Abstract
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 language | English |
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Title of host publication | Proceedings of the 10th International Conference on Multiphase Flow (ICMF 2019) |
Subtitle of host publication | Rio de Janeiro, Brazil, May 19 – 24, 2019 |
Number of pages | 2 |
Publication status | Published - 2019 |
Event | 10th International Conference on Multiphase Flow - Rio de Janeiro, Brazil Duration: 19 May 2019 → 24 May 2019 http://www.icmf2019.com.br/ |
Conference
Conference | 10th International Conference on Multiphase Flow |
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Abbreviated title | ICMF 2019 |
Country/Territory | Brazil |
City | Rio de Janeiro |
Period | 19/05/19 → 24/05/19 |
Internet address |
Keywords
- liquid-solid fluidization
- drinking water
- porosity
- hydraulic models
- symbolic regression
- genetic programming