Modeling the thermal transport properties of hydrogen and its mixtures with greenhouse gas impurities: A data-driven machine learning approach

Vo Thanh, Hung; Rahimi, Mohammad; Tangparitkul, Suparit; Promsuk, Natthanan

doi:10.1016/j.ijhydene.2024.08.100

International Journal of Hydrogen Energy

2024

DOI: 10.1016/j.ijhydene.2024.08.100

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Modeling the thermal transport properties of hydrogen and its mixtures with greenhouse gas impurities: A data-driven machine learning approach

Hung Vo Thanh,

Mohammad Rahimi,

Suparit Tangparitkul

et al.

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Cited by 3 publications

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Study and prediction of photocurrent density with external validation using machine learning models

Sahu,

Azad,

Kumar

2024

International Journal of Hydrogen Energy

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Study and prediction of photocurrent density with external validation using machine learning models

Sahu,

Azad,

Kumar

2024

International Journal of Hydrogen Energy

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Optimizing Methane Uptake on N/O Functionalized Graphene via DFT, Machine Learning, and Uniform Manifold Approximation and Projection (UMAP) Techniques

Rahimi,

Mehrpanah,

Mouchani

et al. 2024

Ind. Eng. Chem. Res.

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Carbon materials possess active sites and functionalities on the surface that can attract prominent interest as solid adsorbents for diverse gas adsorption. This study aimed to predict the optimized methane uptake, adsorption energy (E ad ), and adsorbent rediscovery through multitechniques of neural, regression, classifier ML-DFT, and Uniform Manifold Approximation and Projection (UMAP). Nitrogen and oxygen (N/O) functionalities and graphene, graphene oxide (GO), and N-doped GO were applied to the methane storage medium. Multi-ML algorithms were employed for the adsorption energy of CH 4 uptake on (i) N/O functionalities such as pyridinic (N-py), carboxyl (O−II), oxidized (N-x), hydroxyl (O-h), Nitroso (N-ni), and Amine (primary, secondary, and tertiary). (ii) The graphene surfaces are decorated with N/O heteroatoms to construct graphene oxide (GO) and N-doped GO. The DFT calculations were applied by PW91 and the Dmol 3 package. N/O-functionalities in the distance of ∼2.0 to 3.1 Å groups obtained E ad of approximately −2.0 to −4 eV. Further, ML models accomplished the forthcoming rediscovery of CH 4 physisorption by using the multiadsorptive features of optimized adsorbents with an R 2 of 0.99. ML-derived sensitivity analysis approach was applied to specifications such as deformation adsorption energy, N/O functionality type, and optimized structure. CH 4 adsorption specifications indicate sensitivity levels of −0.03 to 0.02 eV. The synergetic DFT/ML approaches distinguished the modeled and rediscovered phases of CH 4 adsorption on N/O functional groups and graphene structures. UMAP is employed as a new adsorbent screening approach to play a complementary role in the ML modeling process.

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Machine-learning based prediction of hydrogen/methane mixture solubility in brine

Altalbawy,

Al-saray,

Vaghela

et al. 2024

Sci Rep

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With regard to underground hydrogen storage projects, presuming that the hydrogen storage site has served as a repository for methane, the coexistence of a blend of methane and hydrogen is anticipated during the incipient stage of hydrogen storage. Therefore, the solubility of hydrogen/methane mixtures in brine becomes imperative. On the contrary, laboratory tasks of such measurements are hard because of its extreme corrosion ability and flammability, hence modeling methodologies are highly preferred. Therefore, in this study, we seek to create accurate data-driven intelligent models based upon laboratory data using hybrid models of adaptive neuro-fuzzy inference system (ANFIS) and least squares support vector machine (LSSVM) optimized with either particle swarm optimization (PSO), genetic algorithm (GA) and coupled simulated annealing (CSA) to predict hydrogen/methane mixture solubility in brine as a function of pressure, temperature, hydrogen mole fraction in hydrogen/methane mixture and brine salt concentration. The results indicate that almost all the gathered experimental data are technically suitable for the model development. The sensitivity study shows that pressure and hydrogen mole fraction in the mixture are strongly related with the solubility data with direct and indirect effects, respectively. The analyses of evaluation indexes and graphical methods indicates that the developed LSSVM-GA and LSSVM-CSA models are the most accurate as they exhibit the lowest AARE% and MSE values and the highest R-squared values. These findings show that machine learning methods could be a useful tool for predicting hydrogen solubility in brine encountered in underground hydrogen storage projects, aiding in the advancement of intelligent, affordable, and secure hydrogen storage technologies.

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Modeling the thermal transport properties of hydrogen and its mixtures with greenhouse gas impurities: A data-driven machine learning approach

Cited by 3 publications

References 59 publications

Study and prediction of photocurrent density with external validation using machine learning models

Study and prediction of photocurrent density with external validation using machine learning models

Optimizing Methane Uptake on N/O Functionalized Graphene via DFT, Machine Learning, and Uniform Manifold Approximation and Projection (UMAP) Techniques

Machine-learning based prediction of hydrogen/methane mixture solubility in brine

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