High time‐resolution and data‐driven kinetic modeling of cumene autoxidation based on a microreactor system

Abstract The cumene autoxidation process holds great significance in the chemical industry. However, existing kinetic models are marred by limitations such as prolonged reaction and sampling intervals, along with the failure to account for the impact of phenol, a key substance. In this work, in the microreactor system, we successfully realized high time resolution data acquisition (5–10 min) and used it to study the effects of various factors, especially phenol, on the oxidation reaction, pointing out the obvious inhibitory effect of phenol. Further, based on the data‐driven modeling, we achieved a good prediction of the kinetics ( R 2 > 0.9) and included phenol in the model. Finally, based on the model, we can successfully optimize and design the reaction.