Deep Reinforcement Learning for Artificial Upwelling Energy Management
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The potential of artificial upwelling (AU) as a means of lifting nutrient-rich bottom water to the surface, stimulating seaweed growth, and consequently enhancing ocean carbon sequestration, has been gaining increasing attention in recent years. This has led to the development of the first solar-powered and air-lifted AU system (AUS) in China. However, efficient scheduling of air injection systems remains a crucial challenge in operating AUS, as it holds the potential to significantly improve system efficiency. Conventional approaches based on rules or models are often impractical due to the complex and heterogeneous nature of the marine environment and its associated disturbances. To address this challenge, we propose a novel energy management approach that utilizes deep reinforcement learning (DRL) algorithm to develop efficient strategies for operating AUS. Through extensive simulations, we evaluate the performance of our algorithm and demonstrate its superior effectiveness over traditional rule-based approaches and other DRL algorithms in reducing energy wastage while ensuring the stable and efficient operation of AUS. Our findings suggest that a DRL-based approach offers a promising way for improving the efficiency of AUS and enhancing the sustainability of seaweed cultivation and carbon sequestration in the ocean.
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