A deep-learning model for predicting spatiotemporal evolution in reactive fluidized bed reactor

Detailed information of flow fields is of great significance for designing and optimizing multiphase flow systems. However, predicting spatiotemporal evolution of gas-solid flows using numerical simulation often requires a significant amount of computation and time. In this study, we proposed a 3D convolutional neural network for predicting reactive dense gas-solid flows. We first explored the design of model architecture and extensively evaluated the performance in terms of efficiency, accuracy, long-term prediction stability and generalizability for a non-reactive fluidized bed. Then we extended the method to a biomass fast pyrolysis process. The proposed model achieves real-time prediction, 3–4 orders of magnitude faster than CFD-DEM simulations. The surrogate model reasonably captures bubble-driven flow behaviors and effects of bubble on fast pyrolysis reactions. The predicted bubble characteristics, and time-averaged and RMS flow fields match well with the simulation results. Our approach exhibits excellent long-term stability and has good generalization capability to unseen fluidization velocities. To the best of our knowledge, this is the first time a neural network has been successfully applied to learn spatiotemporal evolution of reactive dense gas-solid flows.