Rapid characterization of flow regimes in micro-packed bed reactors utilizing the convolutional neural network

Abstract

Micro-packed bed reactors (μPBRs), which are widely used in multiphase reactions, have the advantages of high mass transfer efficiency and excellent safety. However, the identification of flow behavior in μPBRs with various packings remains a challenge. Rapid characterization of flow regimes needs to be taken into consideration for improving reactor research efficiency. In this work, a transfer learning conventional neural network (CNN) based on LeNet-5 was developed to recognize the flow regime of μPBRs for the first time. Micropillars and spherical particles as typical packings were employed to inspect the applicability of the model successively. The flow regimes of μPBRs with micropillar structure and spherical particles were classified using a trained transfer learning model based on LeNet-5, obtaining high accuracies of 97.5% and 94.3%, respectively. Notably, a highly integrated software platform coupling the trained CNN for analyzing flow regime with a user-friendly graphical interface was constructed, achieving online acquisition and analysis of data efficiently.