柴油十六烷值预测模型研究

目前，测定柴油十六烷值的试验机价格较高，现有十六烷指数预测精度低，为满足炼油厂生产柴油在线调合的需要，迫切需要建立预测精度高的柴油十六烷值预测模型。基于450个具有代表性的柴油样本，建立了柴油理化性质、烃族组成与十六烷值数据库；进而采用逐步回归分析方法，应用统计产品和服务解决方案（SPSS）软件，建立了基于柴油理化性质的十六烷值预测模型和基于柴油烃族组成的十六烷值预测模型。采用F检验、T检验、残差分析验证了上述模型的有效性，并通过计算均方根误差，比较了上述两个模型的精度，结果表明，两种预测模型均有效，基于理化性质模型的预测精度优于基于烃族组成的模型。

STUDY ON PREDICTION MODEL OF DIESEL CETANE NUMBER

At present, the testing engine for measuring the cetane number of diesel is very expensive, and the prediction accuracy of the existing cetane index is low. It is urgent to establish a diesel cetane number prediction model with high prediction accuracy. Based on 450 representative samples of diesel, a database including physical and chemical properties, hydrocarbon group composition and cetane number of diesel was established. Using stepwise regression analysis, statistical product and service solutions (SPSS) software, two cetane number prediction models based on physical and chemical properties of diesel and based on hydrocarbon group composition of diesel were established, respectively. F-test, T-test and residual analysis were used to verify the validity of the models. The accuracy of the two models was compared by calculating root mean square error.The results showed that two models were all valid,and the accuracy of the model based on physical and chemical properties ofdiesel was better than that of the model based on hydrocarbon group composition of diesel.