The global energy content of methane occurring in the
gas hydrate
form is immense. Understanding the fundamental of hydrate phenomena
is crucial in predicting its formation, growth, and decomposition
dynamics. In this contribution, at first, we propose a generalized
formulation of sI hydrate formation and decomposition targeting to
employ for (i) single and mixed guests, (ii) pure and salt waters,
and (iii) reservoir with and without porous particles. This framework
introduces the nth order reaction (phase transformation)
and theoretically addresses various concerns, including the surface
renewal of porous particles and the existence of tension at the interface
between the guest gas and liquid water, arising during the formation
or decomposition of gas hydrates. In the next phase, we develop the
nondominated sorting genetic algorithm-II (NSGA-II) to optimize the
model performance in predicting the experimental data. Such a global
optimization strategy is proposed for the first time in the gas hydrate
area. Validating this optimal model with a wide variety of data sets
reported for the formation, growth, and decomposition of methane and
carbon dioxide gas hydrates at reservoir mimicking conditions, the
model is subsequently compared to show its superiority over a couple
of latest models. Further, we applied the developed optimization strategy
to the five existing hydrate models and showed that the modified optimal
forms of those five models outperform their original forms. The performance
improvement is quantified here by the percent absolute average relative
deviation (%AARD). Based on the promising performance achieved by
this generalized formulation, it is recommended to use it for system
design, operation, optimization, and troubleshooting.