Paul Kieckhefen is a Computational Research Engineer in the chemical industry and was formerly a researcher at Hamburg University of Technology (TUHH). His interests include developing novel data-driven modeling techniques for old-school process industry challenges.
Dissertation: A Novel Method for Predicting Product Properties in Fluidized Bed Spray Granulation
In this work, a method to predict the surface structures of particles produced by fluidized bed spray layering granulation using the CFD-DEM method was developed. A simple state-variable/event tracking approach was implemented to capture indirect quantifiers of the progression of structure-forming microprocesses. The approach was tested on a demonstration case where a particle core is coated with sodium benzoate solution. The experiment was scaled-up by a factor of eight to pilot-scale using the developed method.
This work provides a method for utilizing particle-scale simulations method like the coupled Computational Fluid Dynamics-Discrete Element Method (CFD-DEM) to predict the properties of particles produced in scaled-up apparatuses based on laboratory-scale experiments by correlating the conditions that particle experience to the properties they develop.
Spray coating in spouted bed occurs on the time-scale of hours, but the most sophisticated simulation methods can only cover minutes per day. The Recurrence CFD (rCFD) method was used to get around this time-scale issue by replacing computational complexity with memory complexity.