Dataset underlying the research of: new insights into the fluidisation characteristics of granular activated carbon for drinking water treatment applications

doi:10.4121/14229863.v1
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/14229863
Datacite citation style:
Kramer, Onno (2021): Dataset underlying the research of: new insights into the fluidisation characteristics of granular activated carbon for drinking water treatment applications. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/14229863.v1
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Dataset
Granular activated carbon (GAC) filtration is a very important treatment unit operation in drinking water production processes. GAC filtration is widely used for its filtration and adsorption capabilities as a barrier for undesired macro and micro-pollutants. GAC filtration consists of two ascending procedures: filtration procedure, capturing the impurities from the water, in conjunction with a backwash procedure, flushing these impurities out of the system. The prediction of the bed expansion of GAC is complex since the particles are non-spherical, porous and polydisperse. It is complicated to find GAC particle properties such as the wet density, wet mass and the minimum fluidisation porosity. To be able to predict the porosity these values must be known.This data set contains numerical data of fluidisation experiments for various flow rates, water temperatures for nine different GAC types. In addition, a large data-set is shared with drying log (evaporation) values. Also, photographic and video material as well as morphological measurements (Camsizer, ImageJ, sieve and microscope) are provided.
history
  • 2021-03-17 first online, published, posted
publisher
4TU.ResearchData
organizations
Delft University of Technology, Faculty of Civil Engineering and Geosciences, Department of Water Management
Delft University of Technology, Faculty of Mechanical, Maritime and Materials Engineering, Department of Process and Energy
Waternet, Amsterdam (funder)
HU University of Applied Sciences Utrecht, Institute for Life Science and Chemistry
Queen Mary University of London, Division of Chemical Engineering, School of Engineering and Materials Science

DATA

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