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 viscosity but also of particle size and shape. On the basis of this porosity, it becomes possible to calculate the specific surface area for reactions for seeded crystallization in a fluidized bed.
Original languageEnglish
Title of host publicationProceedings of the 10th International Conference on Multiphase Flow (ICMF 2019)
Subtitle of host publicationRio de Janeiro, Brazil, May 19 – 24, 2019
Number of pages2
Publication statusPublished - 2019
Event10th International Conference on Multiphase Flow - Rio de Janeiro, Brazil
Duration: 19 May 201924 May 2019


Conference10th International Conference on Multiphase Flow
Abbreviated titleICMF 2019
CityRio de Janeiro
Internet address

    Research areas

  • liquid-solid fluidization, drinking water, porosity, hydraulic models, symbolic regression, genetic programming

ID: 54414929