Physics-Informed Neural Networks and Deep Reinforcement Learning for Optimal Anti-Icing Strategies of Circular Tube Components in Polar Vessels

In polar environments, icing on ship deck surfaces severely compromises navigation safety. Conventional electric trace heating systems operate in continuous heating mode, resulting in high energy consumption. This study proposes an intelligent periodic heating control method that integrates physics-informed neural networks (PINNs) and deep reinforcement learning (DRL) for energy-efficient anti-icing of circular pipe components on polar vessels. Using a polar coupled environment simulation platform, experiments were conducted on electric heating anti-icing for circular pipe components. Temperature data under various heating modes were collected, and a physically constrained PINN temperature prediction model was constructed, achieving high prediction accuracy with limited samples (test set R2 = 0.9091; 5-fold cross-validation R2 = 0.8877 ± 0.0312). The DRL agent trained in this virtual environment autonomously optimized the heating strategy, yielding optimal cycle parameters: heating ratio D = 0.722 and cycle duration τ = 88 s. While maintaining surface temperatures above 0 °C against a −10 °C ambient baseline, this strategy achieved a unit energy consumption of 0.27 kJ/°C, representing a 63% reduction compared to conventional continuous heating. This study provides a data-physics fusion control approach for polar vessel anti-icing systems, demonstrating strong potential for engineering applications.