Accurate estimation of gas hydrate formation condition is crucial for many reasons. In one hand, the gas hydrate formation is a promising approach for gas separation, cold energy storage, seawater desalination, etc. On the other hand, in some industries, this phenomenon may cause some challenges, such as pipelines blockage. The current study was undertaken to develop trustworthy predictive tools for methane hydrate formation temperature (MHFT) in the presence of brines. To achieve this target, 1051 experimental data pertinent to the methane hydrate equilibrium in 26 single and multiple brines over an extensive range of operating conditions were amassed from published studies. Four simple factors, including pressure, brine concentration, salt molecular weight and salt melting temperature were defined as the input variables, and the black-box intelligent techniques, including gaussian process regression (GPR), multilayer perceptron (MLP) and radial basis function (RBF) were employed to link MHFT to the mentioned factors. A plethora of graphical and statistically-based assessments were performed to determine the accuracy level of the proposed models. While all intelligent models had excellent performances, the one established by the GPR method showed the best agreements with experimental data, and gave the AARE and R2 values of 0.14% and 99.17%, respectively, in the testing step. The foregoing model was capable of predicting MHFT over a broad range of conditions with relative deviations mostly below 0.10%. Additionally, it showed an excellent performance in describing the physical variations of MHFT versus operating factors. The authority of the analyzed databank and the presented model was asserted via the William's plot investigation. Moreover, a sensitivity analysis was undertaken in order to determine the most fundamental factors in controlling MHFT. Eventually, a simple mathematical correlation was also derived based on the white-box intelligent approach of gene expression programming (GEP), which allowed the accurate estimation of MHFT with an AARE of 0.56%.
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