A thermodynamic analysis of the solubility of gases in water at high pressures and supercritical temperatures

Vul'fson, A. N.; Borodin, O. O.

doi:10.1134/s0036024407040024

Cited by 6 publications

(8 citation statements)

References 3 publications

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“…There are several thermodynamic models which uses the Henry's constant, activity coefficient and cubic equations of state to obtain more information about the equilibrium conditions. The changes of Henry's constant for the pressure lower than 5 MPa are negligible and it is dominantly effected by temperatures [19]. The high dependency on temperature is obvious at low temperature and also the nonlinear decreasing relationship is observed at high temperatures [25].…”

Section: Introductionmentioning

confidence: 93%

“…This databank contains total number of 1175 solubility points for hydrocarbons (881 and 294 points for training and testing phases respectively.). According to the literature [1,4,5,9,[16][17][18][19][20], the solubility of gases in these systems is highly function of aqueous solutions, pressure, temperature and gaseous phase composition. The aqueous phase composition was change into ionic strength (I) from salt concentrations to reduce dimensions of modeling process.…”

Section: Experimental Dataset Collectionmentioning

confidence: 99%

“…In the literature, there are different solubility datasets for various gas-liquid systems. These datasets mostly include hydrocarbons' dissolution in water/brine systems [1,4,5,9,[16][17][18][19][20] and non-hydrocarbons such as CO2 and N2 dissolution in water/brine systems [7, 12-14, 18, 21-24]. A brief summary of the hydrocarbon systems datasets is shown in Table 1 for hydrocarbons.…”

Section: Introductionmentioning

confidence: 99%

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Extreme Learning Machine-Based Model for Solubility Estimation of Hydrocarbon Gases in Electrolyte Solutions

Nabipour¹,

Mosavi²,

Baghban³

et al. 2020

Preprint

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Calculating hydrocarbon components solubility of natural gases is known as one of the important issues for operational works in petroleum and chemical engineering. In this work, a novel solubility estimation tool has been proposed for hydrocarbon gases including methane, ethane, propane and butane in aqueous electrolyte solutions based on extreme learning machine (ELM) algorithm. Comparing the ELM outputs with a comprehensive real databank which has 1175 solubility points concluded to R-squared values of 0.985 and 0.987 for training and testing phases respectively. Furthermore, the visual comparison of estimated and actual hydrocarbon solubility leaded to confirm the ability of proposed solubility model. Additionally, sensitivity analysis has been employed on the input variables of model to identify their impacts on hydrocarbon solubility. Such a comprehensive and reliable study can help engineers and scientists to successfully determine the important thermodynamic properties which are key factors in optimizing and designing different industrial units such as refineries and petrochemical plants.

show abstract

Section: Introductionmentioning

confidence: 93%

Section: Experimental Dataset Collectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Extreme Learning Machine-Based Model for Solubility Estimation of Hydrocarbon Gases in Electrolyte Solutions

Nabipour¹,

Mosavi²,

Baghban³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…There are several thermodynamic models which uses the Henry's constant, activity coefficient, and cubic equations of state to obtain more information about the equilibrium conditions. The changes of Henry's constant for the pressure lower than 5 MPa are negligible and it is dominantly affected by temperatures [19]. The high dependency on temperature is obvious at low temperature and also the nonlinear decreasing relationship is observed at high temperatures [25].…”

Section: Introductionmentioning

confidence: 93%

“…In the literature, there are different solubility datasets for various gas-liquid systems. These datasets mostly include hydrocarbons' dissolution in water/brine systems [1,4,5,9,[16][17][18][19][20] and non-hydrocarbons such as CO 2 and N 2 dissolution in water/brine systems [7,[12][13][14]18,[21][22][23][24]. A brief summary of the hydrocarbon systems datasets is shown in Table 1 for hydrocarbons.…”

Section: Introductionmentioning

confidence: 99%

Extreme Learning Machine-Based Model for Solubility Estimation of Hydrocarbon Gases in Electrolyte Solutions

et al. 2020

View full text Add to dashboard Cite

Calculating hydrocarbon components solubility of natural gases is known as one of the important issues for operational works in petroleum and chemical engineering. In this work, a novel solubility estimation tool has been proposed for hydrocarbon gases—including methane, ethane, propane, and butane—in aqueous electrolyte solutions based on extreme learning machine (ELM) algorithm. Comparing the ELM outputs with a comprehensive real databank which has 1175 solubility points yielded R-squared values of 0.985 and 0.987 for training and testing phases respectively. Furthermore, the visual comparison of estimated and actual hydrocarbon solubility led to confirm the ability of proposed solubility model. Additionally, sensitivity analysis has been employed on the input variables of model to identify their impacts on hydrocarbon solubility. Such a comprehensive and reliable study can help engineers and scientists to successfully determine the important thermodynamic properties, which are key factors in optimizing and designing different industrial units such as refineries and petrochemical plants.

show abstract

Solubility of hydrocarbon and non-hydrocarbon gases in aqueous electrolyte solutions: A reliable computational strategy

Samani

Miforughy

Safari³

et al. 2019

Fuel

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A thermodynamic analysis of the solubility of gases in water at high pressures and supercritical temperatures

Cited by 6 publications

References 3 publications

Extreme Learning Machine-Based Model for Solubility Estimation of Hydrocarbon Gases in Electrolyte Solutions

Extreme Learning Machine-Based Model for Solubility Estimation of Hydrocarbon Gases in Electrolyte Solutions

Extreme Learning Machine-Based Model for Solubility Estimation of Hydrocarbon Gases in Electrolyte Solutions

Solubility of hydrocarbon and non-hydrocarbon gases in aqueous electrolyte solutions: A reliable computational strategy

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