  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.
