Daniel Holland

Associate ProfessorDaniel Holland

Link Rm 410
Internal Phone: 93785

Qualifications

Research Interests

The focus of my research is to provide new insight into the fundamentals of chemical processes by using the latest experimental techniques. Chemical engineering relies on simple models and heuristic equations to enable the design of processes for a diverse array of industries including, for example, petrochemicals, fertilisers, pharmaceuticals and dairy. Whilst this approach is successful for established technology, it is rarely optimal for new processes. The conventional design approach has evolved in part because instruments are normally restricted to observing what goes into and what comes out of a process; very little can be observed about what is actually happening within the process. Recently, new technologies have become available that permit us to "see inside" chemical processes. The development and use of these technologies forms the basis of my research.

These new technologies are used to improve our fundamental understanding of processes and to optimise computational modelling of processes throughout the chemical industries. I am especially interested in using these technologies to study processes that require the handling of particles, however I also work on non-Newtonian, gas-liquid and liquid-liquid flows, diffusion, and mass transfer processes and modelling of chemical reactors.

Recent Publications

  • Clarke DA., Fabich HT., Brox TI., Galvosas P. and Holland DJ. (2019) On the influence of rotational motion on MRI velocimetry of granular flows – Theoretical predictions and comparison to experimental data. Journal of Magnetic Resonance 307 http://dx.doi.org/10.1016/j.jmr.2019.106569.
  • Fullard L., Holland DJ., Galvosas P., Davies C., Lagrée PY. and Popinet S. (2019) Quantifying silo flow using MRI velocimetry for testing granular flow models. Physical Review Fluids 4(7) http://dx.doi.org/10.1103/PhysRevFluids.4.074302.
  • Matviychuk Y., Bostock MJ., Nietlispach D. and Holland DJ. (2019) Time-domain signal modelling in multidimensional NMR experiments for estimation of relaxation parameters. Journal of Biomolecular NMR 73(3-4): 93-104. http://dx.doi.org/10.1007/s10858-018-00224-2.
  • Matviychuk Y., Yeo J. and Holland DJ. (2019) A field-invariant method for quantitative analysis with benchtop NMR. Journal of Magnetic Resonance 298: 35-47. http://dx.doi.org/10.1016/j.jmr.2018.11.010.
  • Clarke DA., Sederman AJ., Gladden LF. and Holland DJ. (2018) Investigation of Void Fraction Schemes for Use with CFD-DEM Simulations of Fluidized Beds. Industrial and Engineering Chemistry Research 57(8): 3002-3013. http://dx.doi.org/10.1021/acs.iecr.7b04638.