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New-generation machine learning models as prediction tools for modeling interfacial tension of hydrogen-brine system

journal contribution
posted on 2023-10-09, 14:57 authored by Afeez Gbadamosi, Haruna Adamu, Jamilu Usman, A.G. Usman, Mahmud M. Jibril, Babatunde SalamiBabatunde Salami, Saheed Lekan Gbadamosi, Lukumon O. Oyedele, S.I. Abba

Recently, hydrogen (H2) gas has gained prodigious attention as a sustainable energy carrier to reduce acute dependence on fossil fuels due to its fascinating properties. To ensure it continuous availability, hydrogen storage in underground geologic formations has been proffered. Nonetheless, H2 storage in underground formations is dependent on fluid-fluid interfacial tension (IFT). Herein, new-generation machine learning models namely Gaussian Process Regression (GPR), the Elman Neural Network (ENN), and the Logistic Regression (LR) were used to predict the IFT of the H2-brine system. For this purpose, the includes temperature (T), pressure (p), and density difference (Δρ), with the surface tension (γ) as the output variable. The effectiveness of each model was assessed through a variety of metrics including the Nash-Sutcliffe efficiency (NSE), the Mean Absolute Error (MAE), the Mean Absolute Percentage Error (MAPE), the Correlation Coefficient (PCC), the Root Mean Square Error (RMSE), and BIAS. Moreover, the limitations of traditional chemometrics feature extraction was overcome by utilizing an original linear matrix input-output (M1-M3) feature extraction approach. The result generated demonstrates that the suggested models and correlation offer sterling IFT estimations. The Gaussian Process Regression (GPR) model outperformed the other evaluated machine learning methods. Particularly, the GPR-M2 model combination showed extraordinary effectiveness, outperforming the BTA-M1 model, which had the lowest performance by 22%. Numerical comparison indicated that GPR-M2 with MAPE = 0.0512, and MAE = 0.002 emerged as the best reliable model. This study extends the frontier of knowledge in achieving carbon-free and sustainable energy society via accurate IFT prediction of H2-brine system.

History

Publisher

Elsevier

Version

  • AM (Accepted Manuscript)

Citation

Gbadamosi, A., Adamu, H., Usman, J., Usman, A.G., Jibril, M.M., Salami, B.A., Gbadamosi, S.L., Oyedele, L.O. and Abba, S.I. (2023) 'New-generation machine learning models as prediction tools for modeling interfacial tension of hydrogen-brine system', International Journal of Hydrogen Energy. https://doi.org/10.1016/j.ijhydene.2023.09.170

Print ISSN

0360-3199

Cardiff Met Affiliation

  • Cardiff School of Management

Cardiff Met Authors

Babatunde Salami

Cardiff Met Research Centre/Group

  • Welsh Centre for Business and Management Research

Copyright Holder

  • © The Publisher

Language

  • en

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