Energy-Efficient Digital Twins for Circular Economy Implementation

¹ Modeling and Simulation of Intelligent Industrial Systems Laboratory (M2S2I), ENSET Mohammedia, Hassan II University, Casablanca, Morocco
² Laboratory of Design, Manufacturing, and Control, Arts et Metiers ParisTech, Metz Campus, 57070 Metz, France

^* Corresponding author: abderrahman.mansourii@gmail.com

Abstract

As industries accelerate their transition toward sustainability, integrating energy efficiency into Circular Economy (CE) strategies becomes a key priority. Digital Twins (DTs) are virtual counterparts of physical systems that enable real-time monitoring, simulation, and optimization of energy performance across product life cycles. This paper proposes a structured framework that integrates DT technology, Artificial Intelligence (AI), and predictive analytics to enhance energy efficiency and circularity in industrial operations. The proposed model enables real-time data collection, simulation, and decision-making for sustainable production and end-of-life management. A simulated manufacturing process demonstrates that DT-based optimization can achieve energy reductions of approximately 10–12% while improving recycling and reuse metrics. The framework aligns with ISO 50001 standards and the EU Circular Economy Action Plan, providing a practical path for scaling digital sustainability tools in manufacturing.