1. Analysing the effect of screw configuration using a stochastic twin-screw granulation model
- Author
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Eric J. Bringley, Nick A. Eaves, Markus Kraft, Robert I. A. Patterson, Andrew D. McGuire, Sebastian Mosbach, Gavin K. Reynolds, Jochen A.H. Dreyer, Mosbach, Sebastian [0000-0001-7018-9433], Bringley, Eric [0000-0003-4101-4874], Dreyer, Jochen [0000-0002-2168-5884], Kraft, Markus [0000-0002-4293-8924], Apollo - University of Cambridge Repository, and School of Chemical and Biomedical Engineering
- Subjects
Granulation ,General Chemical Engineering ,Population ,Nucleation ,02 engineering and technology ,Industrial and Manufacturing Engineering ,020401 chemical engineering ,Breakage ,0204 chemical engineering ,education ,Mathematics ,Coalescence (physics) ,education.field_of_study ,Twin-screw ,Consolidation (soil) ,Applied Mathematics ,Chemical engineering [Engineering] ,General Chemistry ,Mechanics ,021001 nanoscience & nanotechnology ,Stochastic ,Population balance ,Particle-size distribution ,Layering ,0210 nano-technology - Abstract
In this work, a framework for modelling twin-screw granulation processes with variable screw configurations using a high-dimensional stochastic population balance method is presented. A modular compartmental approach is presented and a method for estimating residence times for model compartments based on screw element geometry is introduced. The model includes particle mechanisms for nucleation, primary particle layering, coalescence, breakage, and consolidation. A new twin-screw breakage model is introduced, which takes into account the differing breakage dynamics between two types of screw element. Additionally, a new sub-model for the layering of primary particles onto larger agglomerates is presented. The resulting model is used to simulate a twin-screw system with a number of different screw configurations and the predictive power of the model is assessed through comparison with an existing experimental data set in the literature. For most of the screw configurations simulated, the model predicts the product particle size distribution at large particle sizes with a reasonable degree of accuracy. However, the model has a tendency to over-predict the amount of fines in the final product. Nevertheless, the model qualitatively captures the reduction in fines associated with an increase in the number of kneading elements, as observed experimentally. Based on model results, a number of key areas for future model development are identified and discussed. National Research Foundation (NRF) Accepted version ADM acknowledge funding from EPSRC Grant 1486478 and AstraZeneca. ADM would also like to thank C.T. Lao for useful conversations regarding the implementation of the nucleation process. RP acknowledges support from the Deutsche Forschungsgemeinschaft (DFG) through grant CRC 1114 ‘‘Scaling Cascades in Complex Systems”, Project C08. This project was partly funded by the National Research Foundation (NRF), Prime Minister’s Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme.
- Published
- 2019