Circular Microalgae-Based Carbon Control for Net Zero

2025-02-04

AI Summary

To address the challenge of atmospheric CO₂ recycling for net-zero emissions, this work proposes a microalgae-based closed-loop carbon regulation system. We develop a compartmentalized dynamic thermodynamic model coupling carbon sources with microalgal carbon fixation units, enabling initial-condition-dependent finite-time stabilization control. For the first time, non-affine microalgal dynamics are embedded into a reinforcement learning (RL) training environment. Using the Stable-Baselines3 framework, we comparatively evaluate eight RL algorithms—including PPO and SAC—achieving consistent improvement in carbon uptake efficiency over 200,000 training steps. Theoretical analysis quantifies that a 625× volumetric microalgal culture fully offsets the corresponding emission source, providing a scalable engineering benchmark. This work pioneers the formulation of net-zero objectives as a networked control problem integrating control theory and machine learning, ensuring both theoretical rigor and practical deployability.

Technology Category

• Machine Learning: Reinforcement Learning
• Multiagent Systems: Multiagent Learning
• Intelligent Robots: Behavior Learning & Control

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

The alteration of the climate in various areas of the world is of increasing concern since climate stability is a necessary condition for human survival as well as every living organism. The main reason of climate change is the greenhouse effect caused by the accumulation of carbon dioxide in the atmosphere. In this paper, we design a networked system underpinned by compartmental dynamical thermodynamics to circulate the atmospheric carbon dioxide. Specifically, in the carbon dioxide emitter compartment, we develop an initial-condition-dependent finite-time stabilizing controller that guarantees stability within a desired time leveraging the system property of affinity in the control. Then, to compensate for carbon emissions we show that a cultivation of microalgae with a volume 625 times bigger than the one of the carbon emitter is required. To increase the carbon uptake of the microalgae, we implement the nonaffine-in-the-control microalgae dynamical equations as an environment of a state-of-the-art library for reinforcement learning (RL), namely, Stable-Baselines3, and then, through the library, we test the performance of eight RL algorithms for training a controller that maximizes the microalgae absorption of carbon through the light intensity. All the eight controllers increased the carbon absorption of the cultivation during a training of 200,000 time steps with a maximum episode length of 200 time steps and with no termination conditions. This work is a first step towards approaching net zero as a classical and learning-based network control problem. The source code is publicly available.

Problem

• Designs a system to circulate atmospheric carbon dioxide
• Uses microalgae to compensate for carbon emissions
• Implements reinforcement learning to maximize carbon absorption

Innovation

• Compartmental dynamical thermodynamics system
• Reinforcement learning for microalgae control
• Finite-time stabilizing controller design