RPB simulation predictive model and optimization analysis

Abstract

The rotating packed bed (RPB) has significant potential for improving reaction rates and mass transfer efficiency, but its complex flow dynamics remain incompletely understood. This study presents experimental data on the dry pressure drop at various rotor speeds and gas flow rates, providing a basis for theoretical model development. Computational fluid dynamics (CFD) was used for modelling and analysis, with lattice independence verified by a logistic regression model. A modified porous media model was used to analyze the gas phase flow within the RPB chamber. The corrected predictive model showed an average deviation of 4.71 % from the experimental dry pressure drop data. Further structural optimization analysis showed that using a composite inverse rotor (CIR) significantly increased the average turbulent kinetic energy. In addition, the relationship between turbulent kinetic energy and pressure drop was investigated by varying the position coefficients. The results provide significant theoretical insights for optimizing RPB structure and improving mass transfer efficiency in industrial applications.