关键词: 超临界裂解态;煤油;流量特性;预测模型;深度学习

Abstract: In order to figure out flow characteristics and implement accurate prediction of flow rate of supercritical cracked kerosene， flow rate of supercritical cracked kerosene in a large range of pressure and temperature was measured and flow characteristics were analyzed. Furthermore， prediction models respectively based on multivariate liner regression method， multivariate polynomial regression method and deep learning method were built and several evaluation criteria were given. The results indicate that flow rate of supercritical cracked kerosene varies with pressure drop and temperature， and there exists strong nonlinear relationship between flow rate and pressure drop as well as temperature. The results also show that deep neural network model based on deep learning method with mean relative prediction error at about 1.1% and max relative prediction error lower than 7%， is better than multivariate liner regression model as well as multivariate polynomial regression model and can more accurately reflect flow characteristics of supercritical kerosene.

Key words: Supercritical cracked state;Kerosene;Flow characteristics;Prediction model;Deep learning