2024
DOI: 10.1016/j.energy.2023.129947
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Evaluating CO2 hydrate kinetics in multi-layered sediments using experimental and machine learning approach: Applicable to CO2 sequestration

Vikas Dhamu,
Xiao Mengqi,
M Fahed Qureshi
et al.
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Cited by 14 publications
(9 citation statements)
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“…Since the introduction of artificial intelligence applications in hydrate equilibrium prediction in the late 20th century, various types of ML models, such as k-Nearest Neighbors (KNN), Decision Tree (DT), and Support Vector Regression (SVR), have been utilized. ,, More recently, advanced approaches have emerged that incorporate variations of existing models, such as Least Squares Support Vector Machines (LSSVM), and ensemble methods, including Random Forest (RF) and Extra Trees (ET) . Evolutionary algorithms such as Gene Expression Programming (GEP) have also been employed to predict equilibria of mixed gas hydrates, and neural network-based algorithms, including Multi-Layer Perceptron (MLP) and Adaptive Neuro-Fuzzy Inference System (ANFIS), have enriched the array of predictive tools. , Increasingly, ML is used not only for hydrate equilibrium prediction but also for searching for high-performance thermodynamic promoters of hydrates or predicting hydrate formation kinetics for CCS. , …”
Section: Introductionmentioning
confidence: 99%
“…Since the introduction of artificial intelligence applications in hydrate equilibrium prediction in the late 20th century, various types of ML models, such as k-Nearest Neighbors (KNN), Decision Tree (DT), and Support Vector Regression (SVR), have been utilized. ,, More recently, advanced approaches have emerged that incorporate variations of existing models, such as Least Squares Support Vector Machines (LSSVM), and ensemble methods, including Random Forest (RF) and Extra Trees (ET) . Evolutionary algorithms such as Gene Expression Programming (GEP) have also been employed to predict equilibria of mixed gas hydrates, and neural network-based algorithms, including Multi-Layer Perceptron (MLP) and Adaptive Neuro-Fuzzy Inference System (ANFIS), have enriched the array of predictive tools. , Increasingly, ML is used not only for hydrate equilibrium prediction but also for searching for high-performance thermodynamic promoters of hydrates or predicting hydrate formation kinetics for CCS. , …”
Section: Introductionmentioning
confidence: 99%
“…One such form is under the seabed, where CO 2 can be stored as a lake with a CO 2 hydrate cap, providing enhanced safety and increased storage capacity by preventing CO 2 leakage and allowing for a larger amount of CO 2 to be sequestered beneath the hydrate cap. 6 Another method involves CO 2 hydrate sediments, where CO 2 is injected into marine sediments, resulting in the formation of CO 2 hydrates, thus leveraging the kinetics and morphology of hydrate formation for sequestration, 7 as illustrated in Figure 1.…”
Section: Introductionmentioning
confidence: 99%
“…One such form is under the seabed, where CO 2 can be stored as a lake with a CO 2 hydrate cap, providing enhanced safety and increased storage capacity by preventing CO 2 leakage and allowing for a larger amount of CO 2 to be sequestered beneath the hydrate cap . Another method involves CO 2 hydrate sediments, where CO 2 is injected into marine sediments, resulting in the formation of CO 2 hydrates, thus leveraging the kinetics and morphology of hydrate formation for sequestration, as illustrated in Figure . Additionally, CO 2 replacement with natural gas hydrates (NGH) presents an environmental advantage, as it can exploit the trapped methane for energy while simultaneously storing CO 2 . , These processes not only store carbon but also stabilize methane hydrate formations, providing an innovative dual benefit of potent greenhouse gas management and energy recovery.…”
Section: Introductionmentioning
confidence: 99%
“…17,18 Therefore, storing CO hydrates in marine sediments is a prospective method of carbon storage. 19,20 It is critical to investigate the kinetics of hydrate formation and storage stability in marine sediments. Many researchers have analyzed the effects of various factors on the hydrate formation in simulated marine sediments.…”
Section: Introductionmentioning
confidence: 99%