Insights and analysis of machine learning for benzene hydrogenation to cyclohexene

Cyclohexene is an important raw material in the production of nylon. Selective hydrogenation of benzene is a key method for preparing cyclohexene. However, the Ru catalysts used in current industrial processes still face challenges, including high metal usage, high process costs, and low cyclohexene yield. This study utilizes existing literature data combined with machine learning methods to analyze the factors influencing benzene conversion, cyclohexene selectivity, and yield in the benzene hydrogenation to cyclohexene reaction. It constructs predictive models based on XGBoost and Random Forest algorithms. After analysis, it was found that reaction time, Ru content, and space velocity are key factors influencing cyclohexene yield, selectivity, and benzene conversion. Shapley Additive Explanations (SHAP) analysis and feature importance analysis further revealed the contribution of each variable to the reaction outcomes. Additionally, we randomly generated one million variable combinations using the Dirichlet distribution to attempt to predict high-yield catalyst formulations. This paper provides new insights into the application of machine learning in heterogeneous catalysis and offers some reference for further research.