New Method for Feature Selection Based on Complex Causality Model and Its Application in the Industrial Unit of Wax Oil Hydrogenation
The feature selection method for complex causal relationship models is a challenging issue in the current field of artificial intelligence,and to develop new feature selection methods is of great significance for the development of causal relationship models.This study targets the wax oil hydrogenation industrial device,and focuses on the complex causal relationship between the features of input independent variables including raw material properties,operation parameters and output dependent variables of the model and the sulfur content in refined wax oil.Suitable features are selected by comprehensively considering the relationship between input variables and that between input and output variables.The first step is to calculate the correlation between the features of the independent variable and between the independent variable and the dependent variable.Then it is suggested to design thresholds and discriminant functions,combine independent and dependent variables,comprehensively consider the relationship between the two types of variables,and ultimately screen and remove the initial boiling point in the wax oil distillation range.Compared with traditional correlation feature selection methods,the average absolute error(MAE)and the average relative error(MRE)in prediction of sulfur mass fraction for refined wax oil by the new screening feature model are reduced by 62.97 μg/g and 2.02 percentage points respectively,and the determination coefficient R2 is increased by 0.395,fully demonstrating the effectiveness of the new feature selection method for complex causal relationship models.
万方数据
维普
