Measurements for the Dissociation Condition of Carbon Dioxide Hydrate with each Additive of Cyclopentanol, Cyclohexanol, Cycloheptanol, Cyclohexanone, or Cycloheptanone

Khudaida, Salal Hasan; Lin, Po-Yi; Su, Chie-Shaan

doi:10.1021/acs.jced.3c00190

Cited by 3 publications

(1 citation statement)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

Phase equilibria and crystallographic structure of clathrate hydrate formed with carbon dioxide and cyclohexanone

Kamiya,

Kasai,

Takeya

et al. 2024

Fluid Phase Equilibria

View full text Add to dashboard Cite

Experimental Measurements for Equilibrium Conditions of Carbon Dioxide Hydrate Mixtures with Each Additive of 2-Methoxyethyl Ether, 1,1,3,3-Tetramethylguanidine, 4-Methylmorpholine, 1-Ethyl-3-Methylimidazolium Chloride, Urea, and 1,3-Cyclohexanebis(methylamine)

Lin,

Wu,

Lai

et al. 2024

J. Chem. Eng. Data

View full text Add to dashboard Cite

Due to decarbonization issues, there is growing concern about greenhouse gases, especially carbon dioxide (CO 2 ). Gas hydrate research is a potential solution to energy needs and CO 2 storage tasks. Naturally occurring methane hydrates provide natural gas availability, while CO 2 hydrates are also receiving widespread attention for carbon capture, storage, and pipeline flow assurance. This study measured the thermodynamic equilibrium temperatures and pressures at the dissociation points for CO 2 hydrate mixtures containing various additives. These results provide fundamental data on the thermodynamic phase boundaries of CO 2 hydrate mixtures where three phases of hydrate (H), liquid water (L w ), and vapor (V) coexisted. The equilibrium conditions for CO 2 with pure water and various additives including 2-methoxyethyl ether (0.20 and 0.30 mass fractions), 1,1,3,3-tetramethylguanidine (0.20 and 0.30 mass fractions), 4methylmorpholine (0.10 and 0.20 mass fractions), 1-ethyl-3-methylimidazolium chloride (0.10, 0.20, and 0.30 mass fractions), urea (0.10, 0.20, and 0.30 mass fractions), and 1,3-cyclohexanebis(methylamine) (0.10 and 0.20 mass fractions) were experimentally measured in the pressure range of 1.54−3.58 MPa using the isochoric and temperature cycling method. These additives all had an inhibition effect on the formation of CO 2 hydrates, and the maximum average inhibition effect of each additive on the equilibrium temperature ranged from 5 to 11 K. To simulate seawater conditions, equilibrium conditions for CO 2 hydrate mixtures with three of these additives are also reported. The Clausius−Clapeyron equation was used to estimate possible hydrate structures. The results show that the CO 2 hydrate mixtures exhibited possible sI structures after adding each additive in this study.

show abstract

Measurements for the Dissociation Condition of Carbon Dioxide Hydrate with each Additive of Cyclopentanol, Cyclohexanol, Cycloheptanol, Cyclohexanone, or Cycloheptanone

Cited by 3 publications

References 62 publications

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

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

Phase equilibria and crystallographic structure of clathrate hydrate formed with carbon dioxide and cyclohexanone

Experimental Measurements for Equilibrium Conditions of Carbon Dioxide Hydrate Mixtures with Each Additive of 2-Methoxyethyl Ether, 1,1,3,3-Tetramethylguanidine, 4-Methylmorpholine, 1-Ethyl-3-Methylimidazolium Chloride, Urea, and 1,3-Cyclohexanebis(methylamine)

Contact Info

Product

Resources

About

Measurements for the Dissociation Condition of Carbon Dioxide Hydrate with each Additive of Cyclopentanol, Cyclohexanol, Cycloheptanol, Cyclohexanone, or Cycloheptanone

Cited by 3 publications

References 62 publications

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

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

Phase equilibria and crystallographic structure of clathrate hydrate formed with carbon dioxide and cyclohexanone

Experimental Measurements for Equilibrium Conditions of Carbon Dioxide Hydrate Mixtures with Each Additive of 2-Methoxyethyl Ether, 1,1,3,3-Tetramethylguanidine, 4-Methylmorpholine, 1-Ethyl-3-Methylimidazolium Chloride, Urea, and 1,3-Cyclohexanebis(methylamine)

Contact Info

Product

Resources

About

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

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