Modelling the evolution of particle size distributions in chemical precipitation processes

The application of population balance models (PBM) provides an excellent method to study the variation in particle properties (size, density …) occurring in chemical precipitation and crystallization processes. In view of reactor design and optimization, these particle property distributions are described applying an intelligent coupling of modelling chemical reactions, PBM and mixing behaviour. The model is calibrated and validated based on detailed particle size distribution (PSD) time

Precipitation processes are omnipresent in chemical reaction engineering. These precipitation processes are often the result of chemical reactions occurring when two different streams are mixed, resulting in super-saturation. This super-saturation induces the occurrence of nucleation, continuous growth and agglomeration of particles. These processes, in combination with breakage, typically result in a variety of particles which can differ in size, composition, density and other physical-chemical properties. This variety conflicts with the specified product requirements, which are determined by subsequent processes (such as the separation) or the application domain.<\p>

The chemical process consists of multiple reactions (redox reactions, acid-base reactions, ion pairing …), which may influence the environmental conditions, which may in turn also influence the final product, and hence the product quality. Despite the high complexity of the chemical reactions, its modelling has been common practice in industry. The precipitation reactions (nucleation, continuous growth and agglomeration), on the other hand, are not as well understood and the modelling has not yet reached its full potential. <\p>

Population balance models (PBM) provide an excellent method to study the variation in property size. However, in view of the complexity of the system it is important to make a smart decision on model complexity. This decision on model complexity is required not to overcomplicate the model and not to render an unsolvable model. In this project PBMs are combined in an intelligent way to also include the mixing behaviour in the modeling effort<\p>

Project duration: 2017-11-01 - 2017-12-31

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Youri Amerlinck
Department of Applied Mathematics, Biometrics and Process Control
Coupure Links 653
9000 Gent
Tel: 09 264 59 35

Last update: 01 december 2008,

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