Predicting CO2 and H2 Solubility in Pure Water and Various Aqueous Systems: Implication for CO2–EOR, Carbon Capture and Sequestration, Natural Hydrogen Production and Underground Hydrogen Storage

Energies, Journal Year: 2024, Volume and Issue: 17(22), P. 5723 - 5723

Published: Nov. 15, 2024

The growing energy demand and the need for climate mitigation strategies have spurred interest in application of CO2–enhanced oil recovery (CO2–EOR) carbon capture, utilization, storage (CCUS). Furthermore, natural hydrogen (H2) production underground (UHS) geological media emerged as promising technologies cleaner achieving net–zero emissions. However, selecting a suitable medium is complex, it depends on physicochemical petrophysical characteristics host rock. Solubility key factor affecting above–mentioned processes, critical to understand phase distribution estimating trapping capacities. This paper conducts succinct review predictive techniques present novel simple non–iterative models swift reliable prediction solubility behaviors CO2–brine H2–brine systems under varying conditions pressure, temperature, salinity (T–P–m salts), which are crucial many energy–related applications. proposed predict CO2 + H2O brine containing mixed salts various single salt (Na+, K+, Ca2+, Mg2+, Cl−, SO42−) typical (273.15–523.15 K, 0–71 MPa), well H2 NaCl (273.15–630 0–101 MPa). validated against experimental data, with average absolute errors pure water ranging between 8.19 8.80% 4.03 9.91%, respectively. These results demonstrate that can accurately over wide range while remaining computationally efficient compared traditional models. Importantly, reproduce abrupt variations composition during transitions account influence different ions solubility. capture salting–out (SO) gas types consistent previous studies. simplified presented this study offer significant advantages conventional approaches, including computational efficiency accuracy across conditions. explicit, derivative–continuous nature these eliminates iterative algorithms, making them integration into large–scale multiphase flow simulations. work contributes field by offering tools modeling subsurface environmental–related applications, facilitating their transition aimed at reducing

Language: Английский

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Mutual Diffusivities of Mixtures of Carbon Dioxide and Hydrogen and Their Solubilities in Brine: Insight from Molecular Simulations

Industrial & Engineering Chemistry Research, Journal Year: 2024, Volume and Issue: 63(23), P. 10456 - 10481

Published: May 31, 2024

H2-CO2 mixtures find wide-ranging applications, including their growing significance as synthetic fuels in the transportation industry, relevance capture technologies for carbon and storage, occurrence subsurface storage of hydrogen, hydrogenation dioxide to form hydrocarbons alcohols. Here, we focus on thermodynamic properties pertinent underground hydrogen depleted gas reservoirs. Molecular dynamics simulations are used compute mutual (Fick) diffusivities a wide range pressures (5 50 MPa), temperatures (323.15 423.15 K), mixture compositions (hydrogen mole fraction from 0 1). At 5 MPa, computed agree within 5% with kinetic theory Chapman Enskog at K, albeit exhibiting deviations up 25% between 323.15 373.15 K. Even predictions match 15% comprising over 80% H2 due ideal-gas-like behavior. In higher concentrations CO2, Moggridge correlation emerges dependable substitute theory. Specifically, when CO2 content reaches 50%, achieves 10% Fick diffusivities. Phase equilibria ternary involving CO2-H2-NaCl were explored using Gibbs Ensemble (GE) Continuous Fractional Component Monte Carlo (CFCMC) technique. The solubilities NaCl brine increased fugacity respective component but decreased concentration (salting out effect). While solubility system compared binary CO2-NaCl system, less H2-NaCl system. cooperative effect enhances while suppressing solubility. water phase was found be intermediate systems. Our findings have implications chemical dealing CO2-H2 mixtures, particularly where experimental data lacking, emphasizing need reliable mixtures.

Language: Английский

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Modeling interfacial tension of surfactant–hydrocarbon systems using robust tree-based machine learning algorithms

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: July 5, 2023

Interfacial tension (IFT) between surfactants and hydrocarbon is one of the important parameters in petroleum engineering to have a successful enhanced oil recovery (EOR) operation. Measuring IFT laboratory time-consuming costly. Since, accurate estimation paramount significance, modeling with advanced intelligent techniques has been used as proper alternative recent years. In this study, values were predicted using tree-based machine learning algorithms. Decision tree (DT), extra trees (ET), gradient boosted regression (GBRT) predict parameter. For purpose, 390 experimental data collected from previous studies implement models. Temperature, normal alkane molecular weight, surfactant concentration, hydrophilic-lipophilic balance (HLB), phase inversion temperature (PIT) selected inputs models independent variables. Also, solution alkanes was output dependent variable. Moreover, implemented evaluated statistical analyses applied graphical methods. The results showed that DT, ET, GBRT could average absolute relative error 4.12%, 3.52%, 2.71%, respectively. R-squared all implementation higher than 0.98, for best model, GBRT, it 0.9939. Furthermore, sensitivity analysis Pearson approach utilized detect correlation coefficients input parameters. Based on technique, demonstrated PIT, HLB had greatest effect IFT, Finally, statistically credited by Leverage approach.

Language: Английский

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Predictive modeling of CO2 solubility in piperazine aqueous solutions using boosting algorithms for carbon capture goals

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Sept. 27, 2024

Language: Английский

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Estimation of hydrogen solubility in aqueous solutions using machine learning techniques for hydrogen storage in deep saline aquifers

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Oct. 29, 2024

The porous underground structures have recently attracted researchers' attention for hydrogen gas storage due to their high capacity. One of the challenges in storing aqueous solutions is estimating its solubility water. In this study, after collecting experimental data from previous research and eliminating four outliers, nine machine learning methods were developed estimate To optimize parameters used model construction, a Bayesian optimization algorithm was employed. By examining error functions plots, LSBoost method with R² = 0.9997 RMSE 4.18E-03 identified as most accurate method. Additionally, artificial neural network, CatBoost, Extra trees, Gaussian process regression, bagged regression support vector machines, linear had values 0.9925, 0.9907, 0.9906, 0.9867, 0.9866, 0.9808, 0.9464, 0.7682 2.13E-02, 2.43E-02, 2.44E-02, 2.83E-02, 2.85E-02, 3.40E-02, 5.68E-02, 1.18E-01, respectively. Subsequently, residual plots generated, indicating performance across all ranges. maximum - 0.0252, only 4 points estimated an greater than ± 0.01. A kernel density estimation (KDE) plot errors showed no specific bias models except model. investigate impact temperature, pressure, salinity on outputs, Pearson correlation coefficients calculated, showing that 0.8188, 0.1008, 0.5506, respectively, pressure strongest direct relationship, while inverse relationship solubility. Considering results research, method, alongside approaches like state equations, can be applied real-world scenarios storage. findings study help better understanding solutions, aiding systems.

Language: Английский

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