Application of artificial intelligence (AI) in kinetic modeling of methane gas hydrate formation

Foroozesh, Jalal; Khosravani, Abbas; Mohsenzadeh, A.; Mesbahi, Ali Haghighat

doi:10.1016/j.jtice.2014.08.001

Cited by 22 publications

(15 citation statements)

References 16 publications

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“…One such screening approach has been reported by Chin et al This kind of screening approach can be integrated with robust search algorithms like artificial intelligence (AI) and deep learning (DL) techniques. Foroozesh et al use the artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS), sub-branches of the AI technique to investigate the methane hydrate formation kinetics . The correlation of pressure and temperature with the growth rate of hydrate formation is formulated in the AI framework.…”

Section: Future Direction Of Ils As This and Khis For Gas Hydratementioning

confidence: 99%

Minireview on the Thermodynamic and Kinetic Modeling of Ionic Liquid Promoted Inhibition of Gas Hydrate Formation

2021

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Gas hydrate naturally forms under the subsea bed and in the transportation pipelines with favorable thermodynamic conditions. Ionic liquid (IL) has been established itself as a novel kinetic and thermodynamic inhibitor of gas hydrate formation and dissociation. The theoretical understanding further enables exploration of the various classes of ILs for the dissociation and inhibition of gas hydrate formation. This review is the focused assessment of thermodynamic and kinetic modeling of the gas hydrate/IL system with a detailed discussion of inhibition aspects and the role of ILs. Based on the present status, future areas of gas hydrate/IL modeling and possible improvements are highlighted.

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Section: Future Direction Of Ils As This and Khis For Gas Hydratementioning

confidence: 99%

Minireview on the Thermodynamic and Kinetic Modeling of Ionic Liquid Promoted Inhibition of Gas Hydrate Formation

2021

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“…The precise computational approach of methanol loss to the vapour phase within hydrate inhibition and right injection rate are calculated in (2) [23]. The neuro-fuzzy method is using (2) to find the growth rate of error [24] and in the Statistical Package for the Social Sciences (SPSS) static model of hydrate formation correlation (2) is also applied to determine its error [25]. The optimum number of hidden neurones is calculated by engaging MSE and regression R-value as an evaluation of ANN-MLP [26].…”

Section: Resultsmentioning

confidence: 99%

Evaluating Pressure in Deepwater Gas Pipeline for the Prediction of Natural Gas Hydrate Formation

Abbasi

Hashim

2019

Eng. Technol. Appl. Sci. Res.

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This paper proposes the prediction of hydrate formation pressure in deepwater pipeline with an approach of intelligent optimization. The proposed novel correlation of hydrate formation is using the function of ordinary differential equation. The developed optimization prediction model was founded on the constant coefficients which were examined by a multiple set of experimental data of methane (CH4), ethane (C2H6), propane (C3H8), iso-butane (iC4), nitrogen (N), Carbon Dioxide (CO2) and hydrogen sulfide (H2S) hydrates. The consequences of this research are highly optimistic for the natural gas production industry.

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“…In this method, carbon dioxide gas is injected at a depth below 400 m and it is trapped by solubilization in water and, due to low temperature and high pressure, deep water carbon dioxide is converted into hydrate at 500 to 900 m above sea level. In this study, the effect of additives (nanoparticles and surfactants) on thermodynamics and kinetics of carbon dioxide hydrate formation is studied and investigated [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Investigation of the kinetics of formation of Clatrit-like dual hydrates TBAC in the presence of CTAB

Mashhadizadeh

Bozorgian

Azimi

2020

Eurasian Chem. Commun.

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As it is always necessary to design a pipeline at high pressure (high density), pipelines are also exposed to ambient temperatures and are usually exposed to low temperatures. On the other hand, in the presence of water vapor (almost all natural gases have some water vapor), and more importantly, the presence of hydrocarbons causes hydrate crystals to form. In this work, the capacity of carbon dioxide hydrate storage in water in the presence of surfactants at different temperatures, pressures and concentrations of TBAC and CTAB additives was calculated and measured using induction time measurement. The results of experiments show that with increasing pressure and decreasing temperature the storage capacity of CO2 in hydrate increases. Addition of CTAB also dramatically increases the storage capacity, while increasing pressure has a greater impact on the storage capacity of carbon dioxide in the hydrate. The effect of TBAC and CTAB surfactant on the induction of hydrate formation and carbon dioxide storage capacity was investigated. Design Expert software was used to design the experiment. Finally, statistical analysis of the effective parameters on the time of induction of hydrate formation showed that TBAC can decrease the time of induction of hydrate formation compared to other additives. In investigation of the effect of variables on the storage capacity of carbon dioxide gas, it can be concluded that increasing the amount of CTAB surfactant and pressure has the most impact on the increase of carbon dioxide storage capacity compared.

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Application of artificial intelligence (AI) in kinetic modeling of methane gas hydrate formation

Cited by 22 publications

References 16 publications

Minireview on the Thermodynamic and Kinetic Modeling of Ionic Liquid Promoted Inhibition of Gas Hydrate Formation

Minireview on the Thermodynamic and Kinetic Modeling of Ionic Liquid Promoted Inhibition of Gas Hydrate Formation

Evaluating Pressure in Deepwater Gas Pipeline for the Prediction of Natural Gas Hydrate Formation

Investigation of the kinetics of formation of Clatrit-like dual hydrates TBAC in the presence of CTAB

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