Micromixing Performance and Residence Time Distribution in a Miniaturized Magnetic Reactor: Experimental Investigation and Machine Learning Modeling
Qingchuan Chen, Jian Deng, Guangsheng Luo
Abstract
Miniaturization of mixers is a hot topic in process intensification, but efficient mixing at operational conditions with relatively large viscosity μ and large flow rate ratio R between two mixed solutions is still a great challenge for micromixers. Herein, a miniaturized magnetic reactor (MMR) is developed, and its micro- and macromixing performances are characterized by the Villermaux/Dushman reaction and residence time distribution experiments, respectively. The results show that the miniaturization of the premixing unit and the stirred unit is vital. By arranging the stirred units in series, the MMR is effective in reducing axial dispersion, intensifying mixing at operational conditions with large μ and R and achieving efficient energy utilization without introducing extra energy input. The micromixing time t m in the optimized MMR ranges from 0.24 to 1.42 ms when 4.72 mPa·s < μ < 73.30 mPa·s and 8 < R < 20, indicating superior micromixing performance. Moreover, machine learning models are established and compared for predicting t m with excellent accuracy and guiding mixer design, and the SHAP method is adopted for model interpretation.