Insights into Structure-Activity Relationships between Y Zeolites and their n-C10 Hydrocracking Performances via Machine Learning Approaches

Hydrocracking technology represents a crucial position in the conversion of heavy oil and the transformation development from oil refining to the chemical industry. The properties of catalysts are one of the key factors in the hydrocracking process. As the main acidic component of hydrocracking catalyst, the influence of zeolite properties on the reaction performance has been the focus of research. In this study, a series of NiMo/Al 2 O 3 -Y catalysts were prepared using different Y zeolites as acidic components, and their performances in the hydrocracking of n -C 10 were also evaluated. The structure-activity relationship between Y zeolite and the cracking performance of n -C 10 was investigated with machine learning. First, a database of the physical and chemical properties of Y zeolite and their performance was established, and the correlation analysis was also conducted. Parameters such as the cell constant, acid content, acid strength , B/L ratio, mesopore volume, micropore volume of Y zeolite, and the reaction temperature were selected as independent variables. The conversion of n -C 10 and the ratios of products C 3 /C 7 and i -C 4 / n -C 4 were selected as dependent variables. A model was established by the random forest algorithm and a new zeolite was predicted based on it. The results of model prediction were in good agreement with the experimental results. The R 2 of the n -C 10 conversion, C 3 /C 7 ratio, and i -C 4 / n -C 4 ratio were 0.9866, 0.9845, and 0.9922, and the minimum root mean square error values were 0.0163, 0.101, and 0.0211, respectively. These results can provide reference for the development of high performance hydrocracking catalyst and technology. The complex structure-activity relationship in hydrocracking catalysts, influenced by numerous variables, poses challenges in research. Machine learning enables insights for designing new catalysts by deciphering this intricate relationship.