Key points are not available for this paper at this time.
The heterogeneous continuous flow hydrogenation is pivotal in chemical research and production, yet its reaction optimization has historically been intricate and labor-intensive. This study introduces a heterogeneous continuous flow hydrogenation system specifically designed for the preparation of 2-amino-3-methylbenzoic acid (AMA), employing FTIR inline analysis coupled with an artificial neural network model for monitoring. We explored two distinct reaction optimization strategies: multi-objective Bayesian optimization (MOBO) and intrinsic kinetic modeling, executed in parallel to optimize the reaction conditions. Remarkably, the MOBO approach achieved an optimal AMA yield of 99% and a productivity of 0.64 g/hour within a limited number of iterations. Conversely, despite requiring extensive experimental data collection and equation fitting, the intrinsic kinetic modeling approach yielded a similar optimal AMA yield but a higher productivity of 1.13 g/hour, attributed to increased catalyst usage. Our findings indicate that while MOBO offers a more efficient route with fewer required experiments, kinetic modeling provides deeper insights into the reaction optimization landscape but is limited by its assumptions.
Chai et al. (Tue,) studied this question.