Deep Reinforcement Learning for Artificial Upwelling Energy Management

TL;DR

This paper introduces a deep reinforcement learning approach to optimize energy management in artificial upwelling systems, significantly improving efficiency and sustainability in oceanic carbon sequestration efforts.

Contribution

It develops a novel DRL-based strategy for AUS energy management, integrating QR-DQN with deep dueling networks, outperforming traditional methods in simulations.

Findings

DRL approach reduces energy wastage in AUS operations

Proposed method outperforms rule-based and other DRL algorithms

Enhances sustainability of seaweed cultivation and carbon sequestration

Abstract

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.