Data-driven Surrogate Modeling for Computational Fluid Dynamics in Simulating Spray Fluidized Bed Granulation

This paper presents a data-driven surrogate modeling methodology for computational fluid dynamics (CFD) to deliver a more accurate and efficient modeling for simulating segmented simulating spray fluidized bed granulation (SFBG) which fully considers the fluidization dynamics of being in actual production. Specifically, a data-driven surrogate model is developed for replacing the actual CFD simulations to calculate the time-varying fluidization parameters, and then integrating with a two-compartmental population balance model (TCPBM) to formulate a surrogate-assisted CFD-TCPBM coupled simulation modeling framework. The proposed surrogate-assisted methodology is illustrated by its application to a case study, in which the simulation results indicate the feasibility and effectiveness of surrogate-assisted CFD in simulating SFBG compared to actual CFD simulations. Therefore, one can conclude that the proposed methodology has great potential to deliver more accurate and realistic results in simulating SFBG of being in actual production.