The Optimization Model for Reducing RON Loss in Gasoline Refining Process. (20th February 2021) - Lu, Xuefei; Wang, Xiaoyan; Yang, Yifang; Xue, Jianan
Abstract : As gasoline is the main fuel of small vehicles, the exhaust emissions from its combustion will affect air quality. The focus of gasoline cleaning is to reduce the sulfur and olefin content in gasoline while maintaining its RON as much as possible. The reduction of RON will bring great economic losses to enterprises. Therefore, it is very important for petrochemical enterprises to construct a RON loss model in the gasoline refining process. The model construction, which reduces RON loss during gasoline refining, is the main question in this paper. By Python and SPSS software, we got two variable filtering methods: the random forest importance filtering and PCA filtering, and combined with SVR and random forest models, RON of the product and sulfur content were predicted. The filtering order of the original data by Excel and Python is maximum and minimum removal, 3 σ criterion removal, deletion of too many sites in incomplete data, and filling of empty values in the mean within two hours. Several RON prediction models were established with the help of Python software, and the variables selected were compared by two filtering methods: one is the SVR model based on Gaussian, linear, polynomial, and Sigmoid kernel functions; the other is the random forest model. The sulfur content and RON prediction model was constructed, which use evaluation functions such as MSE, R 2, and RMSE to evaluate and sulfur content as the subject condition. We convert the problem into linear and nonlinear model variable optimization problems: the linear model is the variable selected by the SVR linear kernel function model and random forest; the nonlinear model is the combination of variables selected by the random forest model and random forest. Optimizing for each sample, the optimization method is to find the optimal solution for each variable and use the optimal method for each variable as the local optimal solution for the sample. The two models are evaluated from the perspectives of optimization degree, optimization rate, model running speed, etc.