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 in complex marine environments remains a crucial challenge in operating AUS, as it holds the potential to significantly improve energy efficiency. To tackle this challenge, we propose a novel energy management approach that utilizes deep reinforcement learning (DRL) algorithm to develop efficient strategies for operating AUS. Specifically, we formulate the problem of maximizing the energy efficiency of AUS as a Markov decision process and integrate the quantile network in distributional reinforcement learning (QR-DQN) with the deep dueling network to solve it. 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 to improve the energy efficiency of AUS and enhance the sustainability of seaweed cultivation and carbon sequestration in the ocean.

翻译：近年来，人工上升流作为将富含营养的底层海水提升至表层、促进海藻生长进而增强海洋碳汇的潜在手段，日益受到关注。这促使中国研制出首个太阳能驱动的气升式人工上升流系统。然而，在复杂海洋环境中高效调度注气系统仍是运行该系统的关键挑战，因为此举有望显著提升能源效率。为应对这一挑战，我们提出一种新型能量管理方法，利用深度强化学习算法开发运行人工上升流系统的高效策略。具体而言，我们将该系统能源效率最大化问题建模为马尔可夫决策过程，并将分布式强化学习中的分位数网络与深度决斗网络相结合进行求解。通过大量仿真实验，我们评估了所提算法的性能，并证明其在减少能量浪费、确保系统稳定高效运行方面，较传统规则方法及其他深度强化学习算法具有更优效果。研究结果表明，基于深度强化学习的方法为提升人工上升流系统能源效率、增强海洋海藻养殖与碳汇可持续性提供了可行路径。