Cognitive Digital Twin Architecture Using Reinforcement-Optimized Deep Learning for Self-Adaptive Industrial Process Control

The development of Digital Twin (DT) technology from descriptive simulation to independent decision-making represents a big leap towards Industry 5.0. In this paper, a Cognitive Digital Twin (CDT) architecture with Reinforcement-Optimized Deep Learning (RODL) integration is proposed for intelligent and adaptive control of industrial processes. The architecture combines real-time sensor streams with deep neural representations, where the Deep Q-Learning (DQL) agent is self-tuning the operation parameters like energy usage, equipment dependability and production rate. The Multi-Objective Swarm Optimization (MOSO) strategy directs the reinforcement agent to balance performance and sustainability goals optimally. The results demonstrate the effectiveness of the proposed framework. Component-wise evaluation shows that pre-processing achieves an RMSE of 0.08 and the cognitive deep learning model predicts with 95.6% accuracy. The reinforcement learning agent attains an average reward of 0.92 and swarm optimization balances multiple objectives with a fitness score of 0.89. The overall control feedback system achieves a performance score of 0.94. Through cognitive integration, CDT-RODL architecture is a scalable platform for next-generation intelligent manufacturing, energy management. and healthcare automation.