中文摘要：

随着乙烯裂解原料种类的日益增多,原料分析仪价格昂贵,因此根据乙烯裂解原料属性进行在线聚类,对实现乙烯收率建模,优化乙烯产率、节能减耗具有重要现实意义。为了提高原料在聚类的准确性,提出了一种基于直觉模糊集理论的核聚类算法。即在定义直觉模糊集隶属度时通过引入犹豫度来表征数据的不确定信息,同时利用直觉模糊熵对多核聚类算法的损失函数重新定义,使类簇中的数据点最优化;进一步地,使用随机森林对裂解原料属性进行特征选择,依据对乙烯产率的贡献度选取聚类的主要特征属性。最后根据实际工业裂解的石脑油数据验证了所述算法的有效性。

英文摘要：

Along with the increasing types of ethylene cracking materials and expensive feed analyzer, clustering of ethylene cracking materials which is to improve ethylene yield modeling, ethylene yield and energy consumption has very important practical significance. In order to improve the accuracy of online identification of raw materials, an intuitionistic fuzzy kernel clustering algorithm based on the theory of intuitionistic fuzzy sets is presented. In the definition of membership, membership considers uncertain information which is the hesitation degree. At the same time, intuitionistic fuzzy entropy is incorporated in the loss function of multiple kernel clustering algorithm. That is to optimize the data points in the class. Further, the cracking material attribute feature selection using random forest, based on the main attributes of contribution of ethylene yield. Finally, the actual ethylene cracking naphtha data of industry is used to verify the effectiveness and superiority of the algorithm.