Abstract: The yield of aromatics was predicted based on a Decision Tree Regression model, which was trained using actual feed composition data from a continuous reformer. The relatively concentrated sample range of industrial data can lead to over-fitting which limits the model’s application. The small sample issue can be seen as a common problem when dealing with industrial data. A virtual sample of reforming feed was constructed with Multivariate Gaussian probability distribution method, and the corresponding aromatics yield was simulated with HYSYS mechanism model to improve the problem mentioned above. After the Decision Tree Regression model training with feed composition mixed virtual data and real data, the mean absolute error of the test sample was reduced from 1.4097 to 0.6318, which proves that virtual samples can be used for model training to expand the application of data-driven models.

Key words: reforming process data, virtual sample, Gaussian distribution, HYSYS simulation