Application of Machine Learning in CO₂ Capture Technology

Dear Colleagues,

Global warming has led to more and more events like melting sea ice, rising sea levels, and extreme weather patterns on Earth over the last couple of decades. In order to reduce emissions and achieve green economies, carbon capture science and technologies are crucial. It will require ambitious and innovative initiatives from both the fundamental and applied research communities to mitigate CO₂ emissions and their devastating impacts on the climate.

In recent years, the growth of computing power has made machine learning (ML) increasingly useful for a wide range of applications. In terms of accuracy and robustness, ML has proved to be an excellent approach for uncovering hidden connections between input features that aren’t easily identifiable and also offers lower-cost alternatives to computing. In addition, one of today's challenges is generating useful analyses from collected data, which can lead to useful decisions. Because all decisions must take into account the economy's viability as well as the environment. To overcome this challenge, machine learning methods could be simply applied to predict, classify, or make decisions.   

We invite leading scientists and technology researchers to share their latest research in this Special Issue, which will advance the field and speed up the development and deployment of these essential technologies.

Original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

• Advances in CO₂ capture technologies for environmental protection;
• Advance in machine learning for CO₂ capture and sequestration;
• Advance in machine learning for clean energy production;
• Assessing the economics of CO₂ capture technology for environmental protection/remediation;
• Advances in CO2 capture technologies for gas separation and waste gas treatment;
• Advances in CO₂ capture technologies for oil and gas industries;

We look forward to receiving your contributions.

Dr. Babak Aghel
Dr. Ehsan Heidaryan
Dr. Mostafa Safdari Shadloo
Guest Editors

Manuscript Submission Information

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• CO₂ capture and sequestration
• CO₂ utilization
• CO₂ reduction
• techno-economic assessment
• lifecycle analysis
• machine learning
• data analytics