Methane hydrate: Modeling and assessing the experimental data of incipient stability conditions

ABSTRACT

Accurate knowledge of equilibrium conditions for methane hydrates dissociation is crucial to implement an appropriate hydrate-based technology/process. Hence, much importance is attached to utilize techniques for the modeling of hydrate stability zones and the existence of reliable data as well. This research employs the published experimental data, from 1940 to 2016, for modeling the incipient stability conditions of methane hydrate in pure water using Classification and Regression Tree (CART) as a novel methodology in gas hydrates research. Least squares support vector machine (LSSVM) and artificial neural network (ANN) methods were selected as a basis for comparison. Furthermore, the Leverage mathematical approach was used for evaluating the quality of the data as well as the correctness of the CART model. Results confirmed that the developed tree-based model provides excellent outcomes and no model can rival it for accuracy. In addition, applying the Leverage algorithm specified that: (a) the CART model is statistically valid and correct; (b) the published experimental data in the literature are different in quality; (c) there is no doubtful data in the studied database; and (d) the published data in the temperature range from 148.8 to 238.8 K are not sufficient compared with the whole of the collected database.

GRAPHICAL ABSTRACT

KEYWORDS:

• Classification and regression tree
• clathrate
• methane hydrate
• modeling
• outlier detection