A System-Level Energy-Efficient Digital Twin Framework for Runtime Control of Batch Manufacturing Processes

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publication2023 IEEE 19th International Conference on Automation Science and Engineering, CASE 2023
PublisherIEEE Computer Society
ISBN (Electronic)9798350320695
DOIs
StatePublished - 2023
Event19th IEEE International Conference on Automation Science and Engineering, CASE 2023 - Auckland, New Zealand
Duration: Aug 26 2023Aug 30 2023

Publication series

NameIEEE International Conference on Automation Science and Engineering
Volume2023-August
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089

Conference

Conference19th IEEE International Conference on Automation Science and Engineering, CASE 2023
Country/TerritoryNew Zealand
CityAuckland
Period8/26/238/30/23

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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