Twin Support Vector Regression for Prediction of Natural Gas Hydrate Formation Conditions

Abstract

Natural gas hydrates have become a threat to natural gas companies and oil industries for the formation of gas hydrates in transfer pipelines can lead to pipe blockage. Natural gas hydrate formation is favored by low temperature and high pressure; thus, if the right combination of temperature and pressure are well investigated, it is possible to solve the pipe blockage problems. Therefore, developing a precise, easy-to-use method to predict natural gas hydrate formation temperature (HFT) is very important. In this paper, we are intended to develop a novel machine learning model called twin support vector regression (TSVR) for predicting HFT in a wide range of natural gas mixtures. For constructing the TSVR model, 513 experimental data in the system methane, ethane, propane, butane, pentane, nitrogen, carbon dioxide/water, methane, carbon dioxide, propane, methanol, sodium chloride, and calcium chloride have been collected from open literature. We compared the performance of the TSVR model with least squares support vector regression (LSSVR) and three widely used correlations in the system methane, ethane, propane, butane, pentane, nitrogen, and carbon dioxide, which show that the TSVR model has fewer deviations than the LSSVR model and three correlations. The values of root-mean-square error (RMSE), mean absolute percentage error (MAPE), and correlation factor (R2) are 2.4010, 0.0061, and 0.9108, respectively. In addition, statistical parameters show that the TSVR model can also surpass the LSSVR model in predicting HFT in the system water, methane, carbon dioxide, propane, methanol, sodium chloride, and calcium chloride.