Pattern Recognition Technology Application in Intelligent Processing of Heavy Oil

Abstract

Reliable and efficient product yield estimation for unknown oils after the fluid catalytic cracking (FCC) reaction is one of the key components in heavy oil intelligent processing. This paper describes the use of two chemometric pattern recognition methods, k-nearest neighbor (k-NN) classification and supervised self-organizing maps (SSOMs), for building classification models to determine the most similar oil sample to an unknown sample in a given data set and to use the FCC yields record of the correspondent oil as the product yield prediction for the unknown sample under the same reaction conditions. Two-sided t test, correlation analysis, and hierarchical cluster heat map analysis were performed to assess the quality of the models. The work provides laboratory evidence that k-NN or SSOMs techniques could all be employed for FCC product yield estimation, while the k-NN model would be more suitable for industrial application in terms of stability and efficiency.