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This collection belongs to the doctoral thesis "Nonlinear Parameter Estimation of Experimental Cake Filtration Data". Most of the content are Jupyter notebooks which contain the Python code which reproduces graphs found in the thesis from the original experimental data. The lab practice dataset that contains 500 filtration experiments is contained in the folder for section 3.5. The notebooks are not strictly sorted by section. At any rate, the Readme will guide you to the notebook which produces a certain graph, if it is not part of the main notebook of that specific section. The original Python environment was set up with Anaconda. Please use the provided .yml file to create a Python environment which contains all the necessary packages. Some notebooks may not work with the most current versions of the packages, so updating is not necessarily a good idea.
including - 1 LARGE sample with low-resolution data (micro-CT, VERSA510) - splitted in I, II, III, IV, V - 3 SUB-Samples MEDIUM-resolution (cutted down from above LARGE sample, scanned with micro-CT, ZEISS VERSA510) - 3 SUB-Samples HIGH-resolution (same as MEDIUM, scanned with nano-CT, ZEISS ULTRA810)
Glass Particles (soda-lime glass), particle size distribution 5 to 50 µm
Prediction of micro processes, filter cake build-up and porous media flow is a key challenge to describe macroscopic parameters like filter cake resistance. This is based on a precise description, not only of the disperse solid fraction, but the distributed properties of the voids between the particles. Lab-experiments are carried outwith alumina and limestone,which differ in particle size distribution (PSD) and resulting filter cake structure. Filter cakes of bothmaterials are characterized by standardized lab tests and additionally, alumina cakes aremeasured with X-ray microscopy (XRM). Focusing on distributed process key parameters, the data gives a deeper understanding of the laboratory experiments. The solid volume fraction inside the feed strongly influences the particle sedimentation and leads typically to a top layer formation of fine particles in the final filter cake,which has a negative influence on subsequent process steps. The top layers seal the filter cake for washing liquid and increase the capillary entry pressure for gas differential pressure de-watering. The influence on cake structure can be seen in a change of porosity, particle size and shape distribution over the height of the filter cake. In all measurements, homogenous filter cake structures could only be achieved by increasing the solid volume fraction inside the suspension above a certain percentage, at which particle size related sedimentation effects could be neglected and only zone sedimentation occurred. XRM offers the chance to quantify these effects, which previously could only be described qualitatively.
A more thorough understanding of the properties of bulk material structures in solid–liquid separation processes is essential to understand better and optimize industrially established processes, such as cake filtration, whose process outcome is mainly dependent on the properties of the bulk material structure. Here, changes of bulk properties like porosity and permeability can originate from local variations in particle size, especially for non-spherical particles. In this study, we mix self-similar fractions of crushed, irregularly shaped Al2O3 particles (20 to 90 μm and 55 to 300 μm) to bimodal distributions. These mixtures vary in volume fraction of fines (0, 20, 30, 40, 50, 60 and 100 vol.%). The self-similarity of both systems serves the improved parameter correlation in the case of multimodal distributed particle systems. We use nondestructive 3D X-ray microscopy to capture the filter cake microstructure directly after mechanical dewatering, whereby we give particular attention to packing structure and particle–particle relationships (porosity, coordination number, particle size and corresponding hydraulic isolated liquid areas). Our results reveal widely varying distributions of local porosity and particle contact points. An average coordination number (here 5.84 to 6.04) is no longer a sufficient measure to describe the significant bulk porosity variation (in our case, 40 and 49%). Therefore, the explanation of the correlation is provided on a discrete particle level. While individual particles < 90 μm had only two or three contacts, others > 100 μm took up to 25. Due to this higher local coordination number, the liquid load of corresponding particles (liquid volume/particle volume) after mechanical dewatering increases from 0.48 to 1.47.