Christine Schmaus
| Position | PhD student |
|---|---|
| College | Robinson |
| cs587@cam.ac.uk | |
| Telephone | +44 (0)1223 (7)66339 |
| Research group | Magnetic Resonance, Catalysis |
| Research project title | Fighting Emissions with MRI |
| Supervisor | Prof Gladden |
| Advisor | Dr Holland |
Research description
To reduce carbon dioxide emissions generated by the chemical industry the energy demand of chemical production processes has to be reduced. This can be achieved by enhancement of reactor performance, which also leads to increased yields, suppression of undesired side-products and more efficient use of catalysts. Reactor optimisation requires a profound understanding of the factors which influence the reaction and often relies on numerical models.
This research project focuses on trickle-bed reactors. These packed bed reactors with co-current gas and liquid flow find wide application in hydrogenation and oxidation reactions in the chemical and petrochemical industry. As their performance is not only governed by reaction kinetics, but also by mass transfer limitations and local-scale hydrodynamic phenomena, optimisation and modelling is rather complex. Phenomena on various local scales including transitions between different flow regimes, distortions caused by local packing heterogeneities, stagnant liquid zones, particle wetting efficiency, prewetting effects and hysteresis have to be taken into account. So far, modelling has largely relied on empirical correlations.
Magnetic resonance imaging has successfully been used to gain insight into the above-mentioned local scale hydrodynamic phenomena and thus has contributed to the understanding of their influence on overall reactor performance. It has the potential of providing the detailed local-scale information needed to evaluate reactor models and put them on a more phenomenological basis. The development of fast magnetic resonance imaging techniques has significantly reduced data acquisition times and thus increased the amount of detail available. Further potential for the reduction of data acquisition times is seen in the use of new data sampling strategies and image reconstruction algorithms based on Bayesian signal processing.
In this first phase of my PhD project I am exploring and evaluating existing velocity imaging techniques and modelling strategies for trickle-bed reactors. We will then identify flow phenomena that require further examination in order to base reactor modelling on the understanding of small-scale physical processes and, in collaboration with Microsoft Research Cambridge, develop the imaging and data processing techniques necessary to acquire this information in sufficient detail. This will be a step towards providing industry with the tools to significantly reduce carbon dioxide emissions.