Leveraging Machine Learning and Artificial Intelligence for Enhanced Carbon Capture and Storage (CCS)
Jai Krishna Sahith Sayani, Bhajan Lal
Abstract
The global community is confronting an unprecedented challenge—climate change—propelled by the relentless emission of greenhouse gases into the Earth's atmosphere. The urgency of mitigating the adverse impacts of climate change has driven the quest for innovative solutions to reduce and ultimately eliminate these emissions. By capturing CO2 emissions at the source and securely storing them underground, CCS has the potential to significantly reduce the concentration of greenhouse gases in the atmosphere. This chapter explores the burgeoning field of applying ML and AI to CCS, delving into the myriad ways in which these technologies are revolutionizing the landscape of carbon capture and storage. We will elucidate their application in improving the design and operation of CCS facilities, ensuring secure and leak-free storage, and optimizing the overall CCS lifecycle [5]. Through a comprehensive review of existing studies and a critical assessment of the state of the art, we aim to shed light on the immense possibilities and ongoing challenges of marrying ML and AI with CCS technology. To navigate this fascinating realm, we will embark on a journey that explores the data-driven insights and predictive models that can significantly enhance CCS systems. Additionally, we will consider the ethical and regulatory aspects associated with the integration of advanced technologies in the energy and environmental sectors.