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

Díaz-Ramírez, Néstor L.; Pimentel-Rodas, Alfredo; Galicia‐Luna, Luis A.; Notario-López, Angel M.

doi:10.1016/j.jct.2022.106750

Cited by 5 publications

(1 citation statement)

References 31 publications

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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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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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Formation methods and applications of carbon dioxide hydrate: An overview

Xiao

2023

Carbon Capture Science & Technology

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Formation and decomposition characteristics of CO2+TBAB hydrate for a safer CO2 storage

Ma,

Tian,

Liu

et al. 2024

Energy

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

Cited by 5 publications

References 31 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

Formation methods and applications of carbon dioxide hydrate: An overview

Formation and decomposition characteristics of CO2+TBAB hydrate for a safer CO2 storage

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

Cited by 5 publications

References 31 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

Formation methods and applications of carbon dioxide hydrate: An overview

Formation and decomposition characteristics of CO2+TBAB hydrate for a safer CO2 storage

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