Flow regime, hydrodynamics, floc size distribution and sludge properties in activated sludge bubble column, air-lift and aerated stirred reactors

This paper presents a comparative study how reactor configuration, sludge loading and air flowrate affect flow regimes, hydrodynamics, floc size distribution and sludge solids-liquid separation properties. Three reactor configurations were studied in bench scale activated sludge bubble column reactor (BCR), air-lift reactor (ALR) and aerated stirred reactor (ASR). The ASR demonstrated the highest capacity of gas holdup and resistance, and homogeneity in flow regimes and shearing forces, resulting in producing large numbers of small and compact floes. The fluid dynamics in the ALR created regularly directed recirculation forces to enhance the gas holdup and sludge flocculation. The BCR distributed a high turbulent flow regime and non-homogeneity in gas holdup and mixing, and generated large numbers of larger and looser floes. The sludge size distributions, compressibility and settleability were significantly influenced by the reactor configurations associated with the flow regimes and hydrodynamics.

Numerical simulation of magnetite segregation in a dense medium cyclone

Numerical simulations of turbulent driven flow in a dense medium cyclone with magnetite medium have been conducted using Fluent. The predicted air core shape and diameter were found to be close to the experimental results measured by gamma ray tomography. It is possible that the Large eddy simulation (LES) turbulence model with Mixture multi-phase model can be used to predict the air/slurry interface accurately although the LES may need a finer grid. Multi-phase simulations (air/water/medium) are showing appropriate medium segregation effects but are over-predicting the level of segregation compared to that measured by gamma-ray tomography in particular with over prediction of medium concentrations near the wall. Further, investigated the accurate prediction of axial segregation of magnetite using the LES turbulence model together with the multi-phase mixture model and viscosity corrections according to the feed particle loading factor. Addition of lift forces and viscosity correction improved the predictions especially near the wall. Predicted density profiles are very close to gamma ray tomography data showing a clear density drop near the wall. The effect of size distribution of the magnetite has been fully studied. It is interesting to note that the ultra-fine magnetite sizes (i.e. 2 and 7 mu m) are distributed uniformly throughout the cyclone. As the size of magnetite increases, more segregation of magnetite occurs close to the wall. The cut-density (d(50)) of the magnetite segregation is 32 gm, which is expected with superfine magnetite feed size distribution. At higher feed densities the agreement between the [Dungilson, 1999; Wood, J.C., 1990. A performance model for coal-washing dense medium cyclones, Ph.D. Thesis, JKMRC, University of Queensland] correlations and the CFD are reasonably good, but the overflow density is lower than the model predictions. It is believed that the excessive underflow volumetric flow rates are responsible for under prediction of the overflow density. (c) 2006 Elsevier Ltd. All rights reserved.

CFD simulations of hydrocyclones with an air core - Comparison between large eddy simulations and a second moment closure

CFD simulations of the 75 mm, hydrocyclone of Hsieh (1988) have been conducted using Fluent TM. The simulations used 3-dimensional body fitted grids. The simulations were two phase simulations where the air core was resolved using the mixture (Manninen et al., 1996) and VOF (Hirt and Nichols, 1981) models. Velocity predictions from large eddy simulations (LES), using the Smagorinsky-Lilly sub grid scale model (Smagorinsky, 1963; Lilly, 1966) and RANS simulations using the differential Reynolds stress turbulence model (Launder et al., 1975) were compared with Hsieh's experimental velocity data. The LES simulations gave very good agreement with Hsieh's data but required very fine grids to predict the velocities correctly in the bottom of the apex. The DRSM/RANS simulations under predicted tangential velocities, and there was little difference between the velocity predictions using the linear (Launder, 1989) and quadratic (Speziale et al., 1991) pressure strain models. Velocity predictions using the DRSM turbulence model and the linear pressure strain model could be improved by adjusting the pressure strain model constants.