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

TL;DR

This paper introduces a system-level digital twin framework that optimizes energy efficiency in batch manufacturing by integrating process dynamics with real-time energy pricing for improved scheduling and control.

Contribution

It presents a novel digital twin framework that combines batch process modeling with TOU energy pricing for real-time energy-efficient decision-making.

Findings

Significant energy savings demonstrated in simulations

Effective real-time batch scheduling based on energy costs

Framework supports sustainable manufacturing practices

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.