Optimized Automatic Temperature and Humidity Control for Tobacco Storage Using TwinCAT and Deep Reinforcement Learning

With the rapid development of the tobacco industry, precise temperature and humidity control in storage environments has become essential for maintaining tobacco leaf quality. Traditional manual control methods suffer from low efficiency and limited accuracy, failing to meet modern storage demands. This study proposes an optimized automatic control system integrating TwinCAT and deep reinforcement learning (DRL) to enhance climate regulation in tobacco warehouses. Leveraging TwinCAT’s real-time control capabilities and DRL’s adaptive decision-making, the system achieves precise environmental regulation. Experimental results demonstrate that temperature and humidity control errors are reduced to ±0.5 °C and ±3%, respectively. Compared to conventional methods, the proposed system lowers energy consumption by 20% and reduces the mildew rate of stored tobacco by 15%, significantly improving storage quality. This work offers a novel technical framework for intelligent environmental control in tobacco storage and provides valuable insights for broader applications in similar domains.