FlowBO: A Flow Chemistry Bayesian Optimization Framework Benchmarked by Kinetic Models

The applications of flow chemistry (continuous flow reactions) in the synthesis of pharmaceuticals and fine chemicals require more advanced optimization algorithms to guide laboratory-scale and industry-scale optimization. Although several Bayesian Optimization (BO) frameworks have been developed, they are rarely equipped with state-of-the-art noise-handling acquisition functions and have not been benchmarked by multiple real-world continuous flow kinetic models. In this study, we developed FlowBO for flow chemistry, equipped with the recently-developed MOO algorithm qNEHVI that can better handle experimental noise and make parallel recommendations. Also, five kinetic models built from experimental results, including four series reactions, were used as benchmarks for FlowBO and two other recognized BO frameworks. The results show that FlowBO outperforms in all four series reaction cases with optimization results >99.9% for conversion and selectivity. At the same time, FlowBO offers a range of optimum advantages with a wide choice of temperature, residence time, and reactant concentration, facilitating process optimization for subsequent steps (i.e. separation).