关键词: 原油性质, 直馏沥青性质, 智能预测模型, 特征扩增, 软化点预测, 延度预测, XGBoost,

Abstract:

It introduces the process of using XGBoost algorithm model for data processing and model prediction of straight-run asphalt properties. Based on the operating data of atmospheric and vacuum distillation units and crude oil evaluation data of refining enterprises, combined with production experience to expand the data characteristics, a data-driven regression model is established for the collected 128 asphalt product analysis data. The gradient lifting tree model is used to fully explore the correlation between crude oil properties and asphalt properties, and intelligently predict key indicator properties such as softening point and ductility(10 ℃) of asphalt products under different target penetration degrees. The coefficient of determination for predicting the softening point of asphalt is greater than 0.77. By comparison, the data features that are expanded can effectively improve the predictive ability of the model. The ability of model prediction under different data volumes is also analyzed, and with the continuous accumulation of data, the prediction potential is great. This study can provide enterprises with the crude oil ratio to produce high-grade asphalt, assist enterprises in optimizing the global production process and achieving cost reduction and efficiency increase.

Key words: crude oil properties, straight-run asphalt properties, intelligent predictive models, feature expansion, softening point prediction, ductility predicting, XGBoost