied through a comprehensive literature review to lay the foundation for this study. The review uncovered that there is only one ‘true’ Energy Digital Twin application in published research based on the search criteria used. To address this research gap, the aim of this thesis is to create and evaluate an application of an energy optimizing Digital Twin of the Festo CP Lab Heating Tunnel station. 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. The proposed methodology was validated by performing an array of trial runs varying the input parameters. 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 research completed in this thesis successfully achieved to create 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.
Billey, Anna, "Energy Digital Twins in Smart Manufacturing Systems" (2023). Graduate Theses, Dissertations, and Problem Reports. 11859.
Available for download on Tuesday, April 23, 2024