Data-driven prediction of product yields and control framework of hydrocracking unit

In this study, the relationship between the operating conditions and the product yields and a control framework of the hydrocracking process was developed. The data were collected from a hydrocracking unit in a Chinese refinery. Principal component analysis was used to decrease the number of input variables. Then support vector machine, Gaussian process regression (GPR), and decision tree regression models were developed to establish the relationship above. The best model is GPR, whose Pearson correlation coefficient between the prediction value and the actual value is greater than 0.97 for all the product yields. Shapley additive explanations were performed to interpret the results of the GPR models. A control framework of the hydrocracking unit was then proposed based on the results above. The results show that the machine learning method is a valuable tool for predicting the yield of hydrocracking products, and the control framework proposed helps optimize hydrocracking product yields.

Automated summary

This study developed a relationship between the operating conditions and product yields in the hydrocracking process, and proposed a control framework. Results indicate that machine learning is a valuable tool for predicting product yields, and the proposed framework helps optimize yields.