文献类型：会议论文

英文题名：Characteristic Selection and Prediction of Octane Number Loss in Gasoline Refinement Process

作者：Li, Wei[1,2];Yang, Jiali[3];Yang, Peihao[3];Li, Sheng[3]

机构：[1]Southern Marine Sci & Engn Guangdong Lab Zhanjian, Zhanjiang 524088, Peoples R China;[2]CNOOC China Ltd, ZhanJiang Branch, Zhanjiang 524057, Guangdong, Peoples R China;[3]Guangdong Ocean Univ, Sch Math & Comp, Zhanjiang 524088, Guangdong, Peoples R China

会议论文集：5th International Conference on Advances in Energy, Environment and Chemical Science (AEECS)

会议日期：FEB 26-28, 2021

会议地点：ELECTR NETWORK

语种：英文

外文摘要：In the refining process of gasoline, accurate prediction of the octane number loss is conducive to production management to ensure the octane content in gasoline. Therefore, the relevant research has important theoretical significance and application value. Aiming at the characteristics of octane number loss with few samples, high dimensions and non-linear of the octane number loss, this paper uses maximum information coefficient, recursive characteristic elimination and random forest regression algorithm to select the main characteristics, and establishes the octane number loss prediction model based on least squares support vector machine respectively. Compared with the three algorithms of support vector machine, BP neural network and ridge regression, the experimental results show that the two models of ridge regression and least square support vector machine have higher prediction accuracy, but the least square support vector machine has the best effect.