Towards Sustainable Manufacturing: Digital Twins for Enhanced Energy Efficiency

The rapid growth in industrialization and the increasing demand for energy have brought about a critical need for energy efficiency in various industries. Adopting intelligent manufacturing techniques is imperative to optimize energy consumption and reduce environmental impacts caused by traditional manufacturing processes. Enhancing energy efficiency in manufacturing involves real-time monitoring, improved control mechanisms, modeling tools, and energy-efficient equipment. Two key fronts can be addressed to enhance energy efficiency in the industry: utilizing renewable energy sources for production and reducing energy consumption in manufacturing systems. Digital twins, serving as virtual replicas of assets or processes, offer a promising solution by enabling efficient monitoring and optimization in both areas. This comprehensive approach ensures enhanced energy efficiency in industrial settings. Digital twins should enable real-time monitoring, data analysis, and simulation of manufacturing processes, providing valuable insights into energy consumption patterns and identifying areas for improvement. By integrating the principles of Industry 4.0 and Internet of Things (IoT) technologies, digital twins facilitate the implementation of advanced energy management strategies and enable proactive decision-making. This research explores the importance of energy efficiency in the manufacturing sector and highlights the potential benefits of employing digital twins in achieving energy optimization and also highlights the primary challenges associated with employing digital twins for energy-efficient manufacturing. As a result, we propose a conceptual framework to address the challenges and complexities associated with implementing digital twins for energy efficiency in manufacturing. The framework includes the definition of objectives and metrics, data collection and integration from various sources, data validation, knowledge extraction, and model development and validation. Our framework utilizes well-established Key Performance Indicators (KPIs) for energy-related performance evaluation in manufacturing, offers visualization and simulation capabilities, and enables real-time feedback and control for optimizing energy usage and improving overall efficiency. Lastly, performance evaluation and reporting in digital twins for energy efficiency is proposed as a process that evaluates and measures the performance of digital twins in relation to energy efficiency, and then reports to stakeholders, providing valuable insights into energy efficiency performance and guiding decision-making for further enhancements.