Ramin, Elham1; Wágner, Dorottya Sarolta2; Yde, Lars4; Binning, Philip John5; Rasmussen, Michael R.6; Mikkelsen, Peter Steen1; Plósz, Benedek G.2
1 Department of Environmental Engineering, Technical University of Denmark2 Urban Water Engineering, Department of Environmental Engineering, Technical University of Denmark3 Water Resources Engineering, Department of Environmental Engineering, Technical University of Denmark4 DHI Denmark5 Office for Study Programmes and Student Affairs, Administration, Technical University of Denmark6 Aalborg University
Secondary settling tanks (SSTs) are the most hydraulically sensitive unit operations in biological wastewater treatment plants. The maximum permissible inflow to the plant depends on the efficiency of SSTs in separating and thickening the activated sludge. The flow conditions and solids distribution in SSTs can be predicted using computational fluid dynamics (CFD) tools. Despite extensive studies on the compression settling behaviour of activated sludge and the development of advanced settling velocity models for use in SST simulations, these models are not often used, due to the challenges associated with their calibration. In this study, we developed a new settling velocity model, including hindered, transient and compression settling, and showed that it can be calibrated to data from a simple, novel settling column experimental set-up using the Bayesian optimization method DREAM(ZS). In addition, correlations between the Herschel-Bulkley rheological model parameters and sludge concentration were identified with data from batch rheological experiments. A 2-D axisymmetric CFD model of a circular SST containing the new settling velocity and rheological model was validated with full-scale measurements. Finally, it was shown that the representation of compression settling in the CFD model can significantly influence the prediction of sludge distribution in the SSTs under dry- and wet-weather flow conditions.
Water Research, 2014, Vol 66, p. 447-458
Activated sludge; Compression; Calibration; Computational fluid dynamics; Rheology; Monte Carlo Markov Chain