Using an encoder-decoder convolutional neural network to predict the solid holdup patterns in a pseudo-2d fluidized bed.

• Application of deep learning to predict the flow behavior of fluidized beds. • Introduction of a novel combination of encoder-decoder convolutional neural network and CFD. • The ability of combined deep learning- CFD approaches to predict the complex behavior of fluidized beds. In this paper, the capability of combined use of computational fluid dynamics (CFD) and data-based deep learning to predict fluidized beds' complex behavior without solving transport equations is being examined. A convolutional neural network (CNN) is trained to anticipate fluidized bed volume fraction contours based on the numerical simulations' results and data-based machine learning. The trained CNN receives the first ten frames from the CFD as input and predicts the next frame. This process continues until all the required frames are obtained. The results show CNN's superior spatial learning capability and how its combination with CFD can reduce the required computational power without compromising accuracy. [ABSTRACT FROM AUTHOR]