Bayesian Optimization and Kinetic Modeling for Continuous-Flow Synthesis of 4-(Chloromethyl)-2,2-dimethyl-1,3-dioxolane Directed by Computational Fluid Dynamics

This study addresses the significant exothermic risk associated with the scaled-up synthesis of a key chemical intermediate, 4-(chloromethyl)-2,2-dimethyl-1,3-dioxolane. To precisely control the reaction heat distribution and optimize the reaction parameters, we developed a continuous-flow process optimization method guided by computational fluid dynamics (CFD) and enhanced through the combined application of kinetic modeling and machine learning (ML). A cross-platform cooperative optimization strategy was employed, which successfully identified the optimal reaction parameters: a reactant concentration of 1.013 mol/L, a temperature of 50 °C, a catalyst loading of 0.17 mol/L, and a residence time of 15 min. Under these conditions, the continuous-flow system operated stably, achieving the product in >90% yield and >99% purity. Scale-up experiments confirmed a space-time yield (STY) of 244.17 g·L–1·h–1. These results demonstrate that the integrated “continuous-flow-ML” strategy not only significantly improves the safety and product quality of highly exothermic reactions but also provides a general theoretical framework and a technical pathway for the development and optimization of continuous-flow processes.