TY - GEN
T1 - A System-Level Energy-Efficient Digital Twin Framework for Runtime Control of Batch Manufacturing Processes
AU - Li, Hongliang
AU - Pangborn, Herschel C.
AU - Kovalenko, Ilya
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The manufacturing sector has a substantial influence on worldwide energy consumption. Therefore, improving manufacturing system energy efficiency is becoming increasingly important as the world strives to move toward a more resilient and sustainable energy paradigm. Batch processes are a major contributor to energy consumption in manufacturing systems. In batch manufacturing, a number of parts are grouped together before starting a batch process. To improve the scheduling and control of batch manufacturing processes, we propose a system-level energy-efficient Digital Twin framework that considers Time-of-Use (TOU) energy pricing for runtime decision-making. As part of this framework, we develop a model that combines batch manufacturing process dynamics and TOU-based energy cost. We also provide an optimization-based decision-making algorithm that makes batch scheduling decisions during runtime. A simulated case study showcases the benefits of the proposed framework.
AB - The manufacturing sector has a substantial influence on worldwide energy consumption. Therefore, improving manufacturing system energy efficiency is becoming increasingly important as the world strives to move toward a more resilient and sustainable energy paradigm. Batch processes are a major contributor to energy consumption in manufacturing systems. In batch manufacturing, a number of parts are grouped together before starting a batch process. To improve the scheduling and control of batch manufacturing processes, we propose a system-level energy-efficient Digital Twin framework that considers Time-of-Use (TOU) energy pricing for runtime decision-making. As part of this framework, we develop a model that combines batch manufacturing process dynamics and TOU-based energy cost. We also provide an optimization-based decision-making algorithm that makes batch scheduling decisions during runtime. A simulated case study showcases the benefits of the proposed framework.
UR - http://www.scopus.com/inward/record.url?scp=85174418301&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85174418301&partnerID=8YFLogxK
U2 - 10.1109/CASE56687.2023.10260642
DO - 10.1109/CASE56687.2023.10260642
M3 - Conference contribution
AN - SCOPUS:85174418301
T3 - IEEE International Conference on Automation Science and Engineering
BT - 2023 IEEE 19th International Conference on Automation Science and Engineering, CASE 2023
PB - IEEE Computer Society
T2 - 19th IEEE International Conference on Automation Science and Engineering, CASE 2023
Y2 - 26 August 2023 through 30 August 2023
ER -