Automated Parameter Optimization and Control of Granular Fluidization Dynamics for Enhanced Coating Uniformity via Reinforcement Learning

**Abstract:** This research details the development and implementation of a Reinforcement Learning (RL) agent for real-time, automated optimization of key process parameters within fluid bed coating systems. By leveraging a custom-built physics-informed simulation environment and advanced multi-modal data sensing, the agent dynamically adjusts gas flow rate, spray nozzle position, and coating solution delivery rate to achieve unprecedented coating uniformity and efficiency in the production of pharmaceutical granules. This system, exceeding current manual and pre-programmed control methods, showcases immediate commercial potential within the pharmaceutical and food processing industries.