Modeling interfacial tension of the hydrogen-brine system using robust machine learning techniques: Implication for underground hydrogen storage
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- @Article{NG:2022:ijhydene,
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author = "Cuthbert Shang Wui Ng and Hakim Djema and
Menad {Nait Amar} and Ashkan {Jahanbani Ghahfarokhi}",
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title = "Modeling interfacial tension of the hydrogen-brine
system using robust machine learning techniques:
Implication for underground hydrogen storage",
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journal = "International Journal of Hydrogen Energy",
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volume = "47",
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number = "93",
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pages = "39595--39605",
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year = "2022",
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ISSN = "0360-3199",
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keywords = "genetic algorithms, genetic programming, Hydrogen
storage, Hydrogen-brine, Interfacial tension, Machine
learning, Artificial neural network, ANN",
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URL = "https://www.sciencedirect.com/science/article/pii/S0360319922042616",
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DOI = "doi:10.1016/j.ijhydene.2022.09.120",
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size = "11 pages",
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abstract = "During the last years, there has been a surge of
interest in cleaner ways for producing energy in order
to successfully handle the climate issues caused by the
consumption of fossil fuels. The production of hydrogen
(H2) is among the techniques which have grown up as
attractive strategies towards energy transition. In
this context, underground hydrogen storage (UHS) in
saline aquifers has turned into one of the greatest
challenges in the context of conserving energy for
later use. The interfacial tension (IFT) of the
H2-brine system is a paramount parameter which affects
greatly the successful design and implementation of
UHS. In this study, robust machine learning (ML)
techniques, viz., genetic programming (GP), gradient
boosting regressor (GBR), and multilayer perceptron
(MLP) optimized with Levenberg-Marquardt (LMA) and
Adaptive Moment Estimation (Adam) algorithms were
implemented for establishing accurate paradigms to
predict the IFT of the H2-brine system. The obtained
results exhibited that the proposed models and
correlation provide excellent estimations of the IFT.
In addition, it was deduced that MLP-LMA outperforms
the other models and the existing correlation in the
literature. MLP-LMA yielded R2 and AAPRE values of
0.9997 and 0.1907percent, respectively. Lastly, the
trend analysis demonstrated the physical coherence and
tendency of the predictions of MLP-LMA",
- }
Genetic Programming entries for
Cuthbert Shang Wui Ng
Hakim Djema
Menad Nait Amar
Ashkan Jahanbani Ghahfarokhi
Citations