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.