Optimization of heterogeneous continuous flow hydrogenation using FTIR inline analysis: a comparative study of multi-objective Bayesian optimization and kinetic modeling

12 March 2024, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


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.


Multi-objective Bayesian optimization
Kinetic modeling
continuous flow
heterogeneous hydrogenation
Reaction optimization
Inline analysis

Supplementary materials

Supporting Information
Supporting figures, tables, and experiments.


Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.