Next-Generation Networks and AI for Optimized Carbon Capture and Storage: Enhancing Efficiency and Minimizing Emissions

Omoniyi David Olufemi

Keywords: Next-Generation Networks (NGNs), Artificial Intelligence (AI), Carbon Capture and Storage (CCS), CO2 Capture, Emissions Reduction, Predictive Maintenance, Energy Efficiency, IoT in CCS, Low-Latency Communication, Machine Learning, Automated Control Systems, Data Security in NGNs, AI-Powered Analytics, Scalable CCS Systems, Climate Change Mitigation, Global Sustainability Goals, Geological Storage.

Abstract: The rapid advancement of Next-Generation Networks (NGNs) and Artificial Intelligence (AI) holds transformative potential for addressing climate change, specifically in the context of Carbon Capture and Storage (CCS). CCS is crucial for mitigating the impacts of carbon emissions on the environment, yet its effectiveness is often hindered by operational inefficiencies and high energy consumption. This paper explores the integration of NGNs and AI to optimize CCS systems, focusing on enhancing their efficiency, minimizing emissions, and ensuring sustainability. By leveraging the high-speed, low-latency capabilities of NGNs and the decision-making power of AI, CCS can become more scalable, cost-effective, and environmentally viable. The article examines key technologies, their synergistic impacts, and the challenges and opportunities in deploying AI-driven solutions in NGN-enhanced CCS systems.

Submission Number: 26