2022
DOI: 10.1016/j.ijhydene.2022.08.288
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Prediction of hydrogen solubility in aqueous solutions: Comparison of equations of state and advanced machine learning-metaheuristic approaches

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Cited by 37 publications
(11 citation statements)
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“…In Table S13 of the Supporting Information , we provide an extensive database for solubilities of H 2 at temperatures of (298 to 363) K, H 2 partial pressures of (1 to 1000) bar, and at NaCl molalities of (0 to 6) mol/kg H 2 O. The solubilities of H 2 at partial pressures up to 100 bar are computed for a wider temperature range, i.e., (298 to 523) K. These data can be further used to test and train existing machine-learning models 33 or equations of state 148 for predicting H 2 solubilities in saline solutions at conditions relevant to geological H 2 storage.…”
Section: Resultsmentioning
confidence: 99%
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“…In Table S13 of the Supporting Information , we provide an extensive database for solubilities of H 2 at temperatures of (298 to 363) K, H 2 partial pressures of (1 to 1000) bar, and at NaCl molalities of (0 to 6) mol/kg H 2 O. The solubilities of H 2 at partial pressures up to 100 bar are computed for a wider temperature range, i.e., (298 to 523) K. These data can be further used to test and train existing machine-learning models 33 or equations of state 148 for predicting H 2 solubilities in saline solutions at conditions relevant to geological H 2 storage.…”
Section: Resultsmentioning
confidence: 99%
“…In Table S13 of the Supporting Information, we provide an extensive database for solubilities of H 2 at temperatures of (298 to 363) K, H 2 partial pressures of (1 to 1000) bar, and at NaCl molalities of (0 to 6) mol/kg H 2 O. The solubilities of H 2 at partial pressures up to 100 bar are computed for a wider temperature range, i.e., (298 to 523) K. These data can be further used to test and train existing machine-learning models 33 or equations of state 148 for predicting H 2 solubilities 32 and the dotted lines represent the experimental correlation results of Chabab et al 31 The experimental data of Chabab et al, 31 and Tori ́n-Ollarves and Trusler, 32 and the simulation results of Lopez-Lazaro et al 74 (converted from Henry constants) are also shown. The error bars of the simulations of this work are comparable (Figure 5a) or smaller than (Figure 5b in saline solutions at conditions relevant to geological H 2 storage.…”
Section: T H I S C O N T E N T Imentioning
confidence: 99%
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“…Here, X represents the matrix of explanatory variables, and T represents the transpose matrix operator. Moreover, the critical Leverage ( H* ) is calculated according to the following formula 111 : …”
Section: Resultsmentioning
confidence: 99%
“…Hence, in order to confirm the parameters affecting the adsorption of asphaltenes on magnetite, hematite, calcite, and dolomite NPs, which was discussed in section "Two-phase asphaltene adsorption experiments", the relevancy factor (r) was used. This special function is used to check the parameters that affect the target response 49,50 . Here, we evaluated the influence of parameters such as asphaltene heteroatoms, asphaltene aromatic nature, average asphaltene particle size, asphaltene polarity, and the initial concentration of asphaltene/toluene solution on the target response (adsorption of asphaltene on magnetite, hematite, calcite, and where IN i,j and IN m,i show the jth value and mean value of the ith input parameter, respectively, where i could be heteroatoms, aromatic nature, average asphaltene particle size, polarity, and initial concentration of asphaltene/ toluene solution; Q m denotes the mean value of adsorption of asphaltenes on magnetite, hematite, calcite, and dolomite NPs and Q j is the jth value of asphaltenes adsorption onto the calcite, dolomite, magnetite and hematite NPs.…”
Section: Sensitivity Analysismentioning
confidence: 99%