Artificial intelligence enabled carbon capture: A review

A K Priya; Balaji Devarajan; Avinash Alagumalai; Hua Song

doi:10.1016/j.scitotenv.2023.163913

Artificial intelligence enabled carbon capture: A review

Sci Total Environ. 2023 Aug 15:886:163913. doi: 10.1016/j.scitotenv.2023.163913. Epub 2023 May 5.

Authors

A K Priya¹, Balaji Devarajan², Avinash Alagumalai³, Hua Song⁴

Affiliations

¹ Department of Chemical Engineering, KPR Institute of Engineering and Technology, Tamilnadu, India.
² Department of Mechanical Engineering, KPR Institute of Engineering and Technology, Tamilnadu, India.
³ Department of Chemical and Petroleum Engineering, University of Calgary, 2500 University Dr. NW, Calgary, Alberta T2N 1N4, Canada. Electronic address: avinashandromeda@gmail.com.
⁴ Department of Chemical and Petroleum Engineering, University of Calgary, 2500 University Dr. NW, Calgary, Alberta T2N 1N4, Canada. Electronic address: sonh@ucalgary.ca.

PMID: 37150463
DOI: 10.1016/j.scitotenv.2023.163913

Abstract

Carbon capturing is imperative to fight climate change as much carbon emissions are liberated into the atmosphere, leading to adversely negative environmental impacts. Today's world addresses all the issues with the aid of digital technologies like data pooling and artificial intelligence (AI). Accordingly, this study is articulated based on AI-assisted carbon capturing. Techniques including machine learning (ML), deep learning (DL), and hybrid techniques being adopted in carbon capture are discussed. The role of AI tools, frameworks, and mathematical models are also discussed herein. Furthermore, the confluence of AI in carbon capture patent landscape is explored. This study would allow researchers to envision the growth of AI-assisted carbon capture in mitigating climate change and meeting SDG 13 - climate action.

Keywords: AI in carbon capture; Machine learning; Patent landscape; Prediction capability.

Publication types

Review

MeSH terms

Artificial Intelligence*
Machine Learning*