Dynamic Cyber-Physical Optimization: Leveraging Digital Twins and IoT for Real-Time Autonomous Industrial Process Adjustments

The growing convergence of Digital Twin (DT) technology, Internet of Things (IoT), and automation presents new opportunities for enhancing industrial processes through real-time optimization. This paper explores the development of dynamic cyber-physical systems, driven by DT and IoT, to autonomously adjust industrial processes in response to real-time data. We propose a framework that leverages machine learning algorithms, real-time IoT data, and predictive DT models to optimize production processes in manufacturing, energy systems, and healthcare sectors. The paper also examines the challenges of latency, data security, and scaling in the integration of these technologies, providing potential solutions and future directions.