Model-based prediction of fluid bed state in full-scale drinking water pellet softening reactors

Softening at drinking water treatment plants is often realised by fluidised bed pellet reactors. Generally, sand is used as seeding material and pellets are produced as a by-product. To improve to sustainability, research has been carried out to replace the seeding material by re-using grained and sieved calcite pellets as seeding material. An explicit fluidisation model is developed to predict the fluid bed state in fluid bed pellet softening reactors with calcite as seeding material.
The fluidisation theory is extended in a model whereby soft sensors are derived and experimentally tested for a wide range of seeding material and pellets. With the soft sensors porosity, particle size and pressure drop can explicitly be calculated. Pilot research has been carried out to calibrate and full-scale experiments to validate the fluidisation models.
Four different fluidisation models were reviewed from which the original Richardson-Zaki fluid bed model has been selected as the best explicit fluidisation model to predict the porosity, particle size and pressure drop. Applying a discretisation model for the fluid bed pellet reactor, the current operation of the treatment softening can be improved by estimating the fluidisation, pressure drop behaviour and particle profile.
Waternet can apply the Richardson-Zaki fluid bed model in practice for building a soft sensor to achieve optimal bed fluid conditions for the softening process.