The Use of Optimized Restitution Coefficients to Improve Residence Time Prediction in Computational Fluid Dynamics-Discrete Parcel Method Models for Counter-Current Spray Dryers

Abstract

This work studies the dispersion of solids in the cold isothermal operation of swirl counter current spray dryers. Residence time distributions (RTDs) of glass beads and detergent powder are obtained in a semi-industrial unit under varying Reynolds and injection positions and validated with the results of a novel Reynolds average Navier Stokes-discrete parcel method (DPM) framework. The simulations stress that the particle RTD is governed to a large extent by the interaction of the solids with the walls, which is usually simplified with the assumption of a particle-wall restitution coefficient. Since this is often unavailable experimentally, here, we propose an alternative combined model that integrates the computational fluid dynamics (CFD)-DPM model with reinforcement learning using a training set of experimental RTDs to extract an "effective pair of wall restitution coefficients". The method improves the accuracy of existing CFD platforms, reducing the errors in the mean residence time from 30-100% to <25%.