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 8.37008×10 -5 ,99.8976%,0.5424%,1.9900%,respectively.The pre-treatment origin data were normalized ([1, 2])and the nuclear parameter g (4 )and punishment factor c (1.4142 )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.%天然气水合物的生成过程是一个多组分、多物态的系统,存在着复杂的结晶成核过程,需要考虑压力、温度、促进剂、搅拌速度等因素的影响,不但涉及动力学问题还涉及热力学问题,对其生成很难进行精确预测.基于支持向量机理论,结合实验数据,建立支持向量机预测模型来进行天然气水合物生成时的相平衡压力预测,采用平均平方误差、平方相关系数,以及平方绝对百分比误差和平均绝对误差等四种误差公式对预测精度进行评估,结果分别为8.37008×10-5、99.8976%、0.5424%、1.9900%,还对源数据进行了归一化([1,2])预处理以及利用交叉验证方法对核参数g(4)和惩罚因子c(1.4142)进行了优化.模拟结果显示,由支持向量机预测模型得到的相平衡压力与实际实验获得的相平衡压力基本一致,预测效果较理想,证明该模型具有较高的准确性和可靠性.
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