PubAg

Main content area

Modelling of the rheological behavior of mechanically dewatered sewage sludge in uniaxial cyclic compression

Author:
Liang, Fenglin, Sauceau, Martial, Dusserre, Gilles, Dirion, Jean-Louis, Arlabosse, Patricia
Source:
Water research 2018 v.147 pp. 413-421
ISSN:
0043-1354
Subject:
basins, confidence interval, drying, mechanical testing, models, sewage sludge, texture, viscoelasticity
Abstract:
The rheological behavior of mechanically dewatered sewage sludges is complex but essential as it affects almost all treatment, utilization and disposal operations, such as storage, pumping, land-spreading, or drying. In this work, a specific methodology coupling experiments and modelling is developed to characterize the rheological and textural properties of highly concentrated sludge. The experimental part based on a uniaxial compression method has been presented in a previous paper (Liang et al., 2017). This article is dedicated to the modelling part, which includes the behavior identification and the parameters optimization. Previous and additional mechanical tests allow the identification of a visco-elasto-plastic behavior. This behavior is then modelled with a Burgers-Ludwik model, with 7 rheological parameters. This model is able to simulate the viscoelastic behavior of sludge under the yield stress, and the visco-elasto-plastic hardening behavior over the yield stress. The optimization of model parameters is carried out in two steps and relies on the calculation of basins of attraction and confidence intervals with initial conditions estimated from the mechanical tests. Finally, the entire characterization methodology, from experimental mechanical tests to model parameter optimization, is applied to sludge samples at different operating conditions and structural states. The determination of the rheological properties of sludge is achieved with excellent matching between simulation and experimental results. Being able to take into account these impact factors, the rheological model can be used to predict the sludge behavior in various operating conditions.
Agid:
6176330