Determining the optimal conditions of a complex chemical reaction network using traditional batch experimentation is a costly and time consuming task. However, microreactors are powerful tools for optimization studies due to the continuous flow operations as well as the easy integration of physical and chemical sensors.
A silicon microreactor system was developed to determine the optimal conditions of a chemical reaction by varying several process parameters. Components of this platform include a pneumatic flow system for fluid delivery, thermoelectric modules for heating/cooling, and UV/Vis spectroscopy for online chemical species detection. Flow and temperature control algorithms were integrated with a novel combination of traditional optimization approaches and statistical methods derived from design of experiment (DOE) to perform automated optimization. Specifically, we look at the simplex and gradient-based methods as means to find the optimal conditions while requiring the fewest number of experiments.
We apply the system experimentally to study the optimal conditions of a series reaction – the oxidation of benzyl alcohol to benzaldehyde with further oxidation to benzoic acid. In this reaction network, the automated microreactor platform maximizes the yield of benzaldehyde by varying the reaction temperature, the residence time, and the stoichiometric ratio of reagents.