Agent-Driven Energy Optimization in Modular Manufacturing Systems

This work presents a modular framework that integrates digital twins with intelligent agents for increasing energy efficiency and reducing emissions of manufacturing systems. The presented framework includes real-time monitoring with dynamic thresholding for enhancing sustainability, scalability, and operational effectiveness. Demonstrated through synthetic data simulations, the approach shows measurable improvements in energy consumption and CO 2 emissions, justified by two main methods: variable monitoring and adaptive parameter control. The results underline the important role of intelligent agents and digital twins in enabling sustainable and circular manufacturing technologies, according to the principles of Industry 4.0 and strategies for future-ready production.