Abstract: Based on the commercial data from a FCC MIP process unit，seven properties of feedstock including density, saturated hydrocarbons content, aromatics content, asphaltene plus resin content, nickel content, vanadium content and residue carbon were used to cluster for the feedstock oils for MIP process by K-means clustering method and fuzzy c-means clustering（FCM）method, respectively. The ninety-five data of feedstock properties were classified into four categories by K-means clustering algorithm and into five categories by FCM clustering method. The results showed that the characteristic of every category of the feedstock oils is obvious, indicating the good applicability to the feedstock properties by each method. On the basis of the works above, the product distribution intelligent model for every category of feedstocks can be established to find the optical operation conditions for MIP process.

Key words: Fluid catalytic cracking, clustering method, intelligent model, operation optimization