Accurate voidage prediction in fluidisation systems for full-scale drinking water pellet softening reactors using data driven models

In full-scale drinking water production plants in the Netherlands, central softening is widely used for reasons related to public health, client comfort, and economic and environmental benefits. Almost 500 million cubic meters of water is softened annually through seeded crystallisation in fluidised bed reactors. The societal call for a circular...

Accurate prediction of liquid-solid fluidized bed porosity in drinking water treatment processes using empirical data-driven genetic programming models

For an accurate prediction of the porosity of a liquid-solid homogenous fluidized bed, various empirical prediction models have been developed. Symbolic regression machine learning techniques are suitable for analyzing experimental fluidization data to produce empirical expressions for porosity as a function not only of fluid velocity and...