关键词: 催化重整；LightGBM决策树模型, 产品预测；特征重要度；相关性分析

Abstract: Based on the Aspen HYSYS software platform, the mechanism model of a continuous catalytic reforming unit was established in accordance with the limited real production data from a real-world refinery. Considering various production status on this model, the range of the production data were extended to a more complete data set for product prediction. Furthermore, the LightGBM decision tree model, which is suitable for nonlinear process modeling, was used to model the catalytic reforming unit. Compared with the BP neural networks commonly used in current similar studies and with the same training speed, the LightGBM models have shown the higher prediction accuracy through the 10-fold cross validation tests. The temperatures of the four reactors and the recycle hydrogen consumption were selected as characteristics and operational variables, and the products’ flow of pentane, xylene, C6, gasoline, hydrogen and the purity of hydrogen were objectives. Six single objective data-driven product prediction models are established. Finally, the ranks of the feature importance were obtained by the correlation analysis between the characteristics and the objectives. The obtained research results could provide decision reference for the refinery’s online optimal operations.

Key words: catalytic reforming, lightGBM decision tree model, product prediction, feature importance, correlation analysis