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A prediction model to predict the thermodynamic conditions of gas hydrates.
Krishna Sahith Sayani, Jai; English, Niall J; Khan, Muhammad Saad; Ali, Abulhassan.
Afiliación
  • Krishna Sahith Sayani J; School of Chemical and Bioprocess Engineering, University College Dublin, Belfield, D04 V1W8, Dublin, Ireland.
  • English NJ; School of Chemical and Bioprocess Engineering, University College Dublin, Belfield, D04 V1W8, Dublin, Ireland. Electronic address: niall.english@ucd.ie.
  • Khan MS; CO(2) Research Center, Universiti Teknologi PETRONAS, Seri Iskandar, Perak, Malaysia.
  • Ali A; Department of Chemical Engineering, Jeddah University, Jeddah, Saudi Arabia.
Chemosphere ; 313: 137550, 2023 Feb.
Article en En | MEDLINE | ID: mdl-36521742
ABSTRACT
Gas Hydrate modelling has gained huge attention in the past decade due to its increase in usage for various energy as well as environmental applications at an industrial scale. As the experimental approach is highly expensive and time-consuming, modelling is the best way to predict the conditions before the actual applications at industrial scales. The commercial software currently existing uses the equation of states (EOS) to predict the thermodynamic conditions of gas hydrates. But, in certain cases, the prediction by using EOS fails to predict the hydrate conditions accurately. Therefore, there arose a need for an accurate prediction model to estimate the hydrate formation conditions. So, in this work, an accurate prediction model has been proposed to predict the thermodynamic equilibrium conditions of the gas hydrate formation. The performance of prediction accuracy for the proposed model is compared with those of the SRK equation of state and Peng Robinson (PR) Equation of state. It was observed that in most of the cases the proposed model has predicted the thermodynamic conditions more accurately than the PR and SRK equation of state. This work helps in understanding the limitations of EOS for the prediction hydrate conditions. Also, the current work helps in strengthening the conventional statistical modelling technique to predict the hydrate conditions for a broader range.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Agua Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Chemosphere Año: 2023 Tipo del documento: Article País de afiliación: Irlanda

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Agua Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Chemosphere Año: 2023 Tipo del documento: Article País de afiliación: Irlanda