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.
其他摘要：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.
