In this thesis, a thorough study of the batch top-spray fluidized bed granulation was carried out including experimental study, population balance model (PBM), computational fluid dynamic (CFD) study and control strategy development. For the experimental study, the influence variables of pulsed frequency, binder spray rate and atomization pressure of a batch top-spray fluidized bed granulation process were studied using the Box-Behnken experimental design method. Different mathematical models were developed to predict the mean size of granules, yield, relative width of granule distribution, Hausner ratio and final granule moisture content. Validation experiments have shown the reliability and effectiveness of using the Box-Behnken experimental design method to study a fluidized bed granulation process. The one-dimensional population balance models (ODPBMs) have been developed to model a pulsed top-spray fluidized bed granulation, linking the operating factors of the pulsed frequency, the binder spray rate, and atomization air pressure with the granule properties to predict granule growth behavior at different operating conditions. A multi-stage open optimal control strategy based on the developed ODPBMs was proposed to reduce the model and process mismatch through adjusting the trajectory of the evolution of the granule size distribution at predefined sample intervals. The effectiveness of the proposed modeling and multi-stage open optimal control strategy has been validated by experimental and simulation tests. In addition, an Eulerian-Eulerian two-fluid model (EETFM) was developed to describe the gas-particle two-phase flow in the fluidized bed granulator. By computational fluid dynamic analysis, it has been proven that the fluidized bed granulation system is not homogeneous, based on which a two-compartmental population balance model (TCPBM) was developed to describe the particle growth in the fluidized bed granulation. Validation experiments have shown the effectiveness and superior accuracy of the TCPBM comparing with the ODPBM in predicting the final particle size distribution.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:650166 |
| Date | January 2014 |
| Creators | Liu, Huolong |
| Publisher | De Montfort University |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/2086/11006 |
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