Abstract: C₄ olefins are widely used as a common chemical material to produce pharmaceutical chemicals and intermediates. In the preparation process of C₄ olefins， the catalyst combination and temperature affect the selectivity and yield of C₄ olefins. Hence，exploring the process conditions for preparing ethanol-coupled C₄ olefins is essential and valuable. In this work，an RBF neural network model was established to investigate the influence of catalyst combination composition and temperature on ethanol conversion and C₄ olefins selectivity based on experimental benchmark data for different catalyst combinations and temperatures provided in Problem B in the Mathematics Competition of Chinese College Students 2021. The results showed that temperature had a strong correlation with C₄ olefins selectivity，the mass of Co/SiO₂ and HAP had essentially the same effect on C₄ olefins selectivity，Co loading had a more significant impact on C₄ olefins selectivity than ethanol conversion，and ethanol feed had a weaker correlation with the C₄ olefins selectivity and ethanol conversion.

Key words: catalyst combination and temperature, ethanol conversion, C₄ olefins selectivity, RBF neural network