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

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.