Dynamic optimization of energy efficiency with digital twins and reinforcement learning in lighthouse factories

Digital twin technology combined with reinforcement learning provides a groundbreaking solution for dynamic energy optimization in Industry 4.0, particularly in highly automated and adaptive lighthouse factories. Traditional energy management approaches struggle with real-time adaptability under fluctuating operational demands, leading to inefficiencies in energy utilization. To address this, the proposed framework integrates digital twins with reinforcement learning, leveraging the real-time synchronization and predictive analysis capabilities of digital twins to create high-fidelity virtual replicas of physical systems. Energy optimization is modeled as a Markov Decision Process, enabling the dynamic adjustment of operational parameters such as machine load distribution and power modulation. A hybrid neural network architecture, featuring twin critics and prioritized experience replay, is employed to enhance policy learning by reducing overestimation bias and focusing on transitions with higher temporal-difference errors. Experimental evaluations reveal that the framework achieves 23.7% faster convergence compared to Proximal Policy Optimization (PPO) and up to 31.5% energy savings compared to Deep Q-Networks (DQN). These results are validated through comprehensive benchmarking experiments. However, the deployment was conducted in a general simulated manufacturing environment rather than a specific automotive manufacturing case study. By demonstrating superior performance in dynamic environments, this research establishes a scalable and technically robust methodology for industrial energy optimization. The integration of digital twin technology and reinforcement learning within this framework sets a benchmark for intelligent and sustainable manufacturing, paving the way for broader adoption of advanced computing systems in industrial automation.