|Funding for:||UK Students, EU Students, Self-funded Students|
|Funding amount:||EPSRC stipend and UK/EU fees paid|
|Placed On:||31st July 2018|
|Closes:||30th September 2018|
Academic Supervisors: F. Alberinia, A.W. Nienowa
Industrial Supervisors: F.Chitib ,W. Eaglesb , J. Thomasc.
a School of Chemical Engineering, University of Birmingham, B15 2TT, UK;
bGlaxoSmithKline Services Unlimited, 980 Great West Road, Brentford, Middlesex TW8 9GS;
c M-Star Simulations, LLC;
Funding: EPSRC stipend and UK/EU fees paid
Physical properties of pharmaceutical crystals can be influenced by both the crystallisation process as well as by potential attrition during agitated drying. During crystallisation, amongst other factors, the crystal particle size may be influenced by degree of super-saturation, seed concentration and agitation within the crystalliser. Whilst some factors are relatively straightforward to keep constant on scale up and across different mixing vessels, local energy dissipation around the impeller is likely to be a function of crystalliser geometry, fill level and scale of operation. Computational fluid dynamics (CFD) is a proven tool for predicting aspects of fluid behaviour in a system such as local energy dissipation. In stirred tanks it can successfully predict flow patterns and provide a useful indication of other phenomena for common geometries used across many industries. However, this is mostly true for single phase simulations and standard geometries/vessel configurations. When multiphase systems and non- standard configurations are investigated numerically, usually the computational effort increases exponentially and relevant literature with reliable experimental data is usually scarce when attempting to validate numerical results.
The project aims to address the gap that exists in the literature, aiming to predict power numbers for non-standard geometries; this might include low fill vessels or poorly baffled designs. In particular, generating a CFD prediction using M-star DMT software which has the great advantage of a time-accurate representation of the fluid flow, delivering crucial insights needed for modern engineering analysis. This will be validated against lab work proving the viability of this approach. Experimentally, techniques to map out the fluid and particle dynamics, e.g. PIV (Particle Image Velocimetry) and PEPT (Positron Emission Particle Tracking), will be used in this project.
Validating numerical results with experimental data will potentially lead to the derivation of scale up modelling criteria which can be used during process development and optimisation. This would be extended to a range of impeller types and blade numbers to understand how crystallisations that are sensitive to impeller energy dissipation may be scaled. Such an approach would aid the development and filing of a control strategy to control particle size for crystallisation processes.
This project would suit an applicant with a 1st class or 2i Honours Engineering degree, good mathematical and analytical skills, MATLAB capabilities, wishing to pursue an experimental and computational PhD and interest in fluid dynamic applied to industrial cases.
Informal enquiries can be made to Dr. Federico Alberini firstname.lastname@example.org
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