基于单体烃分析的辛烷值测定方法中模糊聚类技术的应用研究

介绍了一种应用模糊聚类技术构建辛烷值预测模型的新方法。该模型用来由汽油单体烃数据预测汽油辛烷值，通过提取单体烃分析谱图中的140个工艺特征组分以及对辛烷值贡献大的组分为特征值进行模糊聚类。实际应用时,在待测汽油样品共同参与聚类的条件下,用与待测汽油样品为同一类、并有最小欧氏距离(<1.5)的3~10个样本作为构建辛烷值预测模型的样本。这种按新的建模样本选择方式得到预测模型的方法具有更好的辛烷值预测精度、更广的适用范围和更高的数据资源的利用率。

Investigation of Fuzzy-Clustering in Octane Number Prediction Model Based on Detailed Hydrocarbon Analysis Data

A method to establish octane number prediction model based on detailed hydrocarbon analysis (DHA) data is presented. The techniques of fuzzy-clustering and the Euclidian distance are employed to select the samples needed in pattern establishment. One hundred and fifty gasoline samples and an amount of 140 characteristic components in the DHA chromatogram of each sample are used for the fuzzy-clustering research. It is found that the 3-10 samples, which have the nearest Euclidian distance (<1.5) to the prediction sample in the same cluster, are enough to build the octane number prediction model. The experimental results proved that the model obtained according to the above method has more predictable accuracy, wider application range and higher data resource utility compared with the current prediction method.