Neural network prediction of residence time distribution for quasi-2D pebble flow

An important parameter in silo discharging is residence time distribution (RTD), which describes the time each pebble stays within the bed. RTD can be applied as a key indicator to estimate reaction processes, and a key analytical metric to assess the inherent safety of reactors. However, due to the difficulty in obtaining pebble trajectories, few studies on discharging processes have been carried out. In this research, a pilot experimental study is conducted to get quantitative data and prediction model on RTD with precise predictions. The feasibility of applying neural network methods to RTD prediction is also checked. The accuracy of neural network prediction is confirmed by comparing the error between predicted and experimental results. It is discovered that RTD isoline changes in different regions and characterized by intuitive physical models. Compared to DEM, the neural network has a significant advantage in computing speed while retaining acceptable prediction accuracy.(c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This research focuses on the residence time distribution (RTD) in silo discharging and examines the feasibility of using neural network methods for RTD prediction. The results reveal that neural networks offer a significant advantage in computing speed while maintaining an acceptable level of prediction accuracy.