Dual-Surface dew point prediction and control in confined spaces using enhanced deep reinforcement learning

The condensation phenomenon on the internal and external surfaces of industrial confined spaces poses a serious threat to operational safety and service life of equipment. Traditional control strategies based on fixed thresholds often fail to dynamically adapt to complex environmental changes and neglect the synergistic risk of condensation on both surfaces. This study proposes a novel closed-loop intelligent anti-condensation system, addressing this problem by integrating an enhanced deep deterministic policy gradient algorithm with a practical cloud collaboration architecture. The core algorithm incorporates a minimum safety margin reward function for both surfaces to mitigate condensation risk and includes an intelligent pulse control module to enhance energy efficiency. In practical deployment, a cloud server performs inference with the deep reinforcement learning model, while a lightweight controller acts as an edge unit to sense and execute actions in real time. Experimental validation across five globally representative environmental scenarios demonstrated the system's superior performance. Compared with an optimized double-loop PID controller, this method reduces total energy consumption by 44.9% in a single day, achieves high operational safety with zero condensation events and effectively overcomes the traditional trade-off between safety and energy efficiency, and provides a reliable and intelligent solution for condensation prevention in critical industrial applications.