Hydraulic modelling of liquid-solid fluidisation in drinking water treatment processes

Abstract

In drinking water treatment plants, multiphase flows are a frequent phenomenon. Examples of such flows are pellet-softening and filter backwashing where liquid-solid fluidisation is applied. A better grasp of these fluidisation processes is needed to be able to determine optimal hydraulic states. In this research, models were developed, and experiments performed to gain such hydraulic knowledge. As a result, treatment processes can be made more flexible. In a rapidly changing environment, drinking water production must be flexible to ensure robustness and to tackle challenges related to sustainability and long-term changes. In the hydraulic models, the voidage in the fluidised bed and the particle size of the suspended granules are crucial variables. Voidage prediction is challenging as the fluidised bed is a dynamic environment showing highly heterogeneous behaviour that is hard to describe with an effective model. And particle size causes a conundrum due to the irregular shapes of the applied granules. Through the combination of hydraulic dimensionless Reynolds and Froude numbers, an accurate voidage prediction model has now been developed. With a straightforward pseudo-3D image analysis for non-spherical particles measuring particle mass and density, the dimensioned shapes of, for instance, ellipsoids can be determined. Particle shape factors included in models are not constant as is commonly believed, but dynamic. Applying advanced computational fluid dynamics simulations confirmed significant heterogeneous particle-fluid patterns in fluidised beds. Comprehensive sedimentation experiments showed that the average drag coefficient and terminal setting velocity of individual grains can be estimated reasonably well, but with a significant degree of data spread around the mean values. For engineering purposes, this is relevant information which should be taken into consideration. A new soft-sensor was designed to determine the voidage gradient and particle size profile in a fluidised bed. The expansion degree of highly erratic, polydisperse and porous granular activated carbon grains can be predicted with a model, but in full-scale processes the grains are subject to change, and therefore it is most likely that the prediction accuracy will deteriorate rapidly. For reliable drinking water quality, smart models provide solutions to complex challenges, but they are only effective when they are calibrated and validated in advanced pilot plants and are applied in full-scale processes with diligence and commitment on the part of multidisciplinary teams.