Machine Learning in Carbon Capture, Utilization, Storage, and Transportation: A Review of Applications in Greenhouse Gas Emissions Reduction
Problem
CCUS processes face technical and economic challenges in optimization and scalability that hinder reducing greenhouse gas emissions.
Why it matters
CCUS scalability is critical for climate change mitigation and industrial decarbonization.
What the AI did
The paper reviews machine learning applications across CO2 capture, transport, storage and utilization, evaluating ML techniques and optimization objectives; it is a synthesis, not a deployed solution.
Results
not stated in source
Impact
The evidence supports only that ML is claimed to offer faster, more accurate alternatives across the CCUS value chain.
Record
| Venue | Processes |
|---|---|
| Published | 2025 |
| Work type | article |
| Authors | Xuejia Du, Muhammad Noman Khan, Ganesh C. Thakur |
| Citations | 25 (OpenAlex, at import time) |
| References | 100 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3390/pr13041160 |
| Open access | gold |
| Keywords | machine learning, CCUS, CO2 capture, greenhouse gas emissions, optimization, review |
Importance rating
major — millions of lives, an industry or a field
Abstract states CCUS is indispensable for reducing greenhouse gas emissions and combating climate change, a global-scale problem; the specific gap addressed is fragmented ML applications across the CCUS value chain.
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