Daniel Holland

Associate ProfessorDaniel Holland

Director of Second Professional Year Studies
Link Rm 410
Internal Phone: 93785


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., 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.
  • Li K., Chandrasekera TC., Li Y. and Holland DJ. (2018) A Non-Linear Reweighted Total Variation Image Reconstruction Algorithm for Electrical Capacitance Tomography. IEEE Sensors Journal 18(12): 5049-5057. http://dx.doi.org/10.1109/JSEN.2018.2827318.
  • Bostock MJ., Holland DJ. and Nietlispach D. (2017) Improving resolution in multidimensional NMR using random quadrature detection with compressed sensing reconstruction. Journal of Biomolecular NMR 68(2): 67-77. http://dx.doi.org/10.1007/s10858-016-0062-9.
  • Bostock MJ., Holland DJ. and Nietlispach D. (2017) Compressed Sensing ℓ1-Norm Minimisation in Multidimensional NMR Spectroscopy. In Mobli M; Hoch JC (Ed.), Fast NMR Data Acquisition: Beyond the Fourier Transform: 267-303. http://dx.doi.org/10.1039/9781782628361-00267.
  • Boyce CM., Ozel A., Rice NP., Rubinstein GJ., Holland DJ. and Sundaresan S. (2017) Effective particle diameters for simulating fluidization of non-spherical particles: CFD-DEM models vs. MRI measurements. AIChE Journal 63(7): 2555-2568. http://dx.doi.org/10.1002/aic.15623.