CFD-based deep neural networks (DNN) model for predicting the hydrodynamics of fluidized beds

Fluidized beds are central to numerous applications such as drying, combustion, gasification, pyrolysis, CO₂ utilization, mixing, and separation. The design and development of fluidized beds are still evolving owing to the complex hydrodynamics. Various experimental investigations and CFD simulations have been carried out to understand its hydrodynamics. Whereas the experimental approaches are very costly and limited to small scale, CFD modeling on the other hand requires significant computational resources and time. Thus, in this contribution, we propose a hybrid CFD-based ML model for estimating the hydrodynamics of fluidized beds. The CFD simulations of Taghipour et al., 2005 were performed and validated with the experimental measurements for a wide range of inlet gas velocities encompassing multiple flow regimes. A time-averaged simulation data of the CFD model was used for developing a Deep Neural Network (DNN) model. The hydrodynamic parameters, such as solid velocity field, volume fraction, and bed pressure drop, are predicted using the CFD-based DNN model. The results demonstrate that DNN has superior spatial learning capabilities and that, when used with CFD, it can reduce the computational power required without sacrificing accuracy. To evaluate the versatility of the CFD-based DNN model with different operating conditions and hydrodynamic parameters, independent data from (Cloete et al., 2013) and (Li and Zhang, 2013) were used for satisifactory validation.