Learning time-aware multi-phase flow fields in coal-supercritical water fluidized bed reactor with deep learning

The supercritical water fluidized bed (SCWFB) reactor has been utilized to achieve clean and efficient conversion of coal as well as the generation of hydrogen. The complicated time-aware multi-phase flow fields inside the reactor however are usually obtained via time-consuming experiments or transient numerical simulation. To this end, we propose a data-driven deep spatio-temporal sequence model, named ReactorNet, for intelligent modeling and efficient prediction of the complex time-aware multi-phase flow fields inside the SCWFB reactor. Particularly, the model employs the U-Net architecture wherein the coupled convolution module and BiConvLSTM module in a single block are stacked to simultaneously extract the multi-scale spatial and temporal features of multi-phase flow fields in the reactor. The numerical results showcase that the ReactorNet could successfully and simultaneously learn the evolution of multi-phase flow fields in the reactor under different temperature conditions, thus performing accurate multi-step-ahead prediction. The flow fields predicted by ReactorNet are not only highly consistent with the simulated results, but also hundreds of times faster than the traditional numerical simulation. In addition, the ReactorNet can carry out both reasonable spatial and temporal extrapolation and even long-term rollout prediction according to the learned evolution principle of flow fields, thus showcasing remarkable generalization ability. It is the first attempt to apply deep learning to the modeling and predicting of time-aware multi-phase flow fields inside the challenging SCWFB reactor.