Prediction of methane hydrate equilibrium in saline water solutions based on support vector machine and decision tree techniques.

The formation of clathrate hydrates offers a powerful approach for separating gaseous substances, desalinating seawater, and energy storage at low temperatures. On the other hand, this phenomenon may lead to practical challenges, including the blockage of pipelines, in some industries. Consequently, accurately predicting the equilibrium conditions for clathrate hydrate formation is crucial. This study was undertaken to design reliable models capable of predicting the equilibrium state of methane hydrates in saline water solutions. A comprehensive collection of measured data, consisting of 1051 samples, was assembled from published sources. The prepared databank encompassed the hydrate formation temperature of methane (HFTM) in the presence of 26 different saline water solutions. A machine learning modeling was undertaken through the implementation of Decision Tree (DT) and Support Vector Machine (SVM) approaches. While both models had excellent performance, the latter achieved higher accuracy in estimating the HFTM with the mean absolute percentage error (MAPE) of 0.26%, and standard deviation (SD) of 0.78% in the validation process. Furthermore, more than 90% of the values predicted by the novel models fell within the [Formula: see text]1% error bound. It was found that the intelligent models also favorably describe the physical variations of HFTM with operational factors. An examination using the William's plot acknowledged the truthfulness of the gathered data and the suggested estimation techniques. Ultimately, the order of significance of the factors governing the HFTM was clarified using a sensitivity analysis.