Enhanced formation of methane hydrates via graphene oxide: Machine learning insights from molecular dynamics simulations

Yanwen Lin, Yongchao Hao, Qiao Shi, Yihua Xu, Zixuan Song, Ziyue Zhou, Yuequn Fu, Zhisen Zhang and Jianyang Wu

Energy, 2024, vol. 289, issue C

Abstract: Gas hydrates, with their significant applications in energy and environment sectors, have emerged as promising technologies for gas separation, natural gas and energy storage. The formation of methane hydrates occurs in substrate-contact conditions, where the properties of the substrate surfaces play a crucial role in determining hydrate formation pathways. Herein, the influence of the hydroxyl group (-OH) content on the graphene surface on methane hydrate formation in confined systems using molecular dynamics (MD) simulations with machine learning (ML) technique. The MD results show that increasing the oxidation degree effectively enhances the formation of methane hydrate within the oxidation degree of 10 %–40 %. This enhancement is attributed to the hydrophilicity of the (GO) surfaces. When the oxidation degree reaches 50 %, however, the formation rate of methane hydrates slows down compared to the GO-40 system. This deceleration can be attributed to the formation of dense water layers that adhere to the GO surfaces, resulting in impeding the hydrate formation process. Markov State Model (MSM) is employed to analyze the induction time of GO on methane hydrate formation, highlighting the important role of GO in the formation of methane hydrates. Additionally, an eXtreme Gradient Boosting (XGboost) ML model is developed to predict the formation behaviors of methane hydrates. Ternary water-ring aggregations (TWRAs) are adopted as indicators to understand to the formation behaviors, and the XGboost ML model facilitate the identification of key TWRAs associated with the formation process. This work provides molecular-level insights into the influence of –OH group on formation of methane hydrates, and offers a predictive framework for understanding and characterizing the formation clathrate hydrates in confined systems.

Keywords: Methane hydrate; Graphene oxide; Molecular dynamic simulations; Markov model; Machine learning (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:289:y:2024:i:c:s0360544223034746

DOI: 10.1016/j.energy.2023.130080

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