Abstract
Gas-solid fluidized beds are widely used in petroleum, chemical, mineral, pharmaceutical, and power plant applications. The performance of fluidized bed reactors strongly depends on the flow dynamics. Characterization of a particle-laden flow has been one of the challenging issues in fluidization research. The simulation of flow in such processes is challenging as the complex dynamic systems comprised of numerous particles and fluidizing gas confined in specific devices. Nonlinear particle-particle/wall and particle-gas interactions lead to complex flow behavior of the gas-solid flows. We used MFiX to simulate a gas-solid flow in fluidized beds. A data-driven framework is trained with the data from MFiX-PIC simulations. The trained and tested machine learning model is used to characterize the flow regimes in fluidized beds. In the present study, the void fraction is used to characterize the flow regimes. However, others in the literature have used pressure, temperature, heat transfer coefficient, acoustic emission, vibration, and electrostatic charge for the characterization of flow regimes.
