Transforming Data Centers Into Artificial Trees: NSGA-III Optimization CO 2 Capture and Utilization

This paper explores the novel integration of AI powered direct air capture (DAC) within data centers to transform them into carbon-negative computational hubs. By leveraging AI-driven workload scheduling, these data centers actively capture CO2 from the atmosphere while performing high-intensity AI computations. We propose a multi-objective optimization framework based on the non-dominated sorting genetic algorithm III (NSGA-III) to maximize CO2 capture efficiency, minimize energy consumption, and optimize carbon credit monetization through blockchain-based trading. The framework models dynamic workload allocation, DAC process parameters, and energy constraints, ensuring seamless integration with renewable energy sources and storage systems. Through detailed simulations, we demonstrate the feasibility of AI-powered DAC enabled data centers and their potential to revolutionize carbon sequestration while sustaining large-scale computing demands. This research introduces a disruptive paradigm for sustainable AI infrastructure, paving the way for future carbon-neutral and energy-optimized data center architectures.