Effect of Scale-Up on Mass Transfer and Flow Patterns in Liquid–Liquid Flows Using Experiments and Computations

Abstract

Liquid−liquid microchannels have high mass transfer rates but low throughput. To increase productivity, they can be scaled up by increasing the diameter. Therefore, predicting flow patterns and mass transfer rates while accounting for solvent effects as the diameter varies is crucial; however, this topic is currently lacking in the literature. We develop random forest and symbolic genetic regression machine learning (ML) models to predict flow patterns and the mass transfer rate, respectively, using a combination of our experimental and computational fluid dynamics (CFD) data and literature-mined data, while accounting for the effects of solvent properties and channel diameter. This enables rapid prediction for efficient scale-up of microchannels to millichannels. To minimize the number of CFD simulations and maximize the model accuracy, we employ active learning techniques. Furthermore, we quantify the uncertainty of the ML models built on the hybrid data.