Abstract
To predict the natural gas hydrate formation conditions quickly and accurately, a novel hybrid genetic algorithm–support vector machine (GA-SVM) model was developed. The input variables of the model are the relative molecular weight of the natural gas (M) and the hydrate formation pressure (P). The output variable is the hydrate formation temperature (T). Among 10 gas samples, 457 of 688 data points were used for training to identify the optimal support vector machine (SVM) model structure. The remaining 231 data points were used to evaluate the generalisation capability of the best trained SVM model. Comparisons with nine other models and analysis of the outlier detection revealed that the GA-SVM model had the smallest average absolute relative deviation (0.04%). Additionally, the proposed GA-SVM model had the smallest amount of outlier data and the best stability in predicting the gas hydrate formation conditions in the gas relative molecular weight range of 15.64–28.97 g/mol and the natural gas pressure range of 367.65–33,948.90 kPa. The present study provides a new approach for accurately predicting the gas hydrate formation conditions.
Highlights
Natural gas hydrate is a dry ice-like crystalline inclusion compound formed by combining small gas molecules and water molecules in natural gas under certain pressure and temperature conditions
This study provides a comprehensive method for the accurate prediction of the gas hydrate formation conditions
10 representative natural-gas samples were selected, and 688 gas samples in pure water hydrate formation conditions were used as experimental data points [30,31]
Summary
Natural gas hydrate is a dry ice-like crystalline inclusion compound formed by combining small gas molecules (e.g., light hydrocarbons, CO2 , H2 S, or N2 ) and water molecules in natural gas under certain pressure and temperature conditions. Mohammad Akbari as well as Afshin et al [21] used the temperature, pressure, and natural-gas composition as input variables and combined radial basis function (RBF) neural networks with genetic algorithms (GAs) to predict conditions for natural gas hydrate formation at low temperatures. In this method, the number of nodes in the hidden layer and the weight threshold must be determined, and it is difficult to identify the optimal network structure parameters [22,23,24]. This study provides a comprehensive method for the accurate prediction of the gas hydrate formation conditions
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