Kinetic study in an automatic continuous‐flow photochemical platform with machine learning

Abstract

AbstractIn this work, we first solved the partial differential equation of the one‐dimensional axial diffusion model in an open‐source platform, that is, FEniCS, to explore the influence of the axial dispersion on the reaction yield‐to‐time profile. Then, we built an automatic platform, which included a photomicroreactor, a continuously controlled pump, a high‐power UV‐LED light source, an in‐line visible‐light absorbance analytical unit, and a Raspberry Pi based controlling unit. Moreover, steady‐state feeding and sampling functions could be realized in this continuous‐flow photochemical platform. The homogeneous photolysis of methylene blue and the photo‐Favorskii rearrangement synthesis of ibuprofen as the model reactions were used to validate the robustness of this automatic platform with unsteady‐state and steady‐state operations, through which kinetic study was carried out using genetic algorithm based symbolic regression, leading to deep understanding on reaction mechanisms and benefits for process optimization.