Simulation of Fluid-Bed Spray Granulation using rCFD-DEM

Fluid-bed granulation is the process of growing small particles into granules by spraying solids-containing liquids. It is an important process step in the production of various consumer-facing products like fertilizer, laundry detergent and food products that have to be of consistent quality. Industrial-scale granulators are notoriously difficult to build and prone to design flaws such as overwetting due to cold spots, leading to segregation and formation of clumps. The granulation process can be simulated by treating each particle as an individual element and solving the Newtonian equations of motion, as well as mass and energy balances over small time steps. However, when simulating large-scale granulators, the process becomes extremely computationally intensive due to the small time steps required to resolve individual particle collisions that occur on the order of milliseconds while the time scale of the process is on the order of hours. Recurrence Computational Fluid Dynamics (rCFD) is a novel technique that takes previously computed, highly resolved patterns and extrapolates the results, resolving mass and energy balances that do not require these small time steps. This technique can reduce the required computation time by approximately three orders of magnitude. TBC

Faculty Supervisor:

Jan Kopyscinski

Student:

Partner:

Technische Universität Hamburg

Discipline:

Engineering

Sector:

Education

University:

McGill University

Program: