柴油馏分碳数分布的预测研究

STUDY ON PREDICTION OF CARBON NUMBER DISTRIBUTION OF DIESEL DISTILLATE

收集一定数量的柴油馏分样品,利用标准方法分别测定其基本物性、烃类组成信息和详细的碳数分布信息,建立起对应的数据库。对于一个待测柴油样本,首先根据其物性数据和烃类组成信息在库中找出与之距离最近的6个样本,然后利用这几个样本的信息,结合过采样技术在待测样本周围生成大量的虚拟样本,最后根据最近邻回归算法(KNR)进行回归计算,选择与待测样本最相似的5个虚拟样本,将这些样本的碳数分布组成信息进行线性加权加和,以此作为待测样本的预测值。将该方法应用于直馏柴油碳数分布的预测模型,柴油的硫含量、氮含量、酸值以及11个烃类(分别为链烷烃、单环烷烃、双环烷烃、三环烷烃、烷基苯、茚满/四氢萘、茚类、萘类、苊类、苊烯类和三环芳烃)的组成信息作为模型的输入特征,计算结果表明,这种模型能同时计算出直馏柴油中312项碳数集总的含量,计算速度快,准确度高,模型维护简单,具有一定的应用价值。

For the purpose of obtaining the detailed carbon number distribution of diesel distillate from its bulk properties and hydrocarbon group compositions,a new method was proposed based on the k-nearest neighbor regression algorithm （KNR） and over-sampling. With the standard methods,bulk properties,group compositions and carbon number distribution of the representative samples were obtained to build the data bank. As to a new sample to be measured,the nearest 6 samples were confirmed according to the bulk properties and group compositions,a massive virtual samples were obtained around this new sample using over-sampling technique on the 6 samples. With the KNR,the carbon number distribution of the new sample can be determined by linear weighted summing of the 5 virtual neighbors. A prediction model was established for the straight-run diesel,the contents of 312 carbon number lumps can be calculated simultaneously from the content of sulfur,nitrogen,acid number and the compositions of 11 type hydrocarbons（paraffin,monocycloalkane,bicycloalkane,tricycloalkane,alkylbenzene,indan tetrahydronaphthalene,indene,naphthalene,acenaphthene,acenaphthlene and tricyclic aromatic hydrocarbon）. The model was accurate,fast and easy to maintain,which made it more useful and valuable in practical implement.