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A systematic review on the application of machine learning in carbon dioxide absorption in amine-related solvents

  • Jun Hui Law , Farihahusnah Hussin , Muhammed Basheer Jasser and Mohamed Kheireddine Aroua EMAIL logo
Published/Copyright: January 29, 2025
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Abstract

Amine absorption has been regarded as an efficient solution in reducing the atmospheric carbon dioxide (CO2) concentration. Machine learning (ML) models are applied in the CO2 capture field to predict the CO2 solubility in amine solvents. Although there are other similar reviews, this systematic review presents a more comprehensive review on the ML models and their training algorithms applied to predict CO2 solubility in amine-related solvents in the past 10 years. A total of 55 articles are collected from Scopus, ScienceDirect and Web of Science following Preferred Reporting Items for Systematic Review and Meta-Analyses guidelines. Neural network is the most frequently applied model while committee machine intelligence system is the most accurate model. However, relatively the same optimisation algorithm was applied for each type of ML models. Genetic algorithm has been applied in most of the discussed ML models, yet limited studies were found. The advantages and limitations of each ML models are discussed. The findings of this review could provide a database of the data points for future research, as well as provide information to future researchers for studying ML application in amine absorption, including but not limited to implementation of different optimisation algorithms, structure optimisation and larger scale applications.


Corresponding author: Mohamed Kheireddine Aroua, Research Centre for Carbon Dioxide Capture and Utilisation (CCDCU), School of Engineering and Technology, Sunway University, No. 5 Jalan Universiti, Bandar Sunway, 47500, Petaling Jaya, Selangor, Malaysia; and School of Engineering, Lancaster University, Lancaster, LA1 4YW, UK, E-mail:

Funding source: PGR Ph.D. Studentship Scheme

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission. Jun Hui Law – Writing: Original Draft, Resources, Conceptualization. Farihahusnah Hussin – Writing: Review & Editing. Muhammed Basheer Jasser – Supervision. Mohamed Kheireddine Aroua – Supervision.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: The work is supported by Sunway University under the PGR Ph.D. Studentship Scheme.

  7. Data availability: The data collected from literature is included in the supporting information.

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Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/revce-2024-0047).


Received: 2024-06-25
Accepted: 2024-12-08
Published Online: 2025-01-29
Published in Print: 2025-02-25

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