关键词: 天然气水合物,     ,  结晶,    ,  成核,    ,  热力学,    ,  支持向量机,    , 交叉验证

Abstract: Natural gas hydrate has the advantages of abundant reserves, large calorific value and low emission, which can mitigate the environmental pollution problems caused by traditional fossil energy. The generation process of natural gas hydrate form is a system with multicomponents and multiphysical states. The nucleation process is complex, which needs to consider the effects of pressure, temperature, promoters, stirring speed and so on. It is difficult to accurately predict the hydrate formation, because the hydrate formation process not only involves thermodynamics problems but also dynamics problems. In our paper, the support vector machine theory combined with experimental data was used to establish support vector machine prediction model for predicting natural gas hydrate equilibrium pressure. The prediction accuracy was estimated by using the mean square error, the square correlation coefficient, the square absolute percentage error and the average absolute error. The results are 8.370 08×10-5，99.897 6%，0.542 4%，1.990 0%，respectively. The pretreatment origin data were normalized (［1，2］) and the nuclear parameter g(4)and punishment factor c(1.414 2) were optimized by using cross validation methods. Simulation results show that the equilibrium pressure obtained by support vector prediction model is good in agreement with the equilibrium obtained by experiments. The better ideal prediction effects prove that the model has advantages of accuracy and reliability. It can provide certain reference for research on natural gas hydrate in future.

Key words: Natural gas hydrate,    ,  Nucleation,    ,  Dynamics,    ,  Thermodynamics,    ,  Support vector machine,    ,  Cross validation