Experimental determination of hydrate dissociation conditions in CO2 + hexane + dodecane system, + 1-propanol or + tetra-n-butyl ammonium bromide

Chima-Maceda, José M.; Pimentel-Rodas, Alfredo; Galicia‐Luna, Luis A.; Notario-López, Angel M.

doi:10.1016/j.jct.2021.106419

Cited by 5 publications

(2 citation statements)

References 27 publications

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“…In our previous work, 23 hydrate equilibrium dissociation conditions of CO 2 have been measured to verify the accuracy of the test method. Our experimental data are consistent with the data in the literature (Chima-Maceda et al 24 and Mu et al 25 ), as shown in Figure 2.…”

Section: Resultsmentioning

confidence: 99%

Effects of 2,5-Dihydrofuran on Hydrate Equilibrium Dissociation Conditions of CO₂

Zhang,

Sun

2023

J. Chem. Eng. Data

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Hydrate has advantages in the field of CO2 capture and sequestration, cold storage, etc. But CO2 hydrate formation needs very strict conditions, such as high pressure or low temperature conditions. 2,5-Dihydrofuran is used as a thermodynamic promoter to improve CO2 hydrate equilibrium conditions. At any temperature, the experimental results show that the equilibrium pressure of CO2 + 2,5-dihydrofuran + H2O is much lower than that of CO2 in pure water. The CO2 + 2,5-dihydrofuran + H2O system forms a structure II hydrate, while CO2 forms a structure I hydrate in pure water. With more addition of 2,5-dihydrofuran (>10 wt %), the reduction of hydrate formation pressure decreases at any experimental temperatures. At lower pressures, 2,5-dihydrofuran exhibits the properties of a water-soluble thermodynamic promoter, while at higher pressures, it shows the properties of an insoluble thermodynamic promoter.

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Section: Resultsmentioning

confidence: 99%

Effects of 2,5-Dihydrofuran on Hydrate Equilibrium Dissociation Conditions of CO₂

Zhang,

Sun

2023

J. Chem. Eng. Data

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“…In this study, ML models were developed to accurately predict the phase equilibria of the CO 2 hydrate, particularly in systems containing thermodynamic inhibitors. To construct these models, a comprehensive data set comprising 1801 experimentally measured data points was compiled from 85 references − . This dataset covered not only pure CO 2 hydrate but also systems incorporating 14 different types of salts (NaCl, KCl, CaCl 2 , MgCl 2 , NaBr, KBr, CaBr 2 , NaF, Na 2 SO 4 , MgSO 4 , NaHCO 3 , SrCl 2 , HCOONa, and HCOOK) and 9 organic inhibitors: methanol (MeOH), ethanol (EtOH), ethylene glycol (MEG), diethylene glycol (DEG), triethylene glycol (TEG), glycerol, 1-propanol, 2-propanol, and tetrabutyl alcohol (TBA).…”

Section: Methodsmentioning

confidence: 99%

Predicting Phase Equilibria of CO₂ Hydrate in Complex Systems Containing Salts and Organic Inhibitors for CO₂ Storage: A Machine Learning Approach

Mok,

Go,

Seo

2024

Energy Fuels

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In this study, 13 machine learning (ML) models were employed to predict the phase equilibrium temperatures of the CO2 hydrate in systems with salts and organic inhibitors: Multiple Linear Regression (MLR), Support Vector Regression (SVR), k-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), Decision Tree (DT), Random Forest (RF), Extra Trees (ET), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Gradient Boosting Machine (GBM), Light Gradient Boosting Machine (LGBM), Histogram-Based Gradient Boosting (HistGB), and eXtreme Gradient Boosting (XGBoost). A dataset consisting of 1801 experimentally measured equilibrium data points was gathered, which included both pure water systems and systems with thermodynamic inhibitors. After data preprocessing, 1402 data points were selected for ML training and validation. Boosting algorithms generally yielded high predictive accuracy, with the MLP demonstrating notably superior accuracy. The predicted equilibrium temperatures for complex systems containing both salts and organic inhibitors were also compared with those calculated by the widely used CSMGem software. With the exception of SVR, KNN, and DT, all models outperformed CSMGem, in terms of statistical assessment. Specifically, the CatBoost model accurately predicted equilibrium temperatures for most test sets of combinations of salts and organic inhibitors. This study underscores the viability of ML models for predicting the phase equilibria of the CO2 hydrate in the presence of single and mixed thermodynamic inhibitors.

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Study on the effect of TBAB and THF on the hydrate dissociation conditions for H2O + CO2 + C6H14 + C14H30 systems

Díaz-Ramírez

Pimentel-Rodas

Galicia‐Luna

et al. 2022

The Journal of Chemical Thermodynamics

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Experimental determination of hydrate dissociation conditions in CO2 + hexane + dodecane system, + 1-propanol or + tetra-n-butyl ammonium bromide

Cited by 5 publications

References 27 publications

Effects of 2,5-Dihydrofuran on Hydrate Equilibrium Dissociation Conditions of CO₂

Effects of 2,5-Dihydrofuran on Hydrate Equilibrium Dissociation Conditions of CO₂

Predicting Phase Equilibria of CO₂ Hydrate in Complex Systems Containing Salts and Organic Inhibitors for CO₂ Storage: A Machine Learning Approach

Study on the effect of TBAB and THF on the hydrate dissociation conditions for H2O + CO2 + C6H14 + C14H30 systems

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Experimental determination of hydrate dissociation conditions in CO2 + hexane + dodecane system, + 1-propanol or + tetra-n-butyl ammonium bromide

Cited by 5 publications

References 27 publications

Effects of 2,5-Dihydrofuran on Hydrate Equilibrium Dissociation Conditions of CO2

Effects of 2,5-Dihydrofuran on Hydrate Equilibrium Dissociation Conditions of CO2

Predicting Phase Equilibria of CO2 Hydrate in Complex Systems Containing Salts and Organic Inhibitors for CO2 Storage: A Machine Learning Approach

Study on the effect of TBAB and THF on the hydrate dissociation conditions for H2O + CO2 + C6H14 + C14H30 systems

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Product

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Effects of 2,5-Dihydrofuran on Hydrate Equilibrium Dissociation Conditions of CO₂

Effects of 2,5-Dihydrofuran on Hydrate Equilibrium Dissociation Conditions of CO₂

Predicting Phase Equilibria of CO₂ Hydrate in Complex Systems Containing Salts and Organic Inhibitors for CO₂ Storage: A Machine Learning Approach