AI-Driven Carbon Capture Sequestration Technologies (CCST): Advancements, Limitations and Pathways for a Sustainable Future

DOI

10.2991/978-94-6239-750-7_78How to use a DOI?

Keywords

Sequestration; Recurrent neural networks; Machine Learning; Carbon capture

Abstract

Carbon, Capture and Sequestration (CCS) has been a heated topic for the sustainable energy transition. As coal continues to dominate as an abundant resource, on the other hand, amid escalating climate issues, it is now essential to switch to other renewable energy sources to meet power needs. This paper objectifies and identifies the current advancements that are being used in CCS along with the uncertainties and barriers that delocalize the arc of future and current outlook with Artificial Intelligence (AI). The review study shows that reinforcement learning, Convolutional Neural Networks (CNN) as well as Recurrent Neural Networks (RNN) plays a pivotal role in the analytical scaling of the technologies used for Carbon Sequestration. The integration of AI and Machine Learning (ML) algorithms with the mechanical framework enhances the efficiency, precision and allows the Model of Frameworks (MOFs) to prevent any kind of leakages and provide a sustainable-long term safeguard in the storage. In the modern crisis of scarcity of fossil fuels, the AI clubbing in CCS develops a reliable and more accurate infrastructure for a sustainable future outlook of carbon.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Pushkar Nagar
AU  - Kajal Agrahari
AU  - Aanvi Mor
AU  - Paras Mor
AU  - Neelam Baghel
PY  - 2026
DA  - 2026/08/31
TI  - AI-Driven Carbon Capture Sequestration Technologies (CCST): Advancements, Limitations and Pathways for a Sustainable Future
BT  - Proceedings of the International Conference on Advanced Design, Manufacturing, and Sustainable Energy Systems (ICADMSES 2026)
PB  - Atlantis Press
SP  - 1085
EP  - 1095
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6239-750-7_78
DO  - 10.2991/978-94-6239-750-7_78
ID  - Nagar2026
ER  -