Artificial Intelligence in Carbon Capture and Storage Technological Advances and Future Challenges

Abstract

The escalating climate crisis necessitates the rapid development of innovative carbon reduction technologies, among which Carbon Capture and Storage (CCS) is considered a pivotal solution. However, the widespread deployment of conventional CCS is hindered by challenges related to efficiency, cost, and risk management. This review explores the transformative role of Artificial Intelligence (AI), particularly machine learning and deep learning, in overcoming these challenges. By analyzing recent literature, we demonstrate AI's significant potential in optimizing CCS processes, from accelerating the discovery of novel capture materials to enhancing the safety and efficiency of storage site monitoring and enabling intelligent system management. Despite this promise, critical challenges remain, including data scarcity, the carbon footprint of AI model training, and the need for robust regulatory frameworks. This study concludes that interdisciplinary collaboration and a focus on developing dynamic, data-driven approaches are crucial for realizing the full potential of AI-CCS integration, paving the way for its effective contribution to global decarbonization goals.