4.5 Article

Phase equilibrium in canonical cubic structure I (sI) and II (sII) and hexagonal (sH) gas hydrate solid solutions

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FLUID PHASE EQUILIBRIA
卷 578, 期 -, 页码 -

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ELSEVIER
DOI: 10.1016/j.fluid.2023.114005

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Gas hydrates; Phase equilibria; Thermodynamic models; Calculation methods; Machine learning

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This article discusses the research progress and challenges in the field of canonical clathrate or gas hydrate phase equilibria, as well as the application of computational methods and models. It also explores the potential of machine learning techniques in hydrate and thermodynamic calculations.
Canonical clathrate or gas hydrates of structure I, II and H are the main interest of engineers and scientists in a variety of technological fields including flow assurance in hydrocarbon pipelines and processing facilities, natural gas recovery from the earth's hydrates, CO2 capture and storage, methane and H2 storage etc. Clathrate hydrate systems exhibit a variety of phase equilibria and the knowledge of the relevant phase diagrams is necessary for the design of relevant processes and facilities. In this work, the various types of canonical hydrate phase equilibria of interest, models of material behaviour for phases encountered in gas hydrate systems and computational methods of phase equilibria are discussed. The term model refers to thermodynamic models for the calculation of fugacities or activity coefficients. The term method refers to hydrate equilibrium computational procedures based on combinations of the hydrate statistical thermodynamics van der Waals and Platteuw (vPW) or its improved versions with various fluid phase thermodynamic models such as equations of state and activity coefficient models. Our discussion and presentation follows a deductive approach to distinguish it from other works that are a collection of all computational methods to calculate hydrate equilibria and which often use the terms method and model interchangeably. Original references are cited as well as the current status and recent advances and challenges are considered. The advent of machine learning techniques for gas hydrate and thermodynamic calculations is also discussed.

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