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Fast and Accurate Calculation on Competitive Adsorption Behavior in Shale Nanopores by Machine Learning Model

Hao Yu1,*, Mengcheng Huang1

1 Department of Modern Mechanics, University of Science and Technology of China, Hefei, 230027, China

* Corresponding Author: Hao Yu. Email: email

The International Conference on Computational & Experimental Engineering and Sciences 2024, 30(2), 1-1. https://doi.org/10.32604/icces.2024.011120

Abstract

Understanding the competitive adsorption behavior of CO2 and CH4 in shale nanopores is crucial for enhancing the recovery of shale gas and sequestration of CO2, which is determined by both the inherent characteristics of the molecules and external environmental factors such as pore size, temperature, and partial pressures of CO2 and CH4. While the competitive adsorption behavior of CO2/CH4 has been analyzed by previous studies, a comprehensive understanding from the perspective of molecular kinetic theory and the efficient calculation for competitive adsorption behavior considering various geological situations is still challenging, limited by the huge computation cost of classical molecular dynamics (MD) methods. In this work [1], the theoretical connection between inherent characteristics of molecules and adsorption behavior is firstly built to reveal the general laws in the behavior of CO2/CH4 competitive adsorption through posture analysis of the molecules. A machine learning algorithm, aided by molecular kinetic theory, is proposed to facilitate the fast and accurate predictions of competitive adsorption behavior, and detailed analyses of the influencing factors are conducted accordingly. The insights gained from this work provide a foundation for expeditiously optimizing the competitive adsorption behavior of CO2/CH4, with potential implications for CO2 sequestration and enhanced gas recovery (CSEGR) process.

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Cite This Article

APA Style
Yu, H., Huang, M. (2024). Fast and accurate calculation on competitive adsorption behavior in shale nanopores by machine learning model. The International Conference on Computational & Experimental Engineering and Sciences, 30(2), 1-1. https://doi.org/10.32604/icces.2024.011120
Vancouver Style
Yu H, Huang M. Fast and accurate calculation on competitive adsorption behavior in shale nanopores by machine learning model. Int Conf Comput Exp Eng Sciences . 2024;30(2):1-1 https://doi.org/10.32604/icces.2024.011120
IEEE Style
H. Yu and M. Huang, “Fast and Accurate Calculation on Competitive Adsorption Behavior in Shale Nanopores by Machine Learning Model,” Int. Conf. Comput. Exp. Eng. Sciences , vol. 30, no. 2, pp. 1-1, 2024. https://doi.org/10.32604/icces.2024.011120



cc Copyright © 2024 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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