Continuous Flow Nitration of Nitrobenzene: Kinetic Modeling and Bayesian Optimization

The kinetics of nitrobenzene (NB) nitration with mixed acid is critical for industrial scale-up and safety control. However, accurate kinetic data are difficult to obtain in traditional batch reactors. In this study, a continuous-flow system with a micromixer was designed to conduct nitration under homogeneous conditions. Key kinetic parameters, including observed reaction rate constants based on HNO3 and NO2+, the pre-exponential factor, and activation energy for NB nitration, were determined. Subsequently, the kinetic model was employed as a reaction simulator within the Bayesian optimization framework to determine the optimal reaction conditions. Remarkable optimization was achieved in just 15 iterations, resulting in an E-factor of 0.9 and an NB conversion of 0.98. This study highlights the potential of integrating advanced machine learning techniques into chemical reaction optimization, simultaneously enhancing both conversion and the E-factor.