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Title: A three-dimensional population balance model of granulation processes employing mechanistic kernels
Author: Poon, Jonathan Ming-Hin
ISNI:       0000 0001 3494 6943
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2008
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Granulation is a process for agglomeration where powder material is combined with liquid binder solution to facilitate the formation of larger, free-flowing granules. Granulation has become a mainstream process amongst the industries with applicability in numerous areas, which include the pharmaceuticals, mineral processing, fertilisers and in the production of a range of commodity products. A major driVing force for the production of granules from their ungranulated counterparts arises from the economic savings Le., increased bulk density permits savings to be made in transportation. and storage. Furthermore, granules may be tailored to possess certain desirable attributes that will suit the final application of the granules. Granulation is an example of a process that exhibits complex interactions between the underlying granulation phenomena such as nucleation, consolidation, aggregation and breakage. In addition, the granUle properties are distributed heterogeneously across the entire particle population posing as a particular challenge in generating a mathematical model that is able to accurately describe the granulation behaviour. The modelling approach used in this study is different from common practices, which tend to rely on heuristics and empiricism for the operation of the granulation process. This empirical approach signifies a disconnect from our understanding of the underlying physics of the process, which poses as a impediment towards the efficient operation of granulation processes. The work presented in this thesis attempts to address this disconnect by applying a threedimensional population balance with mechanistic representations for the underlying granulation rate processes. The population balance framework is ideally suited for this particular process, as it enables the evolution of the granules to be tracked with respect to differentiating particle traits, e.g. the granule size distribution. The selection of the desired properties is influenced by the importance of these particle properties on the end granule product, and also by their influence on key process mechanisms. A novel mechanistic nucleation kernel is developed incorporating fundamental material properties pertaining to the powder substrate and the liquid binder solution. The model form of the nucleation kernel is formulated by drawing a parallel with the collision/transition state theory. There are few literature reports on the inclusion of nucleation phenomena in the population balance models of granulation processes, let alone a mechanistic nucleation model. This study is one of the first in this regard. The recent recognition of the importance of the wetting kinetics and the nucleation thermodynamics on the nucleation phenomenon has been factored into the nucleation kernel by explicitly accounting for the effects of the liquid flow rate and the physicochemical properties of the material properties (surface tension, contact angle, and spreading coefficient). Batch granulation experiments were conducted obtaining granule measurements with respect to the size distribution, porosity and fractional binder content. Preliminary results for the validation of the population balance model with the experiment-measurements showed a good agreement, providing partial albeit valuable validation of the population balance model. This is also one of the first studies to model and validate a three-dimensional population balance model for granulation. Model based analyses were also carried out under a variety of processing conditions, which included the effects of changing formulations, droplet size effects, feed size distribution and the effects of powder and binder properties. The proposed model demonstrated the interactions for a range of feed formulations in tandem with granulating operating conditions, establishing qualitative agreement with similar findings derived from past experimental studies.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available