AI Article Synopsis

  • Gas Hydrate modelling has become increasingly important for energy and environmental applications, driven by its industrial usage.
  • The traditional experimental methods for studying gas hydrates are costly and time-consuming, making modeling a more practical option for predicting conditions before implementation.
  • The proposed model outperforms existing equations of state (EOS), like SRK and PR, in accurately predicting thermodynamic conditions for gas hydrates, addressing EOS limitations and enhancing statistical modeling techniques for a wider range of conditions.

Article 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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Source
http://dx.doi.org/10.1016/j.chemosphere.2022.137550DOI Listing

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