Application of PCA-Kmeans method-based BP neural network to the prediction and optimization studies in S ZORB Sulfur Removal Technology

Abstract

In this paper, the modeling of predicting the gasoline octane number and sulfur content in S ZORB Sulfur Removal Technology (SRT) is established. In the modelling, the principal component analysis (PCA) and unsupervised K-means clustering algorithm were initially integrated together to determine the key variables that affect the octane number and sulfur content of the product. With the selected key variables, the backpropagation neural network prediction models of the product octane number and sulfur content were established, trained and tested. Moreover, the mean accuracy of the prediction error within 0.15 and 0.3 were 94% and 99%, respectively. Besides the prediction of output of the S ZORB SRT Reactor, a multi-variable random walk optimization method was also proposed and investigated to reduce the octane loss, which was expected to be reduced by more than 30%, during desulfurization of fluid catalytic cracking gasoline in the S ZORB SRT Reactor, meanwhile the sulfur content stayed relatively stable which was less than 5 ppm. The results of the proposed models are reliable and could be applied into the real industrialization, which are beneficial with both the efficiency of economy and environmental protection.