Intelligent selection and optimization method of feature variables in fluid catalytic cracking gasoline refining process

To suppress the research octane number (RON) loss in the gasoline refining process, an intelligent selec-tion and optimization method of feature variables is proposed. In the methodology, the random forest-based feature selection algorithm is first used to calculate the importance of each feature variable, so that the main variables are selected for the prediction modeling of the RON loss and sulfur content. Next, the long short-term memory network is introduced to establish the nonlinear mapping relationship between the main feature variables and the product yield. Finally, an objective function for minimizing RON loss under the constraints of RON loss reduction and product sulfur content is constructed, and the optimal values of feature variables are obtained by the continuous iterative solution with a hybrid grey wolf optimizer algorithm. Based on real-world data in industrial processes, experimental results verify the feasibility and effectiveness of the proposed method. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

An intelligent selection and optimization method of feature variables is proposed to suppress the research octane number (RON) loss in the gasoline refining process. By calculating the importance of main variables and establishing a nonlinear mapping relationship, the optimal values of feature variables are obtained through continuous iterative solution.