Exploring the Application of Machine Learning in Carbon Capture, Utilization, and Storage Technologies
Bo Zhang （ Shanghai University of Electric Power, School of Economics and Management;CAS Key Laboratory of Low-Carbon Conversion Science & Engineering, Shanghai Advanced Research Institute, Chinese Academy of Sciences ）
Chuan Zhang （ Shanghai University of Electric Power, School of Economics and Management, Shanghai, China, 201306. ）
https://doi.org/10.37155/2972-483X-SI-2This Article Belongs To The Special Issue: Interdisciplinary Approaches to AI Bridging Fields with Machine Learning
Abstract
Carbon capture, utilization, and storage (CCUS) technologies are key solutions to mitigating climate change. In recent years, with the advancement of technology, the application of machine learning (ML) in optimizing various stages of CCUS technologies has garnered increasing attention. This paper summarizes the current status of ML applications in CO₂ capture, CO₂ enhanced oil recovery, CO₂ storage, underground sequestration, as well as in the evaluation of the CCUS technologies chain and source-sink matching. It explores the use of ML theory to optimize processes such as data collection, model prediction and recognition, model parameter adjustment, and result comparison in relation to CCUS technologies. Additionally, a full-chain model for CCUS technologies is constructed, and the future directions of ML in CCUS are envisioned. The research provides insights to fully harness the potential of machine learning in the CCUS field.
Keywords
Carbon Capture Utilization and Storage (CCUS) technologies; Machine learningFull Text
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Copyright © 2025 Bo Zhang, Chuan Zhang
Publishing time:2025-02-10
This work is licensed under a Creative Commons Attribution 4.0 International License
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